International Journal of Emerging Technologies in Computational and Applied Sciences issue 11 vol 3 by iasir journals

ISSN (ONLINE): 2279-0055 ISSN (PRINT): 2279-0047

Issue 11, Volume 1, 2 & 3 December-2014 to February-2015

International Journal of Emerging Technologies in Computational and Applied Sciences

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: Germany, Australia, India, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijetcas@gmail.com

PREFACE We are delighted to welcome you to the eleventh issue of the International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJETCAS is publishing high-quality, peer-reviewed papers covering topics such as computer science, artificial intelligence, pattern recognition, knowledge engineering, process control theory and applications, distributed systems, computer networks and software engineering, electrical engineering, electric machines modeling and design, control of electric drive systems, non-conventional energy conversion, sensors, electronics, communications, data transmission, energy converters, transducers modeling and design, electro-physics, nanotechnology, and quantum mechanics.

The editorial board of IJETCAS is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including computer science, cognitive science, learning sciences, artificial intelligence, electronics, soft computing, genetic

algorithms,

technology

management,

manufacturing

technology,

electrical

technology, applied mathematics, automatic control , nuclear engineering, computational physics, computational chemistry and other related disciplines of computational and applied sciences. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to computational and applied sciences. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic Search, INSPEC, ProQuest, ArnetMiner, Base, ChemXSeer, citebase, OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library,

arXiv,

ERIC,

EasyBib,

Infotopia,

WorldCat,

.docstoc

JURN,

Mendeley,

ResearchGate, cogprints, OCLC, iSEEK, Scribd, LOCKSS, CASSI, E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJETCAS for entrusting us with the important job. We are thankful to the members of the IJETCAS editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this eleventh issue, we received 157 research papers and out of which only 48 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of computational and applied sciences.

This issue of the IJETCAS has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in computational and applied sciences and may open new area for research and development. We hope you will enjoy this eleventh issue of the International Journal of Emerging Technologies in Computational and Applied Sciences and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), ISSN(Online): 2279-0055, ISSN(Print): 2279-0047 (December-2014 to February-2015, Issue 11, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune- 411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg, R.K.University,Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana, India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar, Punjab(India) Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India. Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India. Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India. Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India. Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India. Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur-313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India. Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women,s College, Gardanibagh, Patna, Bihar, India. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore, India. Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India. Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India. Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India. Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India. Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt. Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011, India. Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India. Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India. Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India. Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India. Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India. Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India. Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India. Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India. Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt. Arash Shaghaghi, University College London, University of London, Great Britain. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia. Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India. Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001. Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India. Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India. Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009, India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University, Coimbatore-641003,Tamil Nadu, India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066 Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057 Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India.

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Prof. (Dr.)B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India. Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India . Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Engg., Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty, Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT ,Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India.

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Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale, Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman, Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi-835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS-38655, USA Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal-India Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu-India Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India.

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Prof. (Dr.) Meghshyam K. Patil, Assistant Professor & Head, Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics , Bhilai Institute Of Technology, Durg (C.G.) 491001 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India. Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036. Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India. Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria. NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur. Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India. Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India. Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India. Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India. Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh. Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India. Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India. Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India. Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India. Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212. Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India. Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic. Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan. Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014. Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal. Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India. Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria. Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran. Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India. Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India. Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India. Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria. Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’S SCHOOL, ATHANI, India Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering , Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab,India

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Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology, Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura-India Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai, 400103, India, Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, India Prof. (Dr.) Har Mohan Rai, Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036, India. Prof. (Dr.) Aparna Sarkar, PH.D. Physiology, AIPT, Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP, India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher, Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. .

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Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, India. Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University, Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN. Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV),Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India. Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar, PhD(CS), M.Phil(CS), MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India. Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana), India. Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College, Govind Nagar,Kanpur208006, India. Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura, India. Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India. Prof. (Dr.) T Venkat Narayana Rao, C.S.E, Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India. Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India. Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India. Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India. Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India. Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts. Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India. Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India. Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich â€“ 271801 UP, India. Satya Rishi Takyar, Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh (PB) Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

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Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Social networks and intelligence  Social science simulation  Information retrieval systems  Technology management  Digital libraries for e-learning  Web-based learning, wikis and blogs  Operational research  Ontologies and meta-data standards  Engineering problems and emerging application  Agent based modeling and systems  Ubiquitous computing  Wired and wireless data communication networks  Mobile Ad Hoc, sensor and mesh networks  Natural language processing and expert systems  Monte Carlo methods and applications  Fuzzy logic and soft computing  Data mining and warehousing  Software and web engineering  Distributed AI systems and architectures  Neural networks and applications  Search and meta-heuristics  Bioinformatics and scientific computing  Genetic network modeling and inference  Knowledge and information management techniques  Aspect-oriented programming  Formal and visual specification languages  Informatics and statistics research  Quantum computing  Automata and formal languages  Computer graphics and image processing  Web 3D and applications  Grid computing and cloud computing  Algorithms design  Genetic algorithms  Compilers and interpreters  Computer architecture & VLSI  Advanced database systems  Digital signal and image processing  Distributed and parallel processing  Information retrieval systems  Technology management  Automation and mobile robots  Manufacturing technology  Electrical technology  Applied mathematics  Automatic control  Nuclear engineering  Computational physics  Computational chemistry

TABLE OF CONTENTS International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) ISSN (Print): 2279-0047, ISSN (Online): 2279-0055 (December-2014 to February-2015, Issue 11, Volume 1, 2 & 3) Issue 11, Volume 1 Paper Code

Paper Title

Page No.

IJETCAS 15-109

Mathematical Methods for Separation of Overlapping Asymmetrical Peaks in Spectroscopy and Chromatography. Case study: One-Dimensional Signals J. Dubrovkin

01-08

IJETCAS 15-111

Content Based Image Retrieval System with Feature Extraction and Recently Retrieved Image Library Seema H. Jadhav, Dr. Sunita Singh, Dr. Hari Singh

09-16

IJETCAS 15-117

A Dual Security Approach for Medical Images using Encryption and Watermarking Optimized by Differential Evolution Algorithm Mr. CH.Venu Gopal Reddy, Dr. Siddaiah.P

17-29

IJETCAS 15-119

Energy Efficient Routing Protocol for MANET: A Survey GANESH GUPTA, MADNESH KUMAR GUPTA, ASHOK K. RAGHAV

30-37

IJETCAS 15-121

Solving Generalized Assignment Problem with Genetic Algorithm and Lower Bound Theory Mr. VikasThada, Mr. Utpal Shrivastava, Ms. Meenu Vijarania

38-42

IJETCAS 15-123

Priority Based Scheduling in a Federated Cloud Environment A. Stanislas, L. Arockiam

43-48

IJETCAS 15-125

Swarm Intelligence Techniques for Optimization in Data Clustering Ms. Dipali Kharche, Prof. A.D.Thakare

49-53

IJETCAS 15-129

Blind digital watermarking using AES technique for colour images Rahul Saxena, Nirupma Tiwari, Manoj Kumar Ramaiya

54-57

IJETCAS 15-130

Mapping and Partitioning of Task Graphs Using Kernighan-Lin/Fiduccia-Mattheyses Algorithm Ashish Mishra, Raja Jimit, Abhijit Rameshwar Asati, Kota Solomon Raju

58-61

IJETCAS 15-132

Ultrasonic and Pyroelectric Sensory Fusion System for Indoor Human/ Robot Localization and Monitoring Azhar K H

62-66

IJETCAS 15-134

Rough Set Techniques for Text Classification and Sentiment Analysis in Social Media G. K. Panda, Jayanta Mondal

67-74

IJETCAS 15-135

OFDM Channel Analysis between FFT and Wavelet Transform Techniques Quosay Jalil, S Nagakishore Bhavanam

75-79

IJETCAS 15-138

Bianchi Type IX Cosmological Model with Varying LambdaTerm R.K. Tiwari, D.K. Tiwari, C.Chauhan

80-83

IJETCAS 15-140

Thermo Physical Properties of Nano Ferro fluids L.S.V Prasad, Paul Douglas Sanasi, V.Srinivas

84-88

IJETCAS 15-143

A Novel Efficient and Accurate Detection Model to Detect Emerging Attacks in Network Supriya Gupta, Ankur Goyel

89-93

IJETCAS 15-149

Analysis of 5-Iodo-Uracil for its Infrared Spectra, Laser Raman Spectra and its Thermodynamic Functions Dr. Pradeep Kumar Sharma

94-101

Issue 11, Volume 2 Paper Code

Paper Title

Page No.

IJETCAS 15-152

Evaluation of the Peak Location Uncertainty in Spectra. Case Study: Exponentially Modified Asymmetrical Gaussian Doublets J. Dubrovkin

102-109

IJETCAS 15-155

LEBESGUE CONVERGENCE OF MODIFIED COMPLEX TRIGONOMETRIC SUMS JATINDERDEEP KAUR, SANDEEP KAUR AND S.S. BHATIA

110-114

IJETCAS 15-158

MULTIPURPOSE SMART CARD USING ADVANCED ENCRYPTION STANDARD ALGORITHM Nusrath A

115-118

IJETCAS 15-164

A Study on the Academic Performance of the Students by Applying Multiple Linear Regression Analysis using the method of Least Squares G. Narasinga Rao

119-121

IJETCAS 15-166

Analysis of Incompressible fluid flow over wedge with different angles Dr. Deepak Sharma, Mr. Swapnil Jain, Ankush Kumar

122-126

IJETCAS 15-174

Pairwise Key Distribution Mechanism for Heterogeneous Sensor Network Kanwalinderjit Gagneja

127-133

IJETCAS 15-175

Influence and Role of Technology on Stress: A Mathematical Analysis Dr. M.S.Saleem Basha, Dr. Esam Al Lawati, Mrs. Gargi Bhattacharya and Dr. Nasir Ahmed Khan

134-139

IJETCAS 15-176

Design of Unified Power Quality Conditioner (UPQC) To Improve the Power Quality Problems by Using Instantaneous Real & Reactive Power Theory S.Natarajan, Dr.M.AntoBennet, M. Manimaraboopathy,S.Sankararnarayan,N.Srinivasan

140-147

IJETCAS 15-177

Enhanced More Secure Trust Based Routing Scheme for DSR Protocol in MANETs Ankita Sahu, Vikas Sejwar

148-151

IJETCAS 15-178

Recognising Natural Emotions Using Artificial Neural Network In A Classroom Setting Rhea Mahajan, Remia Langer

152-156

IJETCAS 15-180

An Approach to Solve Single Machine Job Scheduling Problem using Heuristic Algorithm Satyasundara Mahapatra, Dr. Rati Ranjan Dash, Dr. Sateesh Kumar Pradhan

157-163

IJETCAS 15-181

Performance Comparison of Unicast and Multicast Routing Protocol in MANET using NS2 Mandeep Singh, Prakash Rao Ragiri

164-168

IJETCAS 15-183

A Study on Mining Approach under Cyber Crime Analysis Priyanka Maan, Meghna Sharma

169-173

IJETCAS 15-190

Design of Universal Gates Based on Reversible Logic Deepali Samtani, Naman Kumar Patel, Aditya Gupta, Shruti Jain

174-178

IJETCAS 15-198

Turbulent Heat Transfer and Pressure Drop in an Internally Ribbed Rectangular Duct Sohil Akahter, Ishwar Singh

179-182

IJETCAS 15-199

Brain Image Segmentation Using Chan-Vese algorithm using Active Contours and Level Set Functions Payal, Satinderjeet Singh

183-187

IJETCAS 15-203

Thermoluminescence of nanocrystalline Eu doped BAM Phosphor Vinit Kumar, M. K. Dhasmana, R.B.S. Rawat

188-189

Issue 11, Volume 3 Paper Code

Paper Title

Page No.

IJETCAS 15-204

Calculation of the Probability for CR-39 Alpha Particles Detectors Ali Farhan Nadir, Noori H.N. Al-Hashimi, Abdul Ridha.H.Subber

190-193

IJETCAS 15-208

Mathematical Analysis of Asymmetrical Spectral Lines.Case study: Exponentially Modified Functions J. Dubrovkin

194-201

IJETCAS 15-213

Bezier Surface Reconstruction using Artificial Neural Networks Kavita, Navin Rajpal

202-206

IJETCAS 15-219

A Dual Mode DNA Cryptography: Bio Inspired Approach for Information Security Fakhrayh Al Harrasi, Dr. M.S.Saleem Basha, Dr. A. Mohamed Abbas and Mohamed Jameel Hashmi

207-212

IJETCAS 15-223

Optimization of land and water resource in Nakatiya Minor Canal command area in Udham Singh Nagar, Uttarakhand, India

213-216

G.S.Yurembam and Vinod Kumar IJETCAS 15-229

Interactive Animations to Present Academic Subjects to Elementary School Children Cristian Javier Cauich Valle, Lizzie Narváez Díaz, Cinhtia M. González Segura

217-222

IJETCAS 15-230

Citizen Charter Validation A K Tripathy, M R Patra and S Pradhan

223-226

IJETCAS 15-231

Type 2 Diabetes, Therapeutic Targets and Potential Drugs: A Review Neha Verma and Usha Chouhan

227-233

IJETCAS 15-236

Database Management - Inline Queries vs. Stored Procedures–An Extended Analysis Er. Shobhit Gupta

234-238

IJETCAS 15-242

An Analytical Study of Weakly Nonlinear Convection in a Horizontal Mushy Layer Prof.Dr.P.K.Srimani, Mr. R.Parthasarathi

239-246

IJETCAS 15-243

Strategic Planning towards Effective Management of New Technology in Open Cast Mines Anand Pd. Sinha, Supriyo Roy

247-254

IJETCAS 15-244

ANALYSIS OF BANK’S UNSTRUCTURED DATA USING MAP REDUCE TECHNIQUE Jeny George, A. Karthika, Dr. Ulagamuthalvi V

255-257

IJETCAS 15-245

SOCIAL AWARENESS THROUGH NEW MEDIA Niket Mehta, Dr. Suparna Dutta, Dr. Asit Bandyopadhyay

258-261

IJETCAS 15-246

IDENTIFYING GENETIC MUTATION RARE GENETIC DISORDER BY ANALYZING CHARACTERISTICS OF GENOTYPE-PHENOTYPE BY IMPLEMENTING APRIORI ALGORITHM Bipin nair B J ,Ratheesh A, Koushik K S

262-265

IJETCAS 15-247

Mining Frequent and Similar Patterns with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) Data Mining Technique Spits Warnars

266-276

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Calculation of the Probability for CR-39 Alpha Particles Detectors Ali Farhan Nadir, Noori H.N. Al-Hashimi, Abdul Ridha.H.Subber Department of Physics, College of Education for Pure Science, University of Basra, Basra, IRAQ

____________________________________________________________ Abstract: Numerical assessments of radon exhalation from soil and air samples together with the detection probability of alpha particles by the two most commonly detectors known as CR39 was the first objective of this work .The SRIM2013 technique has been applied to calculate the energy dependent of alpha particles registration probability. The detection probability for CR39 is different for each radioactive chain, exposition of each detector. It is found that the calculated values of alpha particle ranges and detection probabilities are not the same in two versions, SRIM1992 and SRIM2013, of software. Keywords: energy, alpha range, detection probability, SRIM, CR39 __________________________________________________________________________________________ I. Introduction Detector sensitivity for radon measurements with CR-39 detector was given by many authors see for example [1-5]. Radon diffusion, transport through different media, and detected is a complex process and is effected by several factors. However, usually some of information which is used as input parameters in presented program was missing. For example, removed layer is very rarely given. Somewhere, etching conditions or chamber dimensions was not stated. For these reasons it was difficult to perform comparison with experimental results.. Radon concentration normally determined using diffusion chamber and techniques based on using CR-39 detector. This technique necessitates a calibration with standard sources of radon concentration. To evaluate the concentration in terms radon and thoron with their progenies present in air. The probability for alpha particles emitted by radon, thoron and their daughters and registered on the detector are calculated [6]. Detection efficiency is another important property of radiation detectors. As the general meaning implies, detection efficiency represents the probability of detection for a single radiation quantum. An accurate and precise calibration for detection efficiency is very important for quantitative measurement of an unknown radiation source [7]. The detection efficiency is obtained directly from the geometry factor, i.e. the ratio of the solid angle per 2π or 4π. The detection efficiency is depended on both radiation interaction with mater and size of a detector. Charged particles (electron, proton, alpha) interact more easily than neutral ones (X-ray, gamma-ray, neutron) and give high efficiencies. Theoretically, if we could create detectors with large enough volumes, we could always detect 100 % of the particles reach the detector surface. However, this is either impractical or even impossible, e.g. semiconductor crystals just cannot be grown large enough to be 100 % efficient for high energy photons. Neutrino detectors are already built in mineshafts. This is why the concept of detection efficiency was created. Not all particles can be detected, but if the proportion of detected particles is known, the number of particles can be calculated from the number detected ones [7]. The main objective of the present work is to apply the solid angle definition to the calculation of detection probability of CR39 detector. II. Theory The CR-39 plastic track detector is a C12H18O7 polymer with density 1.3 g/cm 3. This detector has characteristic response to every type of particles from any nuclear reactions. Charged particles are registered directly, and neutrons are detected through the secondary recoil particles or nuclear reactions. Particle tracks on the detector became visible after etching and are investigated using a microscope. α−events in CR-39 detector do not exceed one per few cm2 and the investigation of yields for reaction from thick samples is difficult, because charged particles come to the detector with a large energy spread and their tracks have different diameters. This low background allows experiments to carry out using very long exposures. The main parameter of track detector is the ratio of etching rates at the start of the track and at the end of the track (V T/VB). This ratio is a function of energy loss (stopping power, dE/dx). Track diameter is related to this ratio by a parametric equation [8]. The dependence of track diameter on dE/dx makes possible identification of a particle. The critical angle of registration (Θc = arcsin (VB/VT)) is also an important characteristic. Θc is the minimum angle of particle incidence on the detector in which track formation is possible. It is easy to show that the detection efficiency for a given type of particles is determined by the relation [9]. The solid angle can be defined as [10]: (1) Then, the total solid angle is:

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Ali Farhan Nadir et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 11(3), December 2014February 2015, pp. 190-193

(2) Where is the critical angle, which is defined as the angle between the vertical on the detector surface and the vector represent the range of the particle inside the detector [10]. The probability P of an alpha particle emitted from radioactive sources at the center of a sphere and hits the detector of area dA mounted on the surface of the sphere is defined as the ratio between solid angle to the total solid angle 2π and written as [11]. (3) The probability depends only on the critical angle which is depends on the energy of alpha particle emitted from the source and traveling a distance (x) in the air to reach the detector. This means that the critical angle is a function of the distance x, in other word we can rewrite the probability as [12]; (4) The total distance between the detector and the source is Ro= x= n∆x; divided by ∆x= 1 µm and then we can calculate the total probability by [13]; (5) Divide and multiply the RHS by ∆x (6) (7) (8) The CR39 detector is sensitive to all energy range of alpha particles, [12]. (9) The previous integral in equation (9) written in the form; (10) To calculate the integral in equation (10); one needs to find an analytical expression for the relation of critical angle with rang in alpha particle in air [7]. This procedure similar to procedure in Ref.[10]. The value x taken to be vary from 0 to Ro graduated by step of ∆x =1µm. Using curve fitting to the function by OriginPro8 software then, create the seven degree polynomial, which is shown in Figure (1) and written as; (11) (12) (13) III. Result and Discussion Table (1) shows the best fit parameter of an in equation (11). From the function in equation (13) one can calculate the detection probability for CR39 detector in the energy range 5.49 MeV to 8.78 MeV (radon and thoron alpha energies) as shown in table (2). Table (2) also shows at the side of the calculated detection probabilities, the range inside the CR39 detector. While Table (3) contains the same parameters used by reference [12] which is calculated using SRIM1992. There are differences between the two set probabilities, the probabilities using SRIM2013 are less than those of SRIM1992, and this due to adding CO 2 to the air composition. These probabilities are essential for the calculation of radon gas concentration. Also, there are differences in the ranges for the same alpha energy. This means that, one should use SRIM2013 to get precise results for radon and thoron concentrations. The relation between detection probability of CR39 detector and alpha particle energy is shown in Figure (2). After the calculation of detection probability using SRIM software, we applied these values in the fallowing equation:

(14) The calculation steps for the constants in the above equation are presented in table (4). As a conclusion in this paper we reported a newly developed application of SRIM2013 in the calculation of alpha particle range in CR39 detector and the detection probabilities for different values of alpha energies. We found that, the range of alpha particles and detection probabilities calculations using SRIM2013 are inconsistence with those of

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SRIM1992. These differences are due to the change in the air and detectors composition parameter in the program. . a0

0.7866

-0.01984

4.38713*10-4

-5.89552*10-6

-1.94925*10-8

1.26436*10-9

-8.67955*10-12

Table (1) an the best fit parameters

Nuclide

Energy (Mev)

Range (cm)

Rn-222

5.49

3.05

1.2698

Po-218

6.00

3.50

1.5320

Po-214

7.68

5.15

2.6215

Rn-220

6.28

3.76

1.6891

Po-216

6.78

4.24

1.9937

Bi-212

6.08

3.57

1.5759

Po212

8.78

6.36

3.5328

Table 2 the detection probability for CR39 detector

Figure (1)

VS range of -particles in air for CR-39 detector.

Nuclide

Rn-222

5.49

3.05

12.69 8

38.73

Po-218

3.5

15.32

53.62

Po-214

7.68

5.15

26.21 5

135.01

227.36 ∑ Table (4a) calculated parameters of equation 14

Nuclide

Energy (Mev)

Range (cm)

Rn-222

5.49

3.9

2.871

Po-218

6.00

4.65

3.383

Po-214

7.68

6.62

4.440

Rn-220

6.28

4.80

3.391

Po-216

6.78

4.75

3.433

Bi-212

6.08

5.45

3.527

Po212

8.78

8.36

5.711

Table (3) Detection probability for CR39 calculated by SRIM2013detector used in reference [5]

Figure (2) Detection probability VS alpha energy for CR39

Nuclide

Rn-220

6.28

3.76

16.891

63.51

Po-216

6.78

4.24

19.937

84.53

Bi-212

6.08

3.57

0.36

15.759

20.25

Po-212

8.78

6.36

0.64

35.328

143.80

312.10 ∑ Table (4b) calculated parameters of equation 14

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REFRENCES [1] [2] [3] [4] [5] [6]

[7] [8] [9] [10] [11] [12]

[13]

[14]

[15]

D. Nikezic, K. N. Yu,2 and J. M. Stajic1; " Computer program for the sensitivity calculation of a CR-39 detector in a diffusion chamber for radon measurements"; REVIEW OF SCIENTIFIC INSTRUMENTS 85, 022102 (2014) D. Nikezic and K. N. Yu; "Computer program TRACK_TEST for calculating parameters and plotting profiles for etch pits in nuclear track materials"; Comput. Phys. Commun. 174, 160 (2006) D. Nikezic and K. N. Yu; "Computer program TRACK_VISION for simulating optical appearance of etched tracks in CR-39 nuclear track detectors"; Comput. Phys. Commun. 178, 591 (2008) A. H. Ismail and M. S. Jaafar; "Interaction of low-intensity nuclear radiation dose with the human blood: Using the new technique of CR39NTDs for an in vitro study"; Nucl. Instrum. Meth. Phys. Res. B69, 437 (2011). D. S. Pressyanov; "Radon progeny distribution in cylindrical diffusion chambers"; Nucl. Instrum. Meth. A 596, 446–450 (2008). M. A. Misdaq , F. Aitnouh, H. Khajmi, H. Ezzahery, S. Berrazzouk,"A new method for evaluating radon and thoron activities per unit volume inside and outside various natural material samples by calculating SSNTD detection efficiencies for the emitted particles and measuring the resulting track densities", Applied Radiation and Isotopes 55(2001) 205-213. Soo Hyu Byun, , Radioisotopes and radiation methodlogy, Med. Phys. 4R06/6R03, version 2014-15,ch. 3, Master university, Canada. Samogyi G., Shalay S.A. Nucl. Instr. And Mem., 109, (1973), Ν 2, 211. R. L. Fleisher, P. B. Price, R. W. Walker. Nuclear Tracks in Solids. Principles and Applications. University of California Press, 1975, v. 1. M. A. Misdaq, A. Bakhchi, A. Ktata, A. Merzouki, N. Youbi,"Determination of uranium and thorium contents inside different materials using track detectors and mean critical angles", Applied Radiation and Isotopes 51(1999) 209-215. S. A. Durrani, R. K. Bull, Solid state nuclear tracks, method and application, International series in natural philosophy. M. A. Misdaq. C. Satif (1996) ,"A new metod for studying the influence of pollution and soil nature on the radon emanation from water samples", Jointly published by Elsevier Science S. A., Lausanne and AkademiaiKiado, Budapest Vol.207, No.1(1996)107116. M. A. Misdaq, S. Berrazzouk, A. Elharti, F. Ait Nouh, W.Bourzik (2000),"The hydraulic exchanges between the main water reservoirs of the Moroccan Middle Atlas region measured by solid state nuclear track detectors", Journal of Radioanlytical and Nuclear Chemistry, Vol. 246,No. 2 (2000)395-401. Ali Farhan Nader; Abdul R.H. Subber; Noori.H.N. Al-Hashimi, An Analytical Approach is Developed to Estimate the Values of Range of Alpha Particles Emitted from Radon Gas, IOSR Journal of Engineering (IOSRJEN), ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 09 (September. 2014), ||V1|| PP 51-55. Ali Farhan Nader, Abdul .R.H. Subber, Noori H.N. Al-Hashmi, Numerical and analytical calculations of efficiency and calibration factor for CR-39 detectors in the chamber diffusion by using Monte- Carlo method and the mean critical angle, Scholars Research Library,2014, 5(5):23-30.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Mathematical Analysis of Asymmetrical Spectral Lines. Case study: Exponentially Modified Functions J. Dubrovkin Computer Department, Western Galilee College 2421 Acre, Israel __________________________________________________________________________________________ Abstract: Asymmetrical spectral lines were modeled as a set of exponentially modified asymmetrical functions which were decomposed into a weighted sum of symmetrical and asymmetrical parts. In each model the asymmetrical part was equal to the first-order derivative of the symmetrical part. Invariant expressions of the first three central statistical moments of these lines have been found. Maximum peak positions and widths of the lines and of their symmetrical parts were evaluated numerically. Asymmetrical lines were also studied using their decomposition to the product of symmetrical and asymmetrical parts. Keywords: asymmetrical line modeling; exponentially modified functions; statistical moments. __________________________________________________________________________________________ I. Introduction Spectrometry is one of the most widespread theoretical and experimental physical methods, which has been employed in various fields of science and technology for more than a century [1]. Measured spectra are usually a set of overlapping spectral lines and bands (elementary spectral components- ESCs) which parameters are the main information source of the qualitative and quantitative properties of the samples under study. Precise determination of these parameters often requires spectral curve fitting to some modeling functions especially in the case of strongly overlapped ESCs [2]. If lines and bands are symmetrical then they are usually approximated by Gaussian, Lorentzian and Voigt functions. However, in real life, symmetrical ESCs are disturbed by the interand intra-atomic and molecular interactions [1, 3] and by spectral instruments (e.g., due to the non-zero time constant of the fast-scan instruments [4]). This is why, symmetrical functions have been modified by introducing asymmetry which was controlled by the asymmetry parameters [5, 6]. Symmetry means that the asymmetry parameters are zero. The line shape theory in spectroscopy and chromatography has been discussed in numerous studies; many analytical expressions for line shapes were theoretically obtained (see references in [1-6]). However, these expressions are not often useful for practical applications. Therefore, it is common to described ESCs by mathematical functions which are approximations to theoretical models or experimentally obtained data. This method includes phenomenological approach which is based on the choosing the model parameters for the best model fitting to the measured analytical signal without rigorous physical foundation. It is very important to underline that there is no "universal" modeling function that can described all asymmetry ESCs. However, choosing physically-based line models is the preferred method in this case. In this connection the exponentially modified Gaussian model [7] is interesting since it reflects the impact of the first-order decay in the measuring tool on the obtained signal dependence on time (e.g., on chromatographic peak [7]). Earlier we performed mathematical analysis of the non-integral asymmetry ESCs by decomposing these components into a product of symmetrical and asymmetrical parts [6]. The dependences of the maximum peak positions and the widths of ESCs on the asymmetry parameters have been estimated. In the present study we describe a set of the exponentially modified symmetrical functions which will be used for approximating asymmetrical ESCs. Our goal is to develop flexible mathematical models of asymmetrical spectral components. For simplicity, we use the short term “line” instead of the long term “line and band”. The standard algebraic notations are used throughout the article. All calculations were performed and the plots were built using the MATLAB program. II. Theory Consider symmetrical Gaussian, Lorentzian and Voigt lines which maxima are located at zero on the x axis, and their intensities are equal to one [1]:

where

and are the line shape parameters for (1) and (2), respectively; is the full line width at half maximum, and The full line width of the Voigt line [8]:

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It is follow from Eq.4 that if

then

where Suppose that in the first approximation a measured line is obtained by convolution of the ideal line with an exponential decay kernel function ( ): where is the asymmetry parameter (e. g., it is the instrument time constant). Substituting Eq.7 into Eq.6 and setting we have [9]: where is the complementary error function. If for then , therefore the right wing of decays exponentially. Since derivation of the precise analytical expression for convoluted Lorentzian (2) using elementary functions is very cumbersome [9], the numerical calculation has been performed using Fourier transform (FT) method. It was found that the MATLAB function failed when . Therefore, the frequency domain ( ) convolution was performed instead of Eq.6: where tilde is the sign of Fourier transform,

. Spectra in the spatial domain are obtained by the inverse FT (IFT) of Eq. 9. Since Eqs. 11 and 12 are even functions, a measured line can be represented as the sum of symmetrical and asymmetrical parts: where

According to Eqs. 15 and 16, Eq. 14 is the weighted sum of symmetrical part and its derivative: The common calculation method of the statistical moments of Gaussian line was described by [7]. We used other method based on the decomposition (17) (see Appendix A). It was shown that the first and the third central statistical moments ( ) do not depend on the symmetrical part if the derivatives of the Fourier transform of any line at the origin are continuous: Since the 1st-order derivative of the FT of Lorentzian line has discontinuity in zero point, the moments have been obtained numerically: Eqs. 18 are easily obtained for Losev model [10] by decomposing the model into a product of symmetrical and asymmetrical parts [6]. The symmetrical part is equal to where stands for the hyperbolic secant. Substituting Eq. 10 and Fourier transform of Eq. 20 [11] into Eq. 9, we have

The next step in the calculation of moments (18) is differentiation of Eq. 21 (Eq. A1). Eq. 17 can be slightly modified: where is the weight of the derivative of the symmetrical part. Coefficient asymmetry for a given constant symmetrical part. The odd-order moments (Eq. 18) are:

controls the line

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The family of simple functions is easily obtained in the frequency domain using symmetrical part of Fourier transformed Gaussian line (Eq.11, ):

where

may be a fraction. Equation similar to (24) is constructed for Losev model (Eq. 20):

where The odd-order moments of the modified functions (Eqs. 24 and 25) are represented by Eqs. 23. The second-order central moment of the (Eq. 24) depends only on the symmetrical part (see Appendix A): where and for Gaussian (Eq. 24) and Losev line (Eq. 25), respectively. It is common to use skewness as a measure of the asymmetry of spectral lines: Substituting Eqs. 23 and 26 into Eq. 27, we have It is known that the value of strongly depends on the range of integration while calculating the central moments. This is an essential drawback because, in practice, the range of integration is limited by the finite wings of the observed line profiles. Therefore, instead of the skewness, we introduced [6] the ratios of: the first-order derivative extremes: and of the amplitudes of the left and the right satellites in the second-order derivative: Coefficient is indicative of the line asymmetry at larger distances from the line centre than . Asymmetrical line may be represented as a sum the even and the odd parts in the spatial domain without applying IFT. (see Appendix B). Particularly, for Dobosh model [12]: where

stands for the hyperbolic cosine. In this case the term

equations is replaced by However, generally speaking, Line asymmetry can be also studied by decomposition of asymmetrical parts [6]. However, here we used more

in Eq. 22 and in the following to the product of symmetrical and general mathematical equations:

where

Eq. 34 and the second term of Eq. 22 are similar. For the modified Losev model (Eqs. 20 and 22) Eq. 33 has simple analytical form: where stands for the hyperbolic tangent. The asymptotic properties of Eq. 35 are For the modified Dobosh model (Eqs. B4 (Appendix) and 33) For III. Computer modelling The dependences of the maximum peak position on the asymmetry parameter for the exponentially modified Gaussian, Lorentzian, Voigt, and Losev lines are given in Fig. 1. The quantitative relationships between line asymmetry and parameters and is established in chromatography by asymmetry factor [7]: where is the hold-up time. In spectroscopy can be approximately defined as a -value for which the line intensity is equal to the baseline level. From Fig. 1 it is seen that for (Eq. 18), and If the asymmetry parameter increases then and since . For given

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Maximum peak position of high asymmetrical lines is significantly shifted from zero point (Fig 1) and the lines are broadened (Fig.2). The symmetrical parts become wider than the lines (Fig. 2). For given The same results have been obtained for Gaussian line which FT was modified according to Eq. 24 (Fig. 3). However, similar modification of Losev line (Eq. 25) produces dissimilar dependences (Fig.4). It is very interesting that for [6]. The dependences of the skewness of Gaussian and Lorentzian lines on the asymmetry parameter differ by sign and magnitude (Fig. 5a). According to Eq. 28 increasing of the weight ( ) of the derivative of the line symmetrical part (Eq. 22) significantly increases the skewness. Increasing of parameter of the modified symmetrical parts (Eq. 24) causes inverse effect. Similar results were obtained numerically for the line asymmetry coefficients and (Eqs. 29 and 30). So for a given symmetrical part increasing of the -value increases and (Fig. 5b and c). If values increase, the symmetrical part and its derivative are changed and lines become more asymmetrical. Changing of the -values has strong effect on the line form, width and maximum peak position (Figs. 6-8). Shifts of maximum peak positions and line widths increase with increasing asymmetry. So, it can be concluded that required asymmetry may be obtained by varying of the and parameters. Decomposition of asymmetrical lines to the product of symmetry and asymmetry parts (Eqs. 32 â&#x20AC;&#x201C; 34) is illustrated in Figs.9-11. Wings of the asymmetrical parts of Gaussian and Lorentzian lines are significantly Figure 1. Dependences of the maximum peak position on the asymmetry parameter

(a) Exponentially modified Gaussian, Voigt ( ) and Lorentz lines (from top to bottom plots, respectively). The families of modified Gaussian (Eq. 24) (b) and Losev (Eq. 25) (c) lines. and , respectively, from bottom to top plots. Figure 2. Dependences of the line width and of the symmetrical part width on the asymmetry parameter

Exponentially modified Gaussian (a), Lorentz (b) and Voigt (c) ( ) lines. Bottom and top plots are widths of the lines and of their symmetrical parts, respectively. Figure 3. Dependences of the line width and of the symmetrical part width on the asymmetry parameter for the family of the modified Gaussian lines (Eq. 24)

Lines (a) and their symmetrical parts (b).

from the bottom to the top plots, respectively.

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Figure 4. Dependences of the line width and of the symmetrical part width on the asymmetry parameter for Losev and Dobosh lines.

Losev lines (Eq. 25) (a) and their symmetrical parts (b). from the bottom to the top plots, respectively. Dobosh lines (c) (Eqs. 22 and 31) from the top to the bottom plots, respectively. different (Fig. 9). In contrary, asymmetrical parts of Voigt and Lorentz lines have similar form since the dominant component of the Voigt wings is Lorentzian line. If parameter (Eq. 24) increases, then symmetrical part is broadened, therefore the slope of the plots (Eq. 33) decreases (Fig. 10c). Figure 5. Dependences of the line asymmetry coefficients on the asymmetry parameter

(a) Skewness of Gaussian ( ) and Lorentzian lines (top and bottom plot, respectively). (b, c) Family of modified Gaussian lines (Eqs. 22 and 24), and 1 for bottom and top groups of the plots, respectively. In each group from the bottom to the top plots, respectively. Figure 6. Weighted sum of line symmetrical parts and their derivatives

Gaussian (a) and Lorentzian (b) lines.

from the bottom to the top plots, respectively.

Figure 7. Dependences of the maximum peak position on the asymmetry parameter for weighted sum of line symmetrical parts and their derivatives

Gaussian (a) and Lorentzian (b) lines.

from the bottom to the top plots, respectively.

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Figure 8. Dependences of the line width on the asymmetry parameter for weighted sum of line symmetrical parts and their derivatives

Gaussian (a) and Lorentzian (b) lines. from the bottom to the top plots, respectively. Figure 9. Decomposition of Gaussian, Lorentzian and Voigt lines to the product of symmetrical and asymmetrical parts

Exponentially modified Gaussian (a), Lorentz (b), and Voigt (c) ( ) lines. Figure 10. Decomposition of the modified Gauss line to the product of symmetrical and asymmetrical parts

(a)

(b)

(c)

=0.2, 0.4,.., 1 from the bottom to the top plots, respectively. Since the asymmetrical parts of Losev and Gaussian lines have similar form, the asymmetrical parts of Gaussians can be roughly approximated by modified Eq. 36 (Fig. 12): where for The asymmetrical parts of Lorentzian (Fig. 9b) and Dobosh lines (Fig. 13) are similar, since the Dobosh model includes Lorentzian component (Eq. B4 (Appendix B)). The plots have extremes which intensities increase with increasing the asymmetry coefficient. Obtained results clearly demonstrate that decomposition of the asymmetrical lines to the sum and to the product of their symmetrical and asymmetrical parts is useful method for studying the properties of asymmetrical line shapes. Figure 11. Decomposition of the modified Losev line to the product of symmetrical and asymmetrical parts

=0.2, 0.4,.., 1 from the bottom to the top plots, respectively.

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Figure 12. Approximation of the asymmetrical part of Gaussian line

-values are given next to the plots. Figure 13. Decomposition of the modified Dobosh line to the product of symmetrical and asymmetrical parts

=0.2, 0.4,.., 1 from the bottom to the top plots, respectively. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12].

. B. K. Sharma, Spectroscopy. 19th Ed. India, Meerut-Delhy: Goel Publishing House, 2007 J. G. Dodd, L. DeNoyer, Curve-Fitting: Modeling Spectra. Handbook of Vibrational Spectroscopy. USA ,NY: Spectrum Square Associates, Inc., Ithaca, 2006. J.-M. Hartmann, C. Boulet and D. Robert. Collisional effects on molecular spectra. Laboratory experiments and models, consequences for applications. Elsiever, 2008. S. G. Rautian, "Real spectral instruments", Uspekhi phisicheskich nauk, vol. 46, 1958, pp. 475-517 [Russian]. B. Di Marco and G. G. Bombi, “Mathematical functions for the representation of chromatographic peaks”, J. Chromatogr.,vol. 931, 2001, pp. 1–30. J. Dubrovkin, "Mathematical analysis of asymmetrical spectral lines", Journal of Emerging Technologies in Computational and Applied Sciences, vol. 1-8, 2014, pp. 27-36. Felinger, Data analysis and signal procesing in chromatography. Elsevier, 1998. J. J. Olivero, R.L. Longbothum, “ Empirical fits to the Voigt line width: A brief review”, J. Quant. Spectroscopy and Radiative Transfer , vol. 17, 1977, pp. 233–236. S. Gradshteyn, I. M. Ryznik, Table of Integrals, Series, and Products. 7th Ed., San Diego, USA: Elsevier, 2007. Losev, “On a model line shape for symmetric spectral peaks”, Appl. Spectrosc., vol. 48, 1994, pp. 1289-1290. D. Kamler, A First Course in Fourier Analysis, Prentice Hall, 2000. S. Dobosh et al, “Detection of ions with the energy larger than 100 keV, which are produced due to the interction of the 60fslaser pulse with clusters” , J. Experim. Theor. Phys., vol. 115, 1999, pp. 2051-2066.

Appendix A. Statistical moments of the exponentially modified lines The th -order moment of [7]: where tilde stands for the Fourier transform sumbol, is the th-order derivative, is the angular frequency, and If can be represented as a sum of symmetrical ( and asymmetrical ( parts, then It is easy to show that Impact of the line intensity and of the line width on Using Eq. 17 we obtain:

values is eliminated if Eq. A2 is devided by

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Since the derivative of the Fourier transform of Lorentzian line has a discontinuity in the zero point, Eq. A4 is not applicable for this line. However, dependence , numerically obtained in the interval is very close to Eq. A4. The central th-order moment of [7]:

Substituting Eq. A2 into Eq. A5 for , we have: For Lorentzian line dependence Substituting Eq. A2 into Eq. A5 for

and taking into account that for Gaussian line

, numerically obtained in the interval and taking into account that

and

is described as we have:

According to Eqs. A1 and A2 the second-order moment is equal to the second-order derivative of the Fourier transform of the line symmetrical part. For modified Gaussian line (Eq. 24): B. Decomposition of asymmetrical line to the sum of the even and the odd parts From the following equations

we have In general case is different from the derivative of the symmetrical part (Eq. 34). Particularly for Dobosh model [12]: exp( ),

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Bezier Surface Reconstruction using Artificial Neural Networks 1

Kavita, 2Navin Rajpal Research Scholar, 2Professor USICT, Guru Gobind Singh Indraprastha University Delhi, INDIA. _______________________________________________________________________________________ Abstract: Surface Reconstruction forms a key to many image processing and computer graphics applications. In the current work we have focused on the reconstruction of Bezier surfaces from a point cloud. The reconstruction is materialized using the learning and generalization capabilities of Radial Basis Function Neural Networks (RBFNN). The experimental results are shown to establish that RBF networks perform better than the traditional surface fitting methods. The reconstructed surface is a smooth surface approximating the given set of data points. The learning capabilities of RBF networks make surface reconstruction robust to noise and irregularities in sampling of the data. 1

Keywords: Surface Reconstruction, Point Cloud, Radial Basis Function Neural Networks, Surface Fitting. ______________________________________________________________________________________ I. Introduction Modelling of curves and surfaces is a difficult and challenging problem to solve. Mathematics and numerical analysis forms the very base of curve and surface reconstruction problems. The advent of computer has given ease to the modelling and simulation of curves and surfaces. This problem is not as trivial as it seems. The reason is that the shapes can be easily identified by human eyes but for a computer it is very difficult to establish proper connectivity between the data points and also maintain the correct topology of the desired shape. In general we can define the modeling of curves and surfaces as the process of estimating the values of a curve or a surface at any location [1]. If the data is without noise then we can apply various interpolation techniques to generate the curve or surface. But practically the data is contaminated with noise due to various reasons, may be when the data is transmitted or when it is acquired through scanners. Soft computing techniques have emerged to be more practical and flexible approaches towards the modeling of curves and surfaces. These methods have become popular due to their capability of handling imprecise and uncertain data. In the current work we have used the learning capabilities of a Radial Basis Function (RBF) neural network which was first introduced by Broomhead and Lowe [2]. A detailed survey of RBF neural networks for curve fitting and interpolation problems is given in [3, 4]. A B-Spline surface approximation using RBF networks is given by Liu [5]. The performance comparison of RBF neural networks to Back-propagation neural networks and generalized regression networks for function approximation is given in [6]. Comparative analysis of Radial basis function and Back-propagation neural networks for reconstruction of noisy Bezier curves is given in [7, 8]. In the presented work we have focused on the reconstruction of 3d-Bezier surfaces from dense point cloud. Bezier surfaces are frequently used in design work as they have the advantage of easily describing the complex and random shapes. Moreover these surfaces are invariant under affine transformations whether they are applied to control points or to the surface. Some other surfaces are also reconstructed and the results are presented. The results show that the reconstructed surface is a smooth surface that approximates well the given set of noisy data. The data in the input cloud is assumed to be non-uniformly distributed. So in general the surfaces are reconstructed from uniform as well as non-uniform data set. II. Theoretical Concepts A. Bezier Surface Bezier surfaces are a class of surfaces which are suitable for ab-initio designs and also to the designing of free form surfaces. Given by Pierre Bezier a 3d-Bezier surface is defined by the control points and by using the basis function. The main properties of a Bezier surface are [9]:  The basic functions are nonnegative.  The sum of the basis functions is 1.  The degree of the surface in a single parametric direction is one less than the number of defining polygon vertices in that direction.  The continuity of the surface in a single parametric direction is two less than the number of defining polygons in that direction.

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 

The surface follows the general shape of the defining control polygon. Only the four corner points of the control polygon and the parameterized surface must be shared, coincident, or interpolated.  The surface lies within the convex hull defined by the control points.  The surface is invariant when affine transforms are applied to either the control points or parameterized surface. A Bezier surface is given as [9]: P(s,t) = Bi,j Jn,i(s) Km,j(t) 0≤t≤1 where Bi,j represents the vertices of the defining polygon and J n,i(s), Km,j(t) are Bernstein basis functions in the parametric directions s and t, and are given by the following equations: Jn,i(s) = Km,j(t) =

with

= = B. Radial Basis Function Neural Networks (RBFNN) RBF networks are a class of feed-forward networks which have only one hidden layer and the neurons in the hidden layer are called radial basis neurons. In general a RBF has three layers- input layer, hidden layer and output layer. The mapping between the input layer and hidden layer is non-linear whereas the mapping between the hidden layer and the output layer is linear. Normally a Gaussian function is used in the hidden layer as a Radial Basis Function. It is given as:

f j  exp( 

|| X  cj || ) 2 2

Where fj depicts the output of the jth radial basis neuron, x represents the input vector, c j is the center of the jth hidden neuron and  is the parameter spread which determines the smoothness and it needs to be optimized for specific problem. The number of hidden layer neurons is not necessarily equal to the number of inputs. If the number of hidden layer neurons is less, than the accuracy of the results is affected and if it is more than that increases the complexity of the process and may affect the performance in terms of global generalizations of the network. Various clustering approaches are used to reduce the number of centers in the hidden layer of neurons and thus selecting the appropriate centers for modelling the data. III. Simulation Results In this section the simulation results are shown for various surfaces. Standard interpolation methods are used to approximate a point cloud and also the results of using RBF networks are presented. Various surfaces are reconstructed and the experimental results are shown in order to describe the approximation capabilities of RBF neural networks. Initially a Bezier surface is generated using sixteen control points. The control points which are considered are given in the form of matrices; first matrix gives the x-coordinates, second matrix gives the ycoordinates and third matrix gives the z-coordinates. The point cloud is generated experimentally by adding noise to the points of the surface. The noise level taken varies from 0.1 to 0.5. The noise is added to z-coordinate of the surface. The parameter in the RBF network is spread which is problem dependent. Spread basically determines the smoothness of the curve or the surface. RBF network is trained with the noisy data as the training set and the original surface is reconstructed using the original data set points which are without noise, noisy data points and the training output of the network. Fig.2 (a-e) shows the results for the Bezier surface. Control points, control polygon and the corresponding Bezier surface are shown in fig.2 (a), fig.2 (b) shows the surface. Noisy data of points is shown in fig.2(c), the noise of 0.1 being added to z-coordinate. Reconstructed surface using simple interpolation and RBFNN are shown in fig.2 (d) and fig.2 (e) respectively. This surface is generated using the following control points (Any set of control points can be taken to generate an initial Bezier Surface): B(0,0) = (-5,1,-6), B(0,1) = (4,1,-3), B(0,2) = (-8.5,-1,1), B(0,3) = (-7,2,5) ; B(1,0) = (-2,3,-6), B(1,1) = (-2.5,2,-3), B(1,2) = (-4.5,0,3), B(1,3) = (-3,4,6.5) ; B(2,0) = (2,3,-6), B(2,1) = (2.5,2,-3), B(2,2) = (4.5,0,3), B(2,3) = (3,4,6.5); B(3,0) = (5,1,-6), B(3,1) = (4,1,-3), B(3,2) = (8.5,-1,1), B(3,3) = (7,2,5). Figure 3(a-d) shows the results for the surface defined by an explicit function: z = 3/(x 2 + y2) and in fig. 4(a-d) results of reconstruction of surface defined by z = x*exp{-(x2+y2)}.The spread parameter is adjusted in each of the surface reconstruction in order to obtain a smooth surface. In first case i.e. for a Bezier Surface spread is taken as 1 whereas for the second and third surfaces spread is taken to be 0.3. The hidden neurons are taken

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equal as the number of inputs. All the simulations are performed using MATLAB R2013a on Windows 7 platform.

8 6 4 2 0 -2 -4 4

-6 -8 -10

2 -5

0 0

-2

Fig.2 (a). Bezier Surface with Control points and Control Polygon (Box view) .

Fig. 2(c). Noisy data points of the Bezier surface

Fig. 2 (b). Bezier Surface

Fig. 2 (d). Reconstructed Bezier Surface from the noisy data using traditional interpolation

Fig. 2(e). Reconstructed Bezier Surface from the noisy data using RBF Neural Networks Results of the surface given by z= 3/(x2 + y2) are shown as below in Fig. 3(a-d)

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Fig. 3(a). Noisy data points of a surface with noise 0.1.

Fig. 3(b). Original Surface.

Fig.3(c). Reconstructed Surface using Cubic Interpolation.

Fig.3(d). Reconstructed Surface using RBF Neural Networks.

Results of the surface given by z= x*exp{-(x2+y2)} are shown as below in Fig. 4(a-d)

Fig.4(a). Noisy data points of a surface with noise 0.1.

Fig. 4(b). Original Surface.

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Fig.4(c). Reconstructed Surface using Cubic Interpolation.

Fig. 4(d). Reconstructed Surface using RBF Neural Networks

IV Conclusions In this paper we have reconstructed various surfaces including a Bezier Surface from the point cloud. The learning capabilities of Radial Basis Function are exploited to learn the patterns in the data and finally reconstruct the original surface. A comparison of traditional surface fitting to the RBF based reconstruction is shown for the noisy data set. The results show that the surface reconstructed using RBF networks is a good approximation to the original surface and also the learning/training speed of the network is fast. The simulation results are shown for reconstructed surface by using cubic interpolation and RBF networks. However in the current approach we have used the entire data set for training the network, the future work would focus on reducing the training data to a set of few feature points. References [1] [2] [3] [4] [5] [6] [7] [8]

[9]

Rusu, Cristian. "Neural Network Methods in Surface Modeling. Preliminary Notes." (2004): 111-120. Broomhead, David S., and David Lowe. Radial basis functions, multi-variable functional interpolation and adaptive networks. No. RSRE-MEMO-4148. Royal Signals and Radar Establishment Malvern (United Kingdom), 1988. Yue Wu, Hui Wang, Biaobiao Zhang, and K.-L. Du, “Using Radial Basis Function Networks for Function Approximation and Classification,” ISRN Applied Mathematics, vol. 2012, Article ID 324194, 34 pages, 2012. Seng Poh Lim and Habibollah Haron, “Surface reconstruction techniques: a review”, Artificial Intelligence Review, Springer, pp. 1–20, March 2012. Xumin Liu, Houkuan Huang and Weixiang Xu “Approximate B-Spline Surface Based on RBF Neural Networks” Lecture Notes in Computer Science, Springer,vol 3514, pp 995-1002, 2005. Sibo Yang, T.O. Ting, K.L. Man and Sheng-Uei Guan, “Investigation of Neural Networks for Function Approximation”, Procedia Computer Science, Elsevier, Volume 17, pp. 586–594, 2013. Khanna, Kavita, and Navin Rajpal. "Reconstruction of Noisy Bezier Curves Using Artificial Neural Networks." Proceedings of the Third International Conference on Soft Computing for Problem Solving. Springer India, Volume 258 pp. 459-466, 2014. Rajpal, Navin. "Comparative analysis of feed forward and radial basis function neural networks for the reconstruction of noisy curves." Optimization, Reliability, and Information Technology (ICROIT), 2014 International Conference on. IEEE, pp. 353 – 358, 2014. David F. Rogers, J. Alan Adams, Mathematical Elements for Computer Graphics Rogers McGraw-Hill Publishing Company, 1990.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Dual Mode DNA Cryptography: Bio Inspired Approach for Information Security Fakhrayh Al Harrasi1, Dr. M.S.Saleem Basha2, Dr. A. Mohamed Abbas2 and Mohamed Jameel Hashmi2 1 Post Graduate Student, Mazoon University College, Muscat, Sultanate of Oman. 2 Assistant Professor, Mazoon University College, Muscat, Sultanate of Oman. ________________________________________________________________________________________ Abstract: Modern studies focused on Deoxyribonucleic acid (DNA) because that DNA have several important features including the random nature of the sequence of nitrogenous bases consisting the acid and large storage capability of the information that led to its usage in the field of encryption where the appearance of a new branch which is encryption of DNA . This research provided a new method to encrypt text files using DNA were building a set of coding tables and using them to obtain the cipher text in DNA form, also used a set of transposition cipher methods for the purpose of increasing the security of the resulted cipher text. Security is more important in the organization. The companyâ&#x20AC;&#x2122;s document information is encrypted to save the information. The Deoxyribonucleic Acid (DNA) cryptography is emerged with progress of new promising structure of DNA Computing. This concept is massive parallelism with large information of density and inherent the DNA molecule of exploited of cryptographic purposes. The requirement is high tech model bimolecular in laboratory of proposed structure of DNA molecular one time pad scheme structure with DNA hybridization techniques and it minimizes the complexity time. The DNA cryptography use of DNA nucleotides and it denoted by alphabet characters C, G, A and T to perform encoded with micro messages. It is transmitting the secrets and creates the encoding rule of DNA components. Key words: DNA cryptography, PCR, Steganography _____________________________________________________________________________________ I. Introduction Every company have Network and have Internet access, it will be worry of its data from unauthorized access, so the system Administrator will be responsible to implement all kinds of security to make the network in safety Place to make the Network in Perfect protection and defense for any type of hackers. The System Administrator have keep all the Network Device in secure place for example , he or she have collect the routers, Firewall and switches in rack and the rack should be block all the time by using key. To limit access to the device and make any update or change the configuration for the device. In addition the hardware device should have password to access it from all the ports. The network device and all severs in the Network should be collected in one room its call server room or data canter. This room has on Authorized access for the people who have the permission to access for monitoring and fixing the network. This room should be blocking in all times and they can access it by using the key room or by using access card. For each employee in the company have user name and password to login his computer and before he leave his office he have to log of or make shutdown to limit any unauthorized access for the files and documents or any sensitive data by using the user accounts in the company. All the clients or the PCs in the company should have Software antivirus to protect the data from virus or others when they using USB. This DNA cryptography is to encrypt the textual information and maintain the security for the text message. The company important information like password, account number, employee password can encrypt. While encrypt the information can create the code and can decrypt the encrypted messages. The Nucleotides are quaternary source code and the each letter is denoted character string ATC and the letters B are ACC etc. The synthesize of secret message of are encoded into DNA sequence. According code synthesized of secret message of DNA with of secret messages. The secret messages are encoded to DNA sequence message 69 nucleotides and each word is PRC primers. The DNA message are physically can look the human DNA by nucleotides long and concealing DNA mixed, attached the common colorless form of microdots. II. Literature Review The DNA method is Biotechnological method and it has been wide class of developed for the operation of DNA and RNA standard. It includes site based and splicing operations. The Data Encryption standard (DES) are using vast parallelism are available via the recombinant of DNA to combinatorial to search the large number of various solutions of DNA standards. This method is solving various combinational method of search the problem and succeeds for various problems and intimately limited volume requirements. The research of DAN computing is re-combination of DAN technique and generally bimolecular computing (BMC) are using biotechnological computations [1]. The substitution method are using library to distinct pads and each method are specific randomly to generate pair the encryption of natural DNA and encryption to encrypt the binary data.

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The present novel and chip based micro array technology are 2D data input and encrypted output. The DNA cryptography are secretly take are input in the DNA standard and collection of various DNA standard. The potential limitations of cryptography method are showing assumptions of entropy and plain text messages. The various combination of DNA are showing sufficient to allow the universal commutation of BMC method and solve the difficult re-combinatorial problems are such as Hamiltonian path Problem of data Encryption standards [2]. The cryptography are executing the further application and pure combinatorial search. The DNA and RNA media for data storage and very large amount of the data are store in the compact volume. The vastly exceed storage contain and conversational electronic optic media and store the applied concentrations of DNA standards and constitute biological data and it obtained more convention of binary storage media. The DNA data are moved to conventional binary storage media and varieties of techniques are included in DNA standard use of alphabet and short of alphanumeric sequence. It discussed method to get the fast associative and search the DNA database for hybridization. The BMC technique are perform more sophisticated database operations and database join architecture massively parallel operation of the DNA data. The various techniques are including in the DNA chip array and binary data are encoded DNA standards using alphabet short sequential of letters with continues sequence [3].The data security and cryptography contain critical aspects and conventional computing is more important of DNA database application are provided the basic terminology are in the terminology. The sender and receiver wishes to send the message to encrypted format and over the finite alphabet. The encryption is the process of the plaintext message and transforming encrypted message of cipher text. The fixed code can be provide the initial mapping form he characters to the plaintext alphabet review process of physical system to encrypt and transformation of read out and unbreakable cryptosystem with successful of cryptanalysis and it is decryption algorithms. The two point of critical security of issues and it should be created about the codebooks and can be truly random to security of issues and randomly select the classical approach of random letter and secret code contain the cryptosystems and its absolutely unbreakable [4]. The DNA based cryptography are simulating the DNA using computers with Pseudo virtual DNA computing. The substitution method are using libraries and one time pad and define randomly pair-wise mapping the scheme are index random key string used in the encryption. The DNA sequences are matrix obtain and encode the original image and dividing into equal block of two logistic maps. The DNA sequence additional operations are use and the element in the block. The DNA matrix is decoded and get encrypted image. The plaintext are cipher pairs and constructed there complete coverage of lexicon on each pad and nearly unique word are mapping between the plain text and cipher pairs. The result of cipher word is plain text and assembled in random order. The DNA stand by the higher efficiency technique and hybridization of complemented Almenaâ&#x20AC;&#x2122;s original DNA experiment [5]. The two different cryptographic are based on the DNA binary potential interceptor and the decryption are using PCR with subsequent electrophorus. The DNA sequence are convert into spliced data and curing specified translated and converted mechanisms of DNA function. The method is pseudo DNA cryptography method. The DNA computing are full potential of DNA cryptography and the process are taking to molecular level and realized to outside of ultra-modern DNA laboratory. The DNA binary standard is more secured and satisfactory of multi-level and to resist exhaustive of statistical attack and differential kind of attack. The DNA Sequence of other related data are protein in the secure channel. The DES Data Encryption Standard algorithm with approximately 72 types of quadrillion possible keys is can generate. The security of DES is based on difficulty while picking the rout key and 16 round nonlinear operations can do. Breaking the DES is difficult. The following picture are DNA based cryptography method and it contain repeating unit are contain sequence of world, bi from the set in the cipher of the code book are matching words. The unbreakable of the letter standards and one time pad are between the message sender and decryption of undesired interceptor features of DNA message. To share the advanced for the information to assumption and it require of standard and provide the nature of secret message and fascinated combination of data and large number of message to assemble. The sequence of word Contain and form the set of cipher of codebook. It is matching the words. The sequence word or each pair and uniquely associates of plaint text with the string word. The with B1 are correspond the complement of the cipher word of B1. It can use polymerase primer and the extended growing of DNA standard boundary of paired. Each individual strand from one time pad and it consist of cod book library of specified word pairings. The one time pad contain DNA Strand of length are provided in this picture [6]. The DNA cryptosystem for 2D image are using DNA chip and randomly assembled one time pad and system capable of data encryption and decryption to provide the input data and output data in the form of 2D image are recorded in the microscopic array of DNA chip. The basic DNA technology is previous use of the DNA chip of Input and output. This system are describe the data set to encrypted and the chip are bearing immobilized data sent to encrypted format and various of the method of encoding encrypting in the form of data type and encrypted information. This chip contain the addressable of array and nucleotide of sequence of immobilized and multiple copy of multiple sequence in the microscopic pixel. The DNA chip are currently commercially available and construction of method [7]. The DNA Stegnography technique are class of techniques in the information are hide using secret message and within another message. The Stegnography system is original plaintext and it is not actually encrypted and disgusted of hidden message with another data.

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The historical Stegnography are use of grills in the mask and the image except of secret message. The micro photographic are placed in the large images invisible En-cryptography are generally consider conventional method and low security. The plain text are encrypted and listed of other media with numbers cases are using Stegnography method. It was practice the appealing and simple bio technological method with secret keys are stands. The plain text are DNA are stands and random assemble the knowledge of recombinant of separation of methods. The plain text message is standard and separated of hybridization with magnetic surface. It support the separation of combined with amplification steps are included [8][9]. Cryptanalysis of DNA Stegnography System is true cryptographic cipher and security depended with secret key. The security system is deriving the format of Stegnography system and adversary of existence message in the form of medium and distinguishes of plain text message. This system can be generated and DNA Stegnography system is security and depended of DNA stands. It is more responsible of the distracter and strands are constructed in the form of random of assembly and generated format of additional source are generating with plain text message. The probability of source are generate with plain text message. The probability of distribution of source generator to establish the English text message and class of none coding standard to constant and protein coding standard. The plain text DNA source has entropy and constant of plain text with images are entropy, overall entropy are most classes of DNA. The range 1.2 to 2. The each round for the data separation and assume the mixture of tagged plain text. The mixed high concentration of distracter and size of the information are reduced with previous ration. The pour of factionâ&#x20AC;&#x2122;s s of the volume of current and remaining faction volume into the test data [10][11]. Compression of Plain Text is another method to improve the DNA Stegnography system to recode plain text are using universal compression algorithm method and such also resulting redistribution of the recoded and assembled distracter sequence and suffice to provide and improved security . This system unlined conventional Stegnography method and recording to done by BMC recording method to similar discussed in this section summation to similar section are mapping Stegnography system and absolutely provide one time pads for equivalent decryption of text. The system are describe and consist of data set to encrypted in the chip are barking immobilized using DNA standards. The library of one time pads and encoded long DNA standard. The 2 dimensional images are various data type to incepted microscopic pixel. The DNA chip is currently commercially available and chemical method to construction of customer format [11]. The DNA chip are displayed analog and chemically added into the cipher worked using olio synthesis. The DNA chip complementary contains the plain text lexicon and fluorescent are labeled strands prepared to protect some pixels with plaintext of cipher words. The cipher word is stands and still labeled with decryption word pair strands and must be reconstructed appending the cipher words and proper plain text words. The decrypting process are fluorescent label and photo-labile based on unnecessary and cannot in the present image. The final to involve the bending reformed and word pair are stands to DNA chip with reading message of fluorescent. The following image is showing step by step procedure to encryption [12]. III. Proposed Method In the proposed method there are three steps are implemented for the purpose of DNA cryptography. First, each alphabet in the English language is assigned with the short length of DNA sequence. Second, a random key value is applied to the output of the first process. Third, convert the resultant English alphabets to their corresponding position in the English Language alphabet list. (A) Substitution Method: The proposed method starts with defining with the substitution table with each alphabets in English language including upper case and lower case, is assigned with a short DNA codons consists of 3 nucleotides of DNA (A, G, C, T). So, there are sixty four different combination of codons is possible. The substitution of DNA codons to the equivalent English letters are based on the upper case and the lower case of the English language alphabets as shown in the Table 1. Table 1: Mapping of English Letters to DNA codons Alphabet a b c d e f g h i j k l m

Codon TTC TCC TAC CTT CCT CAC CGC ATC ACC AAC AGA GTA GCA

Alphabet n o p q r s t u v w x y z

Codon TTA TCA TGT CTC CCA CAA CGA ATA ACA AAG AGG GTG GCG

Alphabet A B C D E F G H I J K L M

Codon TTG TCG TGC CTA CCG CAG CGG ATG ACG AGT GTT GCT GAT

Alphabet N O P Q R S T U V W X Y Z

Codon TCT TAT TGG CTG CAT CGT ATT ACT AAT AGC GTC GCC GAC

(B) Key database and PCR: To generate the key set for the secure communication PCR (Polymer Chain Reaction) is used to generate the random key to the each set of information which is to be sent secretly. PCR is the process of generating a

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considerable random length of DNA string. In this proposed method it used the PCR to generate the random key. The random key length in the proposed method is same size to that of the out got from the substitution method referred with Table 1. To be agreed between the sender and the recipient of the series of information and these are available within key database, including the representation of random alphabet to the combination of DNA codons of the key and the output of the substitution method s shown in the Table 2. Table 2: Codons Mapping with Alphabet DNA

DNA

Alphabets

DNA

Alphabets

So, there are sixteen possibilities of the choosing two codons from the set of 4 codons with the repetition. Once, mapped these codons with the alphabets, Convert the alphabet to the numbers according to the alphabetical order. The binary number system is then used to convert the alphabetical order to binary representation. To represent twenty six English alphabetâ&#x20AC;&#x2122;s position five binary bits is sufficient thus it may be considered a ReEncryption. Table 3: This table we used DNA Condon to Binary Binary

Codon

Then the binary representation of the message is then converted to DNA codons with refer to the Table 3. Further the DNA codons are transmitted to the intended recipient. Five stage DNA cryptographic technique A. Encrypt Method Diagram The overall process of the proposed work is stated as a flow char as shown in figure 1. Start

Enter plain text Choose DNA Combinations

Encoding letters To DNA Codon Get the Key Using the table to get encrypted Codon with the key ccodone with the key Convert letters to number according to alphabetical order Convert position to binary using BCD

Convert the binary to DNA End

Figure 1: Encryption Method

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B. Decrypt Method Diagram The decryption of the proposed method is the reverse of all the operations of the encryption method is illustrated in the figure 2. Start Enter cipher text

Convert the DNA to Binary

Convert binary to position orders Convert Number to the letters

Get the Key

Get Codon with the key Get Letters by Codon

Get the Original

End

Figure 2: Decryption Method IV. Implementation The proposed method is implanted in C#. The screen shot is shown in Figure 3 and Figure 4 to illustrate the way how the proposed system works. To clarify the mechanism of proposed method a sample plane text is used as an example. Note that the decryption process are the same steps but in contrary way. Table 4: Encryption of sample plain text. The Plain Text

my name is fakhrayh alharrasi

Convert letters DNA Codon

GCAGCCGGTTCTTTCGATCCTGGTACCCGTGGTCAGTTCGTTATCCATTTCGCCA TCGGTTTCGCTATCTTGCCACATTTCCGTACC

Generate randomly key using PCR

GCCCCGGGATAAGTTTCACTACCGGATGTGCGACCGCTGGTGAAATGTCGACCC CATAATGCTGGGCGTCTAGGTTTGCTGCGACCG

Mapping with the key

VFPEFWVVKBZKHBXSPKFXKEEHIZXWSHEVKFPVMBWVBHRKZXIBMHZEFFP KXYYBHMXVWHPHXMBYWWDXDHMBWFVKPFW

Character coding

22-06-16-05-06-23-22-22-11-02-26-11-08-02-24-19-16-11-06-24-11-05-05-08-09-26-2423-19-08-05-22-11-06-16-22-13-02-23-22-02-08-18-11-26-24-09-02-13-08-26-05-06-0616-11-24-25-25-02-08-13-24-22-23-08-16-08-24-13-02-25-23-23-04-24-04-08-13-02-2306-22-11-16-06-23

Binary Representation

10110-00110-10000-00101-00100-01000-00110-00010-11000-00010-10000-01100-0100011001-00010-00100-01000-01000-10000-00100-01001-10000-10001-00001-01000-0000100010-01000

DNA coding

AGAGAACGACCGAACCAACGAGATAGAGAGAGACACAAAGAGCGACACAAGA AAAGAGCAACGCACCGACACAACGAGCAACACAACCAACCAAGAAAGCAGCG AGCAAGATACGCAAGAAACCAGAGACACAACGACCGAGAGACATAAAGAGAT AGAGAAAGAAGAACGAACACAGCGAGCAAAGCAAAGACATAAGAAGCGAACC AACGAACGACCGACACAGCAAGCCAGCCAAAGAAGAACATAGCAAGAGAGAT AAGAACCGAAGAAGCAACATAAAGAGCCAGATAGATAACAAGCAAACAAAGA ACATAAAGAGATAACGAGAGACACACCGAACGAGAT

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Figure 3: Screen Shot of Proposed Encryption Method

Figure 4: Screen Shot of Proposed Decryption Method V. Conclusion In the paper, we have stated a new encryption method for the secure communication. The key used in this proposed method is generated by the polymer chain reaction, which means the DNA sequence got from the Polymer Chain Reaction is highly random in nature and impossible to guess or brute force attack. Also, two times the plain message was encoded with DNA equivalent; hence it is called Dual Mode encryption. In the proposed method a symmetric key has been used. In near future public key will be used. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Barnes W.M, “PCR amplification of Up to 35 kb DNA with high fidelity and high yield form bacteriophage templates”. Adleman. L, “Molecular Computations of Solution to Combinational Problems”, Science 266,1021 (1994) Adleman. L, “On Constructing a Molecular Computer”, Dept of CS., U.S.C., 1995. Available via anonymous FTP form. Baum. E.B, “DNA sequence useful for computations, 2nd Annual Diamacs Meeting of DNA based computers”, Princeton University, June 1996 Hagiya. M.M., Arita, and D.Kiga, “3rd DIMACS Meeting of DNA based Computers”, University Penns, June 1997. Head. T, “Splicing Schemes and DNA formulas with Molecules”. Mills. A, “Meeting of DNA based computing” , Baltimore, Penns, June 1998 Wang L, Liue A and S. Gillmor, “Surface based DNA computing operations Destroy and Read” Roberts S.S, “Turbocharged PCR”, Journal of NIH Research, Meeting on DNA based computing Baltimore , Penns. Schneier. B, “Applied Cryptography” 2nd Edition, Jonnwally J.Ziv and A.Lempel, A. “Universal Algorithm for Sequential Data Compression”. Mills, A.B.Yurke, P.Platzman, “Error-Tolerant massive DNA neural - Network computations”.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Optimization of land and water resource in Nakatiya Minor Canal command area in Udham Singh Nagar, Uttarakhand, India G.S.Yurembam1 and Vinod Kumar2 Ph.D Scholar, G.B.Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, INDIA. 2 Professor, Department of Irrigation and Drainage Engineering, GBPUA&T, Pantnagar, Uttarakhand, INDIA. __________________________________________________________________________________________ Abstract: With rapid urbanization and reduced water availability, the growing demand of food for large population can only be met by optimum utilization of available water and efficient allocation of available land to different crops. In this study a Linear Programming model was developed to maximize the net returns of the farmers considering, available land and water resources, crop water requirement and net return from different crops,. The objective function of the model was subject to the following constraints: Water availability; Land availability, Crop area, and preference to grow a particular crop in a specific area. Based on three rainfall patterns i.e. normal, deficit and surplus the optimization was performed. Under deficit rainfall condition the optimized results of area allocation from the command was obtained as 20.66% kharif paddy, 17.95% soybean and 1.71% maize during Kharif followed by 24.17% wheat and 2.30% pea during Rabi season. For normal pattern the maximum return can be achieved through 27.75% area under kharif paddy, 70.38% under soybean, and 1.71% under maize during Kharif followed by 34.63% area under wheat and 2.30% area under pea. Likewise the net return can be maximized by growing summer paddy on 98.13% area during Kharif and wheat on 97.54% area during Rabi season. Key words: Command area; cropping pattern; Crop water requirement; Linear Programming model. 1

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I. Introduction Land and water are two basic needs for progress in agriculture and economic development of any country. Both land and water are essential life supporting natural resources and they play a dominant role in agricultural production. Due to the increasing demand for food of the ever increasing human population, crop production per unit area needs to be increased and new areas with less favorable climatic conditions need to be cultivated. The introduction of irrigation schemes has improved agricultural production tremendously, but in spite of all efforts, areas covered by irrigation schemes are limited. Under these situations, it is necessary to do the best under the prevailing environmental conditions, in particular the seasonal water availability. This will need proper planning of the type and sequences of crops to be grown in a particular area for optimal overall production under rainfed condition. With continuing population growth and limited potential to increase suitable cropland, irrigation becomes an increasingly important tool to ensure sufficient global supply of food in the future. However increasing levels of irrigation will raise the cost of water and in some regions this may have severe consequences. As water scarcity increases, inefficient allocation of water will cause higher costs to the society. To fulfill the high demand for food, fiber and fuel of an ever increasing population, it is necessary to bring more area under cultivation or to increase production per unit area of available land and water resources. Bringing additional area under cultivation is difficult due to urbanization and a reluctance to disturb natural environments. Also, the allocation of water for irrigation will probably decrease from the present level of 90 % to 75-80 % over the next 10 to 15 years. Therefore, where demand of these natural resources for ever-increasing population outdoes the availability of these resources they need to be managed efficiently, optimally and sustainably. Keeping in view the need to find a better alternative solution of the problems especially faced by the farmers, an optimization model was formulated to maximize net income of farmers at different levels of water availability. II. Materials and methods The study was conducted for the command area of Nakatiya Minor under the Lower Bhakra Canal System located in Gadarpur Block of Udham Singh Nagar district of Uttarakhand state in India. Linear programming technique was used to allocate the available water optimally among different competing crop activities in the command area. The command area of Nakatiya Minor is 630 ha. III. Collection of rainfall data The rainfall data were collected for a period of 13 years (1999-2011) from the meteorological observatory located at the Crop Research Centre of G. B. Pant University of Agriculture and Technology, Pantnagar which was nearest to the study area. The daily rainfall data were converted into 52 standard meteorological weeks. In each year 7 days were counted in 52 meteorological weeks and in case of leap years 8 days were counted in 9 th meteorological week.

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These weekly rainfall data were used to classify the drought, normal and surplus rainfall weeks, using following criteria. 1. Drought week: - The week was classified as drought week in which rainfall received less than 50 percent of average rainfall. 2. Surplus week: - The week was classified as surplus week in which rainfall received more than twice of average monthly rainfall. 3. Normal week: - The week was classified as normal week in which rainfall received in between 50 percent and 200 percent of average weekly rainfall. IV. Crops and cropping pattern The study area falls in the semi arid zone, which being in Indo-Gangetic plane has fertile soils. The major crops grown by the farmers during khariff season are paddy and wheat in rabi season. Besides these crops in khariff, fodder crops and digger are also grown under irrigated conditions. The information regarding crops grown and area under different crops were obtained from District Agriculture Department and Subdivision of Irrigation Department, Rudrapur. The existing cropping patterns in the study area are shown in the Table 1. Table 1. Existing cropping pattern in the study area Khariff season Rabi season crops

Percent area

crops

Percent area

Paddy Fodder crops

92.90 3.27

Wheat Fodder crops

86.98 0.43

sugarcane

0.156

Others

4.92

others

4.4

Potato

3.07

V. Water Resources Besides rainfall, surface water supply from the Nakatiya Minor Canal system, and the ground water exploited through tubewells were the two important source of water in the command area. The daily canal ‘stage level’ data of Lower Bhakra were collected from Subdivision of Irrigation Department, Rudrapur. The surface water supply in the canal command area was limited. The minor command faces water deficit during Rabi season, due to low available flow of water in the Lower Bhakra Canal system. The Nakatiya Minor Canal command area was having a length of 6.1 km, designed capacity at the head of 0.53808 cumec and a total Culturable Command area of 630 ha. VI. Irrigation requirement of crops Net irrigation water requirements were calculated considering evapotranspiration, conveyance losses, application losses and effective rainfall. Rainfall, temperature, humidity, sunshine hours and wind velocity affect the evapotranspiration requirement of crops. Reference evapotranspiration was calculated using the following equation (Doorenbos and Pruitt, 1977). ET0 = Kp x Epan (1) where, ET0 = Reference crop evapotranspiration, mm/day Epan = Pan evaporation in mm/day and Kp = Pan coefficient. Crop evapotranspiration ET crop (mm/day) was then calculated using the equation: ETcrop=Kc ETo (2) where, Kc is the crop coefficient. Appropriate Kc values were selected for each crop which takes into account the crop characteristics, time of planting or sowing, stages of crop development and general climatic conditions (Dorrenbos and Pruitt, 1977). To account for the long duration of sowing crops in an area, the composite crop coefficient curves were prepared for calculating appropriate KC values (Singh et al., 1998). The net irrigation requirement of all crops was calculated individually as follows: IR= ETcrop - (Pe+Ge+Wb) (3) Where IR is the net irrigation requirement of crop (mm), Pe is the effective rainfall (mm), Ge is the ground water contribution (mm) and Wb is the stored soil water (mm). The effective rainfall (also called dependable rainfall) was calculated according to USDA Soil Conservation Service Method. The formulae used in the analysis were as follows: Peff = Pt 125  0.2  Pt  for Pt<250 mm (4)   125 and Peff = 125 + 0.1× Pt for Pt>250 mm (5) where Peff = Effective rainfall Pt = Total rainfall

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It was assumed that there was no contribution of ground water and there was no change in the value of stored soil water before and after the crop cultivation. The net irrigation requirement of the crop was estimated using the field water balance. The variables included where ETcrop, effective rainfall (Peff), groundwater contribution (Ge) and stored soil water (Wb). So, NIWR = ETcrop – (Peff+Ge+Wb) (6) where ETcrop = Crop evapotranspiration Peff = Effective rainfall Ge = Groundwater contribution Wb = Stored soil water. Considering no change in Wb before and after the crop duration and there is no contribution of groundwater. NIWR =ETcrop – Pe (7) VII. Area allocation model The model used was a linear programming model consisting of three parts: (1) an objective function for maximization of net returns; (2) a set of constraints; and (3) a set of non negativity constraints. The model was formulated to allocate the land area between various crops in order to maximize the net return from the command area, subject to availability of water and land area limitations under different seasons of the year. The model was formulated as follows: 1. Objective function: Maximize Z= ∑ Ci Xi ; for i=1, 2, 3…N (8) where, Z = total net returns from all the crops (Rs.) N= number of crops Ci= net return from ith crop (Rs./ha) Xi = crop area under ith crop ( a decision variable). The crop activities were kharif paddy (X1), summer paddy (X2), wheat (X3), sugarcane (X4), lentil (X5), pea (X6), soybean (X7) and maize (X8). The objective function will be subjected to linearity and non-negativity constraints.

2. Linearity constraints: i) Water availability constraints: ∑ Wi jXi ≤ Wj ; i=1..N, J=1,2….M where, Wij is the water requirement for ith crop during jth season (mm), Wj is the total water available during jth season (ha-mm) ii) Land area constraints: ∑ Xi ≤ Aj where, Aj is the area available for cultivation in different seasons of a year: i = Crops in kharif season (j=1); i = Crops in rabi season (j=2) iii) Crop area constraints: Ei ≤ Xi≤Mi where, Ei is the existing area under the ith crop (ha) Mi is the maximum area which may be kept under cultivation of ith crop (ha)

(9)

(10)

(11)

iv) Non negativity constraints:

Xi  0

(12)

The non negativity constraints in respect of all the decision variables have been imposed so that all the decision variables appear at positive level.

VIII.

Results and Discussion

The main crops grown in the command area are kharif paddy, summer paddy, wheat, sugarcane, maize, soybean, pea and lentil. The weekly gross irrigation water requirement for these crops was computed using Modified Pen-man method and considering the irrigation efficiencies. The gross water requirement estimated as 63.95 mm (kharif paddy), 987.1 mm (summer paddy), 157.2 mm (wheat), 768.4 mm (sugarcane), 80.7 mm (lentil), 112.7 mm (pea), and 15.04 mm (maize). The optimal cropping patterns for Nakatiya Minor Canal command area under different rainfall patterns are shown in Table 1. kharif paddy, wheat and soybean are the three crops that decide the net returns from the command based on the rainfall pattern and available canal irrigation water. It is observed from the Table 1 that under normal rainfall conditions the area under Kharif paddy and wheat increased by 7% and 10%, respectively as compared to deficit rainfall conditions. An increase of about 53% in area under soybean was observed under normal rainfall conditions as compared to deficit rainfall situations. This is due to the lower water requirement

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of soybean as compared to kharif paddy. The area left by kharif paddy and soybean under deficit rainfall pattern remained unoccupied. On comparing the optimal cropping pattern of surplus rainfall situation with other situations it was observed that there was a shift of kharif paddy to summer paddy, which was due to the high net return of the crop and sufficient water availability. The net return under surplus rainfall conditions further increased by about 6% through shifting from kharif paddy to summer paddy on the same area. The total net return from the Nakatiya Minor Canal command area under normal, deficit and surplus rainfall patterns are Rs. 1, 34, 42460, Rs. 83, 06,710 and Rs. 2, 98, 62,360 respectively. Table 2: Optimal Cropping Pattern in Nakatiya Minor Canal Command under different rainfall patterns Kharif Season Rabi Season Crop Area (ha) Area (%) Crop Area (ha) Area (%) Normal Rainfall Pattern Kharif paddy 174.82 27.75 Wheat 218.16 34.63 Sugar cane 1.00 0.16 Sugarcane 1.00 0.16 Soybean 443.38 70.38 Pea 14.50 2.30 Maize 10.80 1.71 Lentil 0.00 0.00 Fallow 0.00 0.00 Fallow 396.33 62.91 Total 630.00 100.00 630.00 100.00 Deficit Rainfall Pattern Kharif paddy 130.17 20.66 Wheat 152.27 24.17 Sugar cane 1.00 0.16 Sugarcane 1.00 0.16 Soybean 113.10 17.95 Pea 14.50 2.30 Maize 10.80 1.71 Lentil 0.00 0.00 Fallow 374.92 59.52 Fallow 462.22 73.37 Total 630.00 100.00 630.00 100.00 Surplus Rainfall Pattern Summer paddy 618.20 98.13 Wheat 614.50 97.54 Sugar cane 1.00 0.16 Sugarcane 1.00 0.16 Soybean 0.00 0.00 Pea 14.50 2.30 Maize 10.80 1.71 Lentil 0.00 0.00 Fallow 0.00 0.00 Fallow 0.00 0.00 Total 630.00 100.00 630.00 100.00 Net Return from command (Rs.) Normal Rainfall 1,34,42,460 Deficit Rainfall 83,06,710 Surplus Rainfall 2,98,62,360

IX. Conclusions During the surplus rainfall pattern, kharif paddy was completely replaced by summer paddy because of higher return of the summer paddy as compared to kharif paddy and availability of water during March-April, from the surplus rains. 1. In the study area i.e. Nakatiya Minor Canal command area, kharif paddy, wheat and soybean are the crops that decide the net return from the command area based on the rainfall pattern and available canal irrigation water. 2. The optimal cropping pattern for maximum return under deficit rainfall condition from the command is 20.66% kharif paddy, 17.95% soybean and 1.71% maize during Kharif followed by 24.17% wheat and 2.30% pea during Rabi season. 3. Under normal rainfall conditions the maximum return can be achieved through 27.75% area under kharif paddy, 70.38% under soybean, and 1.71% under maize during kharif followed by 34.63% area under wheat and 2.30% area under pea. 4. Under surplus rainfall conditions, the net return can be maximized by growing summer paddy on 98.13% area during kharif and wheat on 97.54% area during Rabi season. References [1]. [2]. [3] [4] [5].

Chandra, H.; Yaduvanshi, B.K. and Kumar, A. 2003. Optimum cropping pattern in a canal command area. Journal of Applied Hydrology, XVI (2) : 53-60. Doorenbos, J. and Pruitt, W. C. 1977. Guidelines for predicting crop water requirements. Irrigation and Drainage Paper No-24. FAO. Rome. 197p. Paul, J. V. and Raman, H. 1992. Selection of cropping pattern using linear programming technique. Indian Journal of Agrl. Engg., 2(2):125-131. Salokhe, V. M. and Raheman, H. 1989. Optimal utilization of Land and Water for bhagarbati Delta (India). Journal of Agricultural Engineering, XXVI (3): 229. Singh, A. K.; Singh, J. P. and Singh, R. 1998. Optimum utilization of resources of crop production. A case study. Indian Water Resources Society, 18(4):66-69.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Interactive Animations to Present Academic Subjects to Elementary School Children Cristian Javier Cauich Valle, Lizzie Narváez Díaz, Cinhtia M. González Segura Universidad Autónoma de Yucatán, Facultad de Matemáticas, Unidad Multidisciplinaria Tizimín Calle 48 A s/n x 31, 97700, Tizimín, Yucatán, México Abstract: Nowadays, technology has become a crucial factor for many of our daily activities, we are increasingly using new technological devices as a way to improve our lives. Several areas have been influenced by technology, education is one of them. With this in mind a project called "A Day of Science and Technology in your School", from which this article addresses specifically the use of software-based animations as a means to teach academic subjects to children who attend elementary school. It is presented an analysis of the current context of technology in elementary schools in the eastern part of Yucatan, as well as the opinions collected from the participating group of teachers of those schools, and some comments made by the project staff. Finally, the results obtained during those visits are presented and the usefulness and potential impact of interactive animations in the process of teaching and learning for elementary school children is analyzed. Keywords: Elementary school, primary school, education, animation, software, children. I.

Introduction

In recent years, the educational system at international level has been updated once again in order to be consistent with the digital revolution, computer systems has been implemented to benefit the learning of students, and many educators are advocating the use of laptops, desktops and mobile devices, among others. The impact and the incorporation of technologies of information and communication technology (ICT) in society and especially in the education sector, has led to that the information is a valuable resource that can be used in different ways, allowing teachers to develop innovative ways in which technology is used to create more effective learning environments and thus being able to generate knowledge [1]. In Mexico, there has been progress in the incorporation of ICT in the education system, both in public and private institutions. For example, the Telmex (telephones of Mexico) Foundation has implemented the Program of Digital Education and Culture for the elementary schools level, seeking to develop mathematical and computational skills based on educational axes, such as science, technology and universal values [2]. However, despite all the efforts made, we often hear talk of the term called “digital divide” of which there are different approaches and perspectives; some refer to it simply as the access of people to ICT; but more precisely, the Organization for Economic Co-operation and Development (OECD) says that it “refers to the gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard to their opportunities to access information and communication technologies (ICTs) and their use of the Internet” [3]. In summary we can say that it is the gap between those with and those without access to ICT. As a result, the digital divides lead to an increase in inequalities and are cause of a social and cultural exclusion. However, there are several alternatives to counteract this, such is the case of the Program of Digital Education and Culture, mentioned above, and of the educational program micompu.mx which belongs to the Secretariat of Public Education (SEP) of Mexico, which in its first phase will provide of a laptop to all children in fifth and sixth grades of elementary schools in the States of Colima, Sonora and Tabasco, having as main objective: “contribute, through the use and exploitation of personal computers, to the improvement of study conditions in children, the updating in teaching methods, the strengthening of collectives of teachers, the revaluation of the public school and the reduction of the digital divide” [4]. A. Information and Comunication Tecnologies in Education ICTs are in almost everything around us and not simply help us in communication, but that also in our daily life benefiting us beyond what we commonly do, such as facilitating trade, science, entertainment, education and countless other activities of modern life [5]. The World Bank has defined the countries access to ICT as one of the four pillars to measure their degree of advancement in the context of the knowledge economy (World Bank Institute, 2008) [5]. Many scenarios of the reality in which we live would be extremely different if ICT had not burst exponentially the way we live in the 20th century and beginning of the 21st. Some countries like Nigeria make use of ICT in other contexts such as jurisdiction. In this regard, the use of ICT as a predictor of lawyers' productivity, is discussed in [6] with a focus on the legal system of Nigeria. The work of justice professionals involves a high level of documentation and information, processing, storage and retrieval. The capacity of the tools and technologies to accelerate the documentation, management and processing of information are not only important for a lawyer, but professionally necessary [6].

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Albania implements the use of ICT in the different scenarios that may exist, as example we can see that [7] presents a research that focuses on crucial aspects of ICT in tourism, taking advantage of the fact that the Internet is a perfect platform to bring tourism products to the direct client. Therefore, it can be observed that ICT facilitate life in different contexts, and in education occurs something similar since there technologies are also present. In Latin America, teachers of primary and secondary education have a positive perception about the usefulness of technology in the teaching-learning process, since they think that learning to use it does not require too much effort and helps them to achieve their goals. It is worth mentioning that as well as they have a positive perception, they also have knowledge of the risks involved in the improper use of technology in education. In 2010 the Cisneros Foundation and the program for Update of Education Teachers (AME), based in Venezuela, offered for the second time a five month course to the teachers of elementary school in Latin America called “Appropriation of the Technology in the Basic School”, in order to promote the use of the Information and Communication Technologies (ICT) in the practices of teaching at school [8]. In the objectives of the course is intended to develop skills for selection and effective use of tools and resources available on the Internet, promote the use of information technology in teaching and learning, in the professional development of teachers, in the exchange of expertise and collaboration with other teachers in Latin America, develop positive attitudes towards the use of computer in education and promote reflection on the relevance of the information technologies for the innovation of the school systems in Latin America. The Latin American countries which have the most advanced level of ICT integration (access to technological resources in schools, teacher professional development, and the integration of ICT in the curriculum and in the learning process) are Chile, Uruguay, Argentina, Mexico, Brazil, Costa Rica and Colombia [8]. B. Animations The concept of animation is even older than movies or television. Animations are sequential images that appear in a fixed framework, a succession at the speed of 24 frames per second produces the illusion of fluid motion. Long ago, people wanted to express movement in images, so taking several of these and passing them quickly can be done to simulate a specific movement. In addition to entertainment, animations are also used as a successful and efficient tool for learning in the field of education, since it improves different kinds of skills and knowledge, sometimes even better than the traditional way of teaching. For example we can mention that some authors emphasize that animations in literature classes improves the intellectual, emotional, and social experience of the student. Currently, television and the Internet are two of the main media of animations, today's children spend more time watching television or doing some activity on the computer, therefore, digital is important for all aspects of their life since they use digital as a second language [9]. Children can find the use of visual and audio resources to share information, knowledge and ideas, which is an opportunity to express themselves and improve their knowledge and skills in the learning process. The research described in [10] states that the computer-assisted learning has positive effects on student achievement, increasing the performance of students at different levels of education and subjects II.

Use of ICT and Animations in Elementary Schools

In Mexico, the authors of this work are part of the Multidisciplinary Unit Tizimín (UMT) of the Autonomous University of Yucatan (UADY), where takes place the project called “A day of Science and Technology in your School", which consists of visits to different elementary schools of the eastern part of the State of Yucatan. The overall objective of the project is to promote and encourage interest in science and technology among students through the exhibition of academic subjects related to the six grades of primary education in Mexico, using scientific and technological material; to date 12 schools have been visited mainly from the municipalities Tizimin, Panaba, Sucila and Calotmul. The project consists of 6 stands or booths covering different areas: animations, mathematical challenges, robotics, electronics, paper folding and video; each stand consists of 2 or 3 students of different grades enrolled in the Bachelor of Computer Science in the UMT, who direct the activity supported by their professors advising them. In Fig. 1 is shown one of the activities carried out in the stand called "Animations", to which this paper focuses. In the stand of animations, some educational applications and some videos with cartoons are used to teach children topics of some courses of their scholar grade. To mention one example, in Spanish are practiced spelling and vocabulary; in math are practiced basic operations as sum and product, the multiplication of fractions and the use of the scale; in the exploration of nature and society are addressed topics such as: the food pyramid, and states and capital cities of the Mexican Republic, among others. On the other hand, some animated videos are used to promote environmental responsibility, encouraging the use of materials and products that do not harm the environment. A video is also used to compare a person who consumes too much packaged and instant products and another that is more aware of the importance of eating healthy. Some other videos also promote care of electrical energy and water saving.

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Cristian Javier Cauich Valle et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 11(3), December 2014-February 2015, pp. 217-222 Figure 1. Animation stand of the project â&#x20AC;&#x153;A Day of Science and Technology in your Schoolâ&#x20AC;?.

Besides, supporting the issues discussed is projected a video that illustrates the current technology in which we are engaged and which is to come in the not-too-distant future. For example: a way of working from home, to watch a movie and even cooking, always supported with the use of technology. It is worth mentioning that this projection aroused a great interest in the children due to the novelty and surprising that it results for them. Finally, to teach the children a new view and use of the computer, besides to use their imagination and creativity, the basic steps to design and create a 3D animation are shown, using a software called Blender, specifically the version 2.69. During the exhibition, basic shapes are created such as spheres, cones, cubes, rectangular prisms, pyramids, etc. Animals, objects such as chairs, tables, and floors of different kinds of material as grass, concrete and wood, are designed. Later it is made a small animation with the designed objects. In Fig. 2 is illustrated the design environment of Blender. Figure 2. Design in 3D with Blender v2.69.

Using animation software to create a short 3D animation in the classroom, children experience a new way of seeing everything that is behind the animated films, this situation allowed to compare the animated drawings of some years ago with current films. Projection of educational software useful for academic subjects, arouses in children and teachers the interest of using the available technological resources to enhance their experience during the process of teaching and learning. During the project, the planning of the animation stand was divided in 2 stages, below each one of them and the reasons that led to their creation are described, as a strategy to improve results. A. First Stage Initially, prior to the exposure of materials to children, it was applied a diagnostic questionnaire to a sample consisting of 30% of the children in each group, approximately. Subsequent to the event, another similar questionnaire was applied. The questions included in each of the surveys were directly related to the activities carried out during the event, i.e., the issues related to the questions asked to the participating children before and after the event were covered during the same. This methodology was intended to compare the immediate observable impact on the participating children. In addition, a brief opinion survey was applied to the group of participating teachers, in order to obtain their own perception of the activities as well as suggestions and comments in general to improve the event. B. Second Stage Due to the limited time to work with children (30 minutes per group) and the time that took the applications of initial and final questionnaires, it was decided to modify the strategy. Thus, the performance of children are registered through the observations made by the contributors to the project, who at the end of each visit recorded their appreciation in polls answered during the feedback session, which was complemented with the opinion of the elementary school teachers, who responded to a slightly more extensive survey. III. Results This paper describes in more detail the second stage of the project, so following are presented the results obtained and the observations made by the collaborators of the animation stand during this second stage. The opinion survey was applied to teachers of the elementary schools visited and of the various grades. In total 45 teachers participated, who were surveyed in order to estimate the impact of the preformed activities, specifically here is described the animation stand. Table 1 shows information regarding the years of teaching experience of the participating group of teachers. As can be seen, most of them have less than 20 years of experience, allowing

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to catalog them as young teachers, which means that, although they are not native to the technology, they have had early contact with it. Table I. Years of theaching experience. Years of experience Percentage of theachers 0-9 33% 10-19 33% 20-29 18% 30-39 11% No answer 5%

A. Opinion of the Visited Teachers In general, the event had a good acceptance among directors, professors and students from all the visited schools, as shown in table 2. At asking the group teachers their general opinion about the event, 36 of them (80%) indicated that it was "excellent", 5 of them (11%) did not provide an answer, and 4 more (9%) teachers categorized it as "good". Their opinions expressed indicated that students were very motivated, participative and interested, due to the innovative and interactive topics presented in each stand. In addition, children had fun learning topics of their interest but above all they were motivated and making use of the tools that will serve them in the classroom to keep learning. It is worth mentioning that topics were obtained from their curricula. Table II. Evaluation of the project. The event seemed to him Total of teachers Excelent 36 Good 4 Regular 0 Bad 0 Worst 0 No answer 5

B. Suggestions from the Visited Teachers Among the suggestions to improve the event, teachers expressed that it was very important to give more time to stands, since originally it was 25 minutes per stand, period in which the planned topics had to be covered, along with their corresponding activities. Subsequently the time extended 5 minutes more. The majority of the surveyed ones did not find some other negative element in the different stands, including the one of animation. It is worth mentioning that in the first stage all grades were included (1 to 6), i.e., with the 6 or 12 groups of each school. At that time, the presenters of the stands indicated that to work with groups of almost 80 children (in some cases) was very difficult, so instead of working with all the grades, it was decided to include only half of them, thus electing the more advanced groups. That means that, if the school had 2 groups of each grade, it was decided to work only the 3 more advanced degrees (4°A, 4°B, 5°A, 5°B, 6°A and 6° B). In the case that the school has 3 groups by grade, we only worked with the 2 more advanced degrees. In the last edition of the event, it was decided to also cover children from third grade since the number of children permitted it. Of the seven schools visited in the second stage, only one of them is private: College Teresa of Avila, as shown in table 3.

1. 2. 3. 4. 5. 6. 7.

Table III. Schools visited and number of participating children. School Participating grade # of children Justo Sierra Méndez 4º a 6º 366 Abelardo Conde Ruz 4º a 6º 193 Otilia López 5º a 6º 160 Gabino Barreda 4º a 6º 205 Colegio Teresa de Ávila 1º a 6º 120 Sebastián Molas 4º a 6º 181 Luis Álvarez Barret 3º a 6º 226 Total 44 groups 1,451

C. Technologic context of the elementary schools visited The group of collaborators in the project noted that the majority of schools does not have the technological resources needed to implement the activities to perform during the event (most of them are public), which basically consist of computers and projectors in the majority of cases, although in some stands are used sheets of colors, wood sticks, printed boards and robots (Robotics stand). In this regard, 49% of the surveyed teachers said that there are very few technological resources at his or her school, 35% said that they do not have the necessary technological resources and 7% said that they have enough technology, although most of such equipment are in poor condition. The remaining 9% did not provide any opinion (see Fig. 3). With regard to the level of ICT proficiency that the teachers themselves feel they possess, 56% considers to be in an intermediate level, 18% indicates an advanced level and 22% considers himself a beginner. 4 percent of teachers could not answer at which level they are, i.e. they do not selected an answer. (See Fig. 4). As regards the possibility of training in the use of ICTs, it is interesting that most of the teachers (62%) said that they receive training in the use of ICT, 33% said that on rare occasions they are summoned to a meeting for the

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purpose of training and 5% said that there is definitely no such support. Therefore we can say that more than half of those surveyed have or may have the necessary knowledge for the management of ICT. (See Fig. 5) Figure 3. Opinions of teachers about the availability of technological resources at their schools. At your school, Do you have the neccesary technological resources?

7% 9%

Yes No Very Few No Answer

35% 49%

Figure 4. Levels of ICT proficiency Which is your level of ICT proficiency?

4% 22%

18%

Beginner Intermediate Advanced Expert No answer

56%

Figure 5. Training in ICT Does the education system, to which you belong, provides you with training in the use of ICT?

Yes

33% 62%

No Some Times

IV. Conclusions This work has presented the activities carried out at the stand of "Animations" and the results obtained so far, as part of the project named "A day of science and technology in your school". In particular, the animations stand provides to teachers a set of options for using ICT in the classroom. During the visits to elementary schools, it has been perceived a very good acceptance of the project by the visited teachers and students, who orally and in writing, have externalized a great amount of positive reviews. For example, teachers mention that the animations and programs used in the activities are new and innovative, teachers also comment that the animated videos help to motivate children but specially to make them aware of the issues addressed. Thus, they claim that they would be willing to use them in their class sessions and that the video about future technology seemed significant and interesting as it shows to the children a window to the world to come, awakening their interest and motivation to learn more on the subject. Besides, during those visits it has been observed that most of the schools are equipped with a projector and computer equipment in some salons, however the lack of maintenance has made them unusable. Precisely one of the problems mentioned insistently by the teachers on the opinion surveys is the lack of computer equipment in their own schools, which prevents the children to interact on a daily basis with computers. However, despite the fact that schools do not have adequate technological resources, with this project it has been fomented the interest of teachers about having tools or materials to use in the classroom, as can be seen in the graphs presented.

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VI.

References

Orozco, E., et al. “El m-Learning como soporte para la construcción de conocimiento en la enseñanza de las Ciencias,” Conferencia Conjunta Iberoamericana sobre Tecnologías y Aprendizaje. Cancún, Quintana Roo, México 2013. [2] Programa de Educación y Cultura Digital, telmex educacion, México. 2014 http://www.telmexeducacion.com/elprograma/Paginas/default.aspx?IDT=que_es/ Fecha de última consulta 17 de Febrero de 2014. [3] Sandoval, A. “Explorando la brecha digital en México: Diagnóstico del proyecto e-México en el estado de México,” Espacios Públicos, 2006, vol. 9, núm. 17. México. [4] micompu.mx Dotación de equipos de cómputo portátiles. Documentos Base, SEP. México, D.F. 2013 http://basica.sep.gob.mx/ Fecha de última consulta 26 de Enero de 2014. [5] Romaní, J. (2009). “El concepto de tecnologías de la información. Benchmarking sobre las definiciones de las TIC en la sociedad del conocimiento,” (Spanish). Zer: Revista De Estudios De Comunicación, 14(27), 295-318. [6] Owoeye, J. (2011). “Information Communication Techonology (ICT) Use as a Predictor of Lawyers' Productivity,” Library Philosophy & Practice, 122-134. [7] Kromidha, J., & Muca, B. (2011). “Adoption of Information and Comunication Technology in Albanian Tourism Industry in Global Setting: Challenges and Benefits,” Journal Of Information Technology & Economic Development, 2(1), 64-73. [8] García-Urrea, S., & Chikhani, A. (2012). “PERCEPCIONES QUE TIENEN LOS DOCENTES DE AMÉRICA LATINA SOBRE LAS TECNOLOGÍAS DE LA INFORMACIÓN Y LA COMUNICACIÓN,” (Spanish). Revista Q, 6(12), 1-32. [9] Ausekle, D., & Šteinberga, L. (2011). “ANIMATION AND EDUCATION: USING ANIMATION IN LITERATURE LESSONS,” Pedagogy Studies / Pedagogika, (104), 109-114. [10] AKTAŞ, M., BULUT, M., & YÜKSEL, T. (2011). “THE EFFECT OF USING COMPUTER ANIMATIONS AND ACTIVITIES ABOUT TEACHING PATTERNS IN PRIMARY MATHEMATICS,” Turkish Online Journal Of Educational Technology, 10(3), 273-277. [1]

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net

Citizen’s Charter Validation

Ajaya Kumar Tripathy1, 3, Manas Ranjan Patra2, and Sipali Pradhan1 1

Dept. of Computer Science and Applications, Utkal University, India 2 Dept. of Computer Science, Berhampur University, India 3 Dept. of CSE, Silicon Institute of Technology, India

Abstract: Citizen Charter is a document of commitments made by a government organization to the citizens in respect of the fundamental procedures being provided to them. The main objective of Citizen Charter is to build trust between the citizen and administration, and to streamline administration in tune with the needs of the citizen. The bureaucratic process flow for different administration services are dynamic and volatile in nature. To make government organizations accountable towards citizen charter plays a vital role. However, making citizen charter without proper validation of fulfilling them is of no use. For electronic validation it is essential to specify them formally. This paper proposes a novel approach for citizen charter Specification and validation. Keywords: e-Governance, Citizen’s Charter, Service Based Systems, Service Based Systems Monitoring

I. Introduction In general most of the services provided by government to the citizen, involves one or more government organizations. Whereas the involved organizations works independently irrespective of their involvement in inter organizational services. In case of any degradation of quality of service arises in a particular service none of the organizations are taking responsibility. To make government accountable towards quality and on time service delivery Citizen Charter plays an important role. Research on e-governance [1, 2] spans over many interesting issues covering all the phases of government service lifecycle. In this article, we focus on yet another very interesting research topic: Citizen Charter specification and validation. A Citizens' Charter represents the commitment of the government organization towards standard, quality and time frame of service delivery, grievance redress mechanism, transparency and accountability. Citizens' Charter one type of agreement between citizens and government organizations mentioning the terms and conditions of the services provided by the organization. For government organizations validation of Charter is necessary in order to gather evidence regarding the proper service provisioning in case of any dispute with the citizen. Beside these reasons monitoring of government organizations are necessary for the administrators to make the system effective by taking actions before violation of charter. It is in the interest of administrators and organizations to create citizens’ charters with minimum human interaction on one hand, generating electronic charters and monitor the charter on the other hand [3, 4, 5]. Several monitoring frameworks have been proposed to cope with the Web Service Based System monitoring (see e.g., [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]). We believe that in the literature no effort has been given to formally specify and monitoring Citizens’ Charter. A novel solution to the problem of formal specification and provisioning time validation of Citizens’ Charter has been proposed in this paper. This paper proposes a Citizens’ Charter specification and validation framework based a Web Service Based System monitoring framework [4,5 ]. The Citizens’ Charter specification is based on a formal specification language called Monitor Specification Language (MSL) [5]. MSL Specified formulas are monitored at runtime using the monitoring framework [4,5 ]. The monitoring framework is an event based and non-intrusive in the sense that events are collected during the operation any organization. MSL is a temporal logic based language. The choice of MSL as the language for specifying Citizens’ Charter is due to its expressiveness as a formal language, which allows specification of temporal constraints and the ability to monitor Citizens’ Charter using well defined reasoning processes in the form of inference rules written in first order logic. The rest of the paper is structured as follows. Section II, introduces an example scenario. The Citizens’ Charter formal specification has been described in Section III. Section IV, gives an overview of the monitoring framework. Finally we conclude our work with a mention of our future research directions in Section V.

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II.

Online Passport Application Scenario

Figure 1: Online Passport Application Service Message Flow This section depicts an online government passport issue service provided to the citizen as a visionary egovernances system. To apply for a passport citizens make an online application to the passport office with personal details. The passport office make preliminary verification of the eligibility of the application and either reject the application directly or ask the citizen to make a application fee payment, though an online payment system. Upon receiving the payment the passport office send the citizen details to DCP office for residential and character verification .The DCP office make a verification at their level and replies with a positive or negative result upon receiving a positive verification result the payment office make further processing and issue a passport to the citizen otherwise replies the citizen with a reject information .Even if this is a very simple government service scenario the citizens, administrators and the passport office my required to monitor following properties as a citizen charter rules : Rule 1. Passport should be issued only if the verification certification status is positive. Rule 2. Verification duration should be less than 10 days. Rule 3. Passport issue process should be less than 30 days. Rule 4. Count the no of times the payment process fails. Rule 5. Average response time of passport application process should be less than 20 days. Rule 6. Verification process starts after payment process success Rule 7. Count the percentage of application rejected. In this example scenario, we assume the message flows of online passport service (OPS) are as follows. Citizens starts the passport application process by sending passportRequest(Name,add,age,que.apptype) to the OPS. OPS makes a preliminary verification and replies the citizen by sending a reject(Reason) or accept the application by sending a application fee payment request by paymentRequest(amount) by this message upon receiving this message the client send the payment details by sending paymentDetails(accInfo,amt). After receiving this OPS use a online banking service (Bank) for payment by sending paymentRequest(officeAccInfo,citizenAccInfo,amount) message. The Bank processes the request and replies with a payment success message or payment fails message. Based on the bank message the OPS acknowledge the citizen about the application fails of application success status. Then the OPS send a verification request to Deputy Commissioner of Police office (DCPO) for citizen residential and charter verification by sending verificationRequest(Name,add,que,age). After verification DCPO replies with a positive or negative status. In case of negative verification status OPS send the citizen a rejection message (passportReject(reason)). In case of positive verification status OPS makes further processing and issue a passport to the citizen by sending passportIssue(ID) message to the citizen. The complete message flow has been shown in Figure 1. III. Monitor Specification Languege The citizen charter need to be monitor as expressed in a temporal logic based, executable language, which was proposed in [4, 5], which is described as follows. In this language the properties are specified in terms of events. Grammar: event ꞉꞉= eventName | eventName.(condition), eventName ꞉꞉= [a ‒ z] [a ‒ z A ‒ Z 0 ‒ 9] *

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condition ꞉꞉= type var cond value | condition ˅ condition | condition ˄ condition type ꞉꞉= int | double | string var ꞉꞉= [a ‒ z A ‒ Z 0 ‒ 9] + cond ꞉꞉= ≠ | = | > | < value ꞉꞉= [‒ +] [0 ‒ 9] + | [‒ +] [0 ‒ 9] + [0 ‒ 9] * | [a ‒ z A ‒ Z] * This part of grammar facilitates the specification of the events with the condition on the internal variables of the event, eventName specifies the message name,type,var and value specifies the internal variable name (which may be int, double or string), internal variable name (which is a string) and internal variable value(which can be real number, integer or a string) respectively. Condition is defined as type varcondvalue :where type is the data type of the variable(int or double or string), varis the name of the variable, condis the logical condition ( = | <|>) on variable and value is a value(number / string) to compare with the variable value. The following grammar defines the Boolean, temporal and statistical formulas. We distinguish Boolean formula ‘b’ which monitors the properties that can be either true or false, a numeric formula ‘n’ which monitor properties that define a numerical value (which includes temporal and statistical formulas). b ꞉꞉= event | b ˅ b | b ˄ b | b => b | ˜ b | n = n | n > n | Y b | O b | H b | b S b n ꞉꞉= C (b) | T(b) | b ? n : n | n + n |n – n | n * n | n / n | NUM NUM ꞉꞉= [0 ‒ 9] * | [0 ‒ 9] + [0 ‒ 9] * A. MSL Specification The monitoring properties we have introduced is Section II can be define by the following MSL formulae: Rule 1. Passport should be issued only if the verification certification status is positive. passportIssue=>0(verificationRes (status = positive)) Rule 2. Verification duration should be less than 10 days. T(~ verificationRes S verificationReq)<10*24h Rule 3. Passport issue process should be less than 30 days. T(~(passportReject V passportAccept) S applicationSucess)>30*24h Rule 4. Count the no of times the payment process fails. C(paymentFails) Rule 5. Average response time of passport application process should be less than 20 days. SUM(T(~(passportReject V passportAccept) S applicationSucess)) / C ( passportAccept passportReject))<20*24H

Rule 6. Verification process starts after payment process success. verificationReq=>0(paymentSucess) Rule 7. Count the percentage of application rejected. C(passportReject V passportIssue)*100/C(passportRequest) IV. Monitoring Framework For monitoring citizen charter we have used the monitoring framework proposed in [4, 5], which is depicted in Figure 2.

Figure 2: Service Based System Monitoring Framework

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This framework has 7 main components namely Event Bus, MSL Specification Interface, Monitor Generator, Monitor Repository, Monitor Handler, Monitor Result DB, Monitor Result Viewer. Event Bus: The”Event Bus” collects events from the service based system to be monitored and puts the events in an event queue. The monitors consume the events from the queue. The types of events the Event Bus receives are: messages received from or sent to the services by one of the component services of the process, interesting events from business layer/Infrastructure layer. MSL Specification Interface: The “MSL Specification Interface” is used by the framework to input the MSL formulas for creation of monitors. Monitor Generator: The “Monitor Generator” is a MSL compiler, designed using Bison [15] as parser generator and Flex [16] as lexical analyzer generator. This compiler translates the MSL specified formula to a C program named MonitorID.c and stores it in the Monitor Repository, where ID is the serial number of the monitor. Also the name of the created monitor (i.e, MonitorID) is registered (i.e, stored) in the Monitor Repository (i.e, a registry which stores name of created monitors). Monitor Repository: The “Monitor Repository” is used to index and store the created monitors for on time consumption of events and validation of the rules. Monitor Handler: The “Monitor Handler” is responsible for receiving new events from Event Bus, creating the required new instances of the existing monitors in the Monitor Repository and waking up appropriate monitors to consume the incoming event. Monitor Result DB: The “Monitor Result DB” is responsible to store the updated results produced by the MSL formula monitors after consuming the Events from the Event Bus. Monitor Result Viewer: The “Monitor Result Viewer” is an interface to display the results stored in Monitor Result DB at real time. V. Conclusion and Future Work In this paper, we have presented an approach for monitoring Citizen Charter for Government systems. An event based approach has been proposed that separates functional logic from the monitoring functionality and Citizen Charter monitoring. Further, a monitoring language has been discussed to formally specify the Citizen Charter .The specification is automatically translated into an executable C program which is used by the framework while monitoring the specified behavior of the system .In future we will try to implement and test the framework in a real like scenario. References [1] [2] [3] [4]

[5]

[6] [7] [8] [9] [10] [11] [12] [13]

[14] [15] [16]

M R Patra, A. K. Tripathy and R. K. Das. Monitoring of Service Based e-Government Systems. In the 5th International Conference on Theory and Practice of Electronic Governance: ICEGOV 2011 Estonia, Pages 353-354 ACM 2011. A. K. Tripathy, M. R. Patra. MSL Based Service Based e-Governance Systems Monitoring. In International Journal of ACM Jordan: The research Bulletin of Jordan ACM Volume II (II), Page 90-101 Jordan 2012. F. Barbon, P. Traverso, M. Pistore, and M. Trainotti. Run-time monitoring of instances and classes of web service compositions. In ICWS, volume 6, pages 63-71 2006. A. K. Tripathy and M. R. Patra. An Event Based, Non-Intrusive Monitoring Framework for Web Service Based Systems. In Proceedings of the 6th International Conference on Next Generation Web Service Practices: NWeSP 2010, pages 201-206. IEEE, 2010. A. K. Tripathy and M. R. Patra. Service Based System Monitoring Framework. In International Journal of Computer Information Systems and Industrial Management Applications: IJCISIM, Volume 3, Page 924-931, 2011, Dynamic Publisher USA. L. Baresi, C. Ghezzi, and S. Guinea. Smart monitors for composed services. In Proceedings of the 2nd international conference on Service oriented computing, pages 193–202. ACM New York, NY, USA, 2004. A. K. Tripathy and M. R. Patra. Modeling and Monitoring SLA for Service Based Systems. In Proceedings of the International conference on Intelligent Semantic Web - Services and Applications – ISWSA 11, Jordan, pages 60-65. ACM 2011. L. Baresi and S. Guinea. Towards dynamic monitoring of WS-BPEL processes. Lecture Notes in Computer Science, 3826:269, 2005. A. K. Tripathy and M. R. Patra. Automating WS-Agreement Monitoring. In International Journal of ACM Jordan: The research Bulletin of Jordan ACM Volume II (I), Page 99-113 Jordan 2011. A. K. Tripathy, M. R. Patra and S. K. Pradhan. Run-Time Monitoring of SLA for WSBS at Application Layer. In International Journal of Software and Web Sciences, Volume 9(2), Page 100-104, Aug 2014, IASIR. K. Mahbub and G. Spanoudakis. A framework for requiems monitoring of service based systems. In Proceedings of the 2nd international conference on Service oriented computing, pages 84-93. ACM, 2004 A. K. Tripathy, M. R. Patra, M. A. Khan H. Fatima and P. Swain. Dynamic Web Service Composition with QoS Clustering. In 21st IEEE International Conference on Web Services: ICWS, pages 678–679, Alaska, USA, 2014. A. K. Tripathy and M. R. Patra. Cost Effective, Requirement Oriented Web Service Composition and Adaptation. In the International Journal on Recent Trends in Engineering & Technology: IJRTET, Volume 05, No. 01, Page 95-98 Mar 2011. ACEEE USA. S. Satpathy, A. K. Tripathy, S. K. Pradhan and M. R. Patra. A Survey & Analysis of Web Service Composition. In International Journal of Software and Web Sciences, Volume 9(1), Page 23-27, Aug 2014, IASIR. C. Donnelly, and R. Stallman. The YACC-compatible parser generator. The Free Software Foundation. 1992. V. Paxson and others. Flex–fast lexical analyzer generator. Free Software Foundation, 1988

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Type 2 Diabetes, Therapeutic Targets and Potential Drugs: A Review Neha Verma1 and Usha Chouhan2 1,2 Department of Bioinformatics, Maulana Azad National Institute of Technology (MANIT), Bhopal- 462051 (M.P.), INDIA _________________________________________________________________________________________ Abstract: Type 2 diabetes mellitus (T2DM) is very common diseases and results from a combination of defects in insulin secretion and insulin action, either of which may predominate. This type of diabetes accounts for 90 to 95% of all diabetic patients. The current prevalence of T2DM is 2.4% in the rural population and 11.6% in the urban population of India. The present review gives a brief introduction to various therapeutic targets for Non Insulin Dependent Diabetes Mellitus (NIDDM) and available drugs which can activate/inhibit the receptors and expected to lower low density lipoprotein (LDL) cholesterol and triglycerides, raise high density lipoprotein ( HDL) cholesterol, and normalize hyperglycaemia. The increasing incidences of T2DM, represents a considerable public health problem and is characterized by loss in sensitivity of tissues towards insulin which can be restored by activation of peroxisome proliferator-activated receptors (PPARs), members of nuclear receptor family which are functioning as ligand dependent transcription factor. The present paper also reviews medicinal plants that have shown experimental or clinical antidiabetic activity and that have been used in traditional systems of medicine. Keywords: Type 2 diabetes mellitus; peroxisome proliferator-activated receptors; non insulin dependent diabetes mellitus; low density lipoprotein; high density lipoproteins. __________________________________________________________________________________________ I. Introduction Diabetes mellitus (DM), long considered a disease of minor significance to world health, is now taking its place as one of the main threats to human health in the 21st century [1]. It is the most common non-communicable disease worldwide and the fourth to fifth leading cause of death in developed countries [2].The global figure of people with diabetes is set to rise from the current estimate of 150 million to 220 million in 2010 and 300 million in 2025 [3]. Developing countries such as India has seen the maximum increase in the last few years. The current prevalence of type 2 diabetes is 2.4% in the rural population and 11.6% in the urban population of India. It has been estimated that by the year 2025, India will have the largest number of diabetic subjects in the world [3]. DM is a heterogeneous group of disorders characterized by high blood glucose levels [4]. II. Cause of T2DM Type 2 Diabetes Mellitus (T2DM) is a non-autoimmune, complex, heterogeneous and polygenic metabolic disease condition in which the body fails to produce enough insulin, characterized by abnormal glucose homeostasis [5]. Its pathogenesis appears to involve complex interactions between genetic and environmental factors [5]. T2DM occurs when impaired insulin effectiveness (insulin resistance) is accompanied by the failure to produce sufficient cell insulin [6]. T2DM as a common and complex disease has been characterized by the following causes: Obesity: obesity is also considered a key risk factor for T2DM. The association between increasing body mass index (BMI) and greater weight gain and risk of diabetes is most pronounced among Asians, suggesting that lower cut off BMI values are needed to identify Asians at a higher risk of diabetes [7] BMI cut point for Indians for any cardio metabolic risk factors is 23 kg/m2 in both sexes, whereas that of waist circumference (WC) is 87cm for men and 82cm for women [8]. Abdominal adiposity: there is also a probable indication that there is a preferential abdominal adiposity in Indians irrespective of the degree of general adiposity [9]. Imbalance of human metabolism is associated with T2DM: Changes in work patterns from heavy labor to sedentary, the increase in computerization and mechanization, and improved transport are just a few of the changes that have had an impact on human metabolism [10]. Ethnicity: the interethnic differences (like differences in prevalence of T2DM among Europeans, Americans, Chinese, and Asian Indians) in insulin resistance may have an environmental or genetic explanation. The main acquired factors that seemingly increase insulin resistance in all ethnic groups include obesity, sedentary lifestyle, diet rich in animal products, and aging [11].

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III. Complications of Diabetes in India The burden of diabetes is to a large extent the consequence of macrovascular (coronary artery disease, peripheral vascular disease, and atherosclerosis) and microvascular (like retinopathy, neuropathy, and nephropathy) complications of the disease [6]. The increasing incidences of type 2 diabetes and their consequences in terms of cardiovascular morbidity and mortality represent a considerable public health problem [12]. Therefore, identification of the molecular targets of the transducers critically involved in the control of glucose and lipid homeostasis is crucial for developing new therapeutic agents for the treatment of metabolic syndrome. IV. Receptors Metabolic nuclear receptors (NR) are particularly attractive target molecules, since they have been found to play a central role in maintaining cellular and whole-body glucose and lipid homeostasis. Among these receptors, special attention has been paid for more than a decade to the members of the peroxisome proliferator-activated receptor (PPAR) family [13]. V. PPARs PPARs belong to the nuclear hormone receptor family, which is defined as transcriptional factors that are activated by the binding of ligands to their ligand-binding domains (LBDs) [14]. There are 3 PPAR subtypes namely (PPAR α [NR1C1], PPAR β [NR1C2], and PPAR γ [NR1C3] [15], which display different tissue distribution pattern and distinct pharmacological profiles [16] but they share similar three dimensional structure within LBDs. Thus ligands that simultaneously activate two or all of the PPARs could be potent candidates in terms of drugs for the treatment of abnormal metabolic homeostasis. PPAR α is mostly expressed in the tissues involved in lipid oxidation, such as liver, kidney, skeletal, cardiac muscle, and adrenal glands [17]. PPAR γ is expressed in adipose tissue, macrophages, and vascular smooth muscles [18]. PPARγ was first identified as a master regulator of adipocyte differentiation, but more recent molecular-biological studies have indicated that PPARγ activation is also linked to the expression of many important genes that affect energy metabolism, such as the TNF-α, leptin, and adiponectin genes [19]. Costs of synthetic drugs have escalated these days in leaps and bounds. In this situation, development of cost-effective treatment and indigenously engineered remedies become extremely essential. Chemometric modelling, which is a part of an enormous field of cheminformatics technology, has now become very popular among the researchers and the pharmaceutical industries to identify the drug targets via computational tools. Structural activity relationship (SAR) which is a statistical technique capable of analyzing screening datasets and deriving predictive models of biologically interested activity, molecular dynamics and molecular docking are already proved vital chemometric techniques to optimize the drug candidates [20]. The chemometric techniques can be used to identify important features necessary for a compound to behave as activator of PPAR-α and PPAR-γ receptors while such studies with PPAR-β/δ reveals that lead compound shows very weak PPAR-β/δ transactivation activity [21]. VI. α-GLUCOSIDASE α-Glucosidase (EC 3.2.1.20) is an enzyme responsible for catalytic cleavage of a glycosidic bond [22]. αGlucosidase (EC 3.2.1.20) enzyme can be an important strategy in the management of postprandial blood glucose level in type-2 diabetic patients and borderline patients as it is involved in digestion of carbohydrates significantly decrease the postprandial increase of blood glucose after a mixed carbohydrate diet [23]. In small intestine the intestinal α-glucosidase hydrolyzes complex carbohydrates to glucose and monosaccharide. The reduction in rate of digestion of carbohydrates can be achieved by inhibiting these enzyme systems [24]. In diabetics the short term effect of enzyme inhibitor drug therapies decreases high blood glucose levels [25]. α-Glucosidase has drawn a special interest of the pharmaceutical research community because it shows the inhibition of its catalytic activity in earlier studies and resulted in the decrease in postprandial blood glucose level and retardation of glucose absorption [26]. α-Glucosidase inhibitors do not cause hypoglycemic events or other life-threatening events, even at overdoses, and cause no weight gain [27]. Glucosidase inhibitors are highly useful for medical therapies, such as diabetes, obesity, hyperlipoproteinemia, cancer and HIV and hence are the potential bio-tools [28]. It has also been observed that α-Glucosidase inhibitors block viral infections and proliferation in HIV-infections [29], [30]. VI. AMPK-α Adenosine monophosphate -activated protein kinase (AMPK) is a key sensor and regulator of intracellular and whole-body energy metabolism. AMPK is composed by a catalytic α subunit and two regulatory subunits, β and γ. These subunits are expressed in various tissues and sub cellular locations and include multiple isoforms (α1, α2; β1, β2; γ1, γ2, γ3) [31], [32]. Cellular energy regulation and maintenance of the energy balance in the whole body is the major function of AMPK. AMPK stimulates ATP generation, glucose uptake and fatty acid oxidation upon activation, while it simultaneously inhibits the syntheses of hepatic triacylglycerol, cholesterol, protein and glycogen and down-regulates the ATP-consuming anabolic pathways [33]-[37]. AMPK- α plays a

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central role in the regulation of body weight, systemic glucose homeostasis, lipid metabolism, and mitochondrial biogenesis [38], [39], due to which the activation of AMPK would be a promising therapeutics against type 2 diabetes as well as other metabolic syndromes [40]. Two first-class antidiabetic drugs, biguanides and thiazolidinediones, were proved to be related to the AMPK activation [41], [42]. VIII. Drugs for Treatment of Type II Diabetes Mellitus Oral hypoglycemic drugs: Sulfonylureas which include tolbutamide, chlorpropamide, glibenclamide, glipizide, acetohexamide, gliclazide and tolazamide [43]; Biguanides which includes metformin and Phenformin [44]; and other miscellaneous drugs such as Acarbose and guar gum are most common oral hypoglycemic drugs. Other drugs for NIDDM and their corresponding targets: The other treatment for NIDDM include insulin sensitizers, drugs which reduce resistance of tissues towards insulin by interaction with the PPAR, a nuclear receptor regulating genes involved in metabolism of lipid. The cause of insulin sensitivity is the result of decreased production of non-esterified fatty acids. The non-esterified fatty acids have the capacity to potentiate the effect of endogenous insulin. Troglitazone, which belongs to the thiazolidinedione group, is used in the treatment of T2DM. A benzoic acid derivative Repaglinide, has also been used in T2DM patients as it stimulates insulin production at meal times [45]. Thiazolidinediones: The thiazolidinediones also known as glitazones were introduced in the late 1990s [46]. Glitazones are a class of medications that activates PPARs and are used in the treatment of T2DM. Αlpha glucosidase inhibitors: The introduction of drugs which can inhibit the enzymes responsible for breakdown of carbohydrates in intestine is an alternative approach for the treatment of overweight patients with NIDDM. Acarbose is a sugar that competitively inhibits α-glucosidase enzymes, as a result of which, dietary carbohydrates are poorly absorbed, and the postprandial rise in blood glucose is reduced. Undigested starch is broken down by fermentation when it enters the large instestine. Dosage needs careful adjustment to avoid the side effects such as abdominal discomfort, flatulence and diarrhea. Since it is mainly inactivated in the gut, very little acarbose enters the circulation, but liver dysfunction may rarely occur with high doses [47]. IX. Use of Medicinal Plants for Hypoglycemic Agents Oral hypoglycemics, dietary modification, and insulin, are the currently available therapeutic options for NIDDM, which have limitations of their own [48]. Many herbal medicines and natural products have been recommended for the treatment of NIDDM. The present paper reviews medicinal plants that have shown experimental or clinical antidiabetic activity and that have been used in traditional systems of medicine. Table I Shows the medicinal plants used as hypoglycemic agents. Name

Family

Part Used Flower bud.

Chemical Constituent Mucilage, phytosterol, dihydride alcohol, tannin, and faradial.

Tussilago farfara

Asteraceae

Prinsepia utilis Royle

Rosaceae

Aerial parts.

Triterpenoids, pentacyclic.

Oil from seeds is rubifacient and is applied locally in rheumatism.

Anti-inflammatory, anti-arthritic and hypoglycaemic.

Ricinus communis

Euphorbiaceae

Root, leave, oil.

Ricinolein, ricin, flavonoids, ricinolic acid, sodium ricinoleate, tristearin.

Constipation, pain and inflammation.

Anti-inflammatory, hypoglycaemic and laxative.

Aloe vera

Xanthorrhoeaeceae

Leaf Gel.

Bacterial infections and Inflammation, wounds.

Anti-inflammatory, emmengogue, emollient, and antibacterial.

Crataeva

Capparidaceae

Leaves.

Anthraquinone glycosides, free anthaquinones, enzymes, antibiotic principles,resins, glucomannan, steroids, organic acids, amino acids, salicylic acid and cinnamic acid, essential oil. Tannin and saponin.

Diabetes mellitus.

Hypoglycemic.

Medicinal Use Flower bud of Tussilago farfara is useful in T2DM

Pharmacological Activity Anti-inflammatory, anti-spasmodic and hypoglycaemic.

Study α-Glucosidase inhibitory effect by the flower buds of Tussilago farfara [49]. Hypoglyceminc effect of flavonoids from Prinsepia utilis on alloxaninduced diabetic mice [50]. Antidiabetic activity of 50% ethanolic extract of Ricinus communis and its purified fractions [51]. Hypoglycemic effect of Aloe vera gel on streptozotocininduced diabetes in experimental rats [52].

Antidiabetic

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Hyssopus officinalis

Lamiaceae

Leaves.

Glycosides, essential oil, sugar, tannins, resins, fats,mucilage, flavonoid glycoside.

Trigonella foenumgraecum L

Fabaceae

Leaves.

Trigonelline, glycosides,flavonoid, saponin, fenugreekine, ascorbic acid.

Smilax chinensis

Liliaceae

Leaves.

Salvadora oleoides Decne

Salvadoraceae

Fruit, root, seed.

Beta sitosterol, oil, diosgenin, smilacin, parallin, sarsaponin resin, tannin, starch, gum, sarsapogenin, sapogenins, and saponins. Ethanolic Extract.

Acacia nilotica

Fabaceae

Wood, gum and leave.

Aegle marmelos

Rutaceae

Fruits, leaves.

-----

Momordica balsamina L.

Cucurbitacea

Fruit and seeds.

Vitamin C and momorcidin.

Psidium guajava L.

Myrtaceae

Leaves, fruit.

Syzygium cumini

Myrtaceae

Leave, bark, fruit, nut.

activity of Crateva nurvala stem bark extracts in alloxan-induced diabetic rats [53]. Inhibitory effect Hyssopus officinalis extracts on intestinal alphaglucosidase activity [54].

Abdominal pain, insomnia, constipation, respiratory tract infections, viral infections and gastrointestinal Disorders. T2DM, respiratory tract infections, swelling, body pain, stomach pain, piles, dandruff, baldness, breast pain, lungs infection, ulcer and diarrhea. Inflammation, cancer and T2DM.

Antispasmodic, expectorant, sedative, antiviral, astringent, carminative, diaphoretic, tonic and stomachic. Anti-inflammatory, tonic and hypoglycemic.

Anti-inflammatory and anti-diabetic.

Antidiabetic activity of Smilax chinensis in alloxan induced diabetic rats [56].

Pyorrhea.

Anti-anemic, anti-septic and laxative.

Treatment of T2DM.

Astringent and hypoglycemic.

The hypoglycemic activity of ethanolic extract of Salvadora oleoides [57]. Indusyunic Medicine [58].

Chronic constipation, dysentery, piles, hyperacidity, abdominal pain and T2DM. T2DM, gas flatulence, obesity, trouble, constipation, boils and pimples.

Mucilaginous, antidysentric and antidiabetic.

A study conducted on normal and diabetic rats[59].

Hypoglycemic, blood purifier and stomachic.

Ethanolic extract.

T2DM and intestinal worms.

Tonic and antidiabetic.

Myrcetin, glucosides, kaemferol, isoquercetin, ellagic acid, anthocyanins, jambolin, jambosine alakloids,

Fever, motion, painful swellings, vomiting, anemia, and T2DM.

Anti-inflammatory and hypoglycemic.

Anti-diabetic and hypoglycaemic effects of Momordica charantia (bitter melon): a mini review [60]. Effect of guava (Psidium guajava Linn.) leaf soluble solids on glucose metabolism in type 2 diabetic rats [61]. Syzygium cumini extract decrease adenosine deaminase, 5â&#x20AC;&#x2122;nucleotidase activities

Gum arabic, catechin, tannins, mucilage, magnesium, potassium, calcium, arabic acid, malic acid and flavonoid compounds.

Research and development of indigenous drugs [55].

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Curcuma longa

Zingiberaceae

Sesamum indicum

Pedaliaceae

Ziziphus mauritiana Lam.

Rhamnaceae

Allium cepa

Ficus bengalensis

Tubers and rhizomes.

and oxidative damage in platelets of diabetic patients [62].

Essential oil, guaiane, turmerol, curcumin, sesquiterpenic ketone, bisabolane, curlone, tumerone, arturmerone, and zingiberone. Molybdenum, cobalt, iodine, iron, zinc, calcium methionine, thiamine, niacin, carbohydrates, tryptophan, lecithin, sesamin, sesamolin, phytosterol, and sitosterol.

Whooping cough, chronic skin disorders, asthma, bronchitis, scabies, irritation, wounds, strokes, bruises, eczema, prurigo and ringworm.

Expectorant, hypoglycemic and blood purifier.

Curcumin and turmeric delay streptozotocininduced diabetic cataract in Rats [63].

Cough, inflammations, sexual debility, asthma, thorax complaints, and bleeding piles.

Aphrodisiac, hypoglycemic and anti-inflammatory.

Hypoglycemic effect of a hotwater extract from defatted sesame (Sesamum indicum L.) seed on the blood glucose level in genetically diabetic KK-Ay mice [64].

Leaves, fruit.

Ethanol seed Extract..

T2DM.

Hypoglycemic.

Amaryllidaceae

Leaves and bulbs.

Phytoncides, quercetin, vitamins, allicin, flavonoids, allylpropyl disulfide, essential oil, scordine and fatty oil.

Ear pains, skin diseases and flatulence.

Aphrodisiac and hypoglycemic.

Hypoglycemic activity of Ziziphus mauritiana aqueous ethanol seed extract in alloxan-induced diabetic mice [65]. Hypoglycaemic effects of onion, Allium cepa Linn. on diabetes mellitus - a preliminary report [66].

Urticaceae

Latex, buds, bark, fruits, roots, root and aerial roots.

Triterpine, tannins, friedelin, sitosterol, tigilic acid, quercetin, rutin, waxes, albuminoids and carbohydrates.

T2DM.

Hypoglycemic.

---------

Antidiabetic effect of Ficus bengalensis aerial roots in experimental animals [67].

X. Conclusion Unlike many other diseases, treatment exists for Type 2 diabetes. Better understanding of Receptor structure and active molecules has helped to develop newer, effective drugs for the treatment. Medicinal Plants are also seemed to be effective in curing T2DM to a great extent. Acknowledgement I would like to acknowledge the University Grant Commission (UGC) for providing me Rajiv Gandhi National fellowship (RGNF). References [1]. [2]. [3].

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Database Management - Inline Queries vs. Stored Proceduresâ&#x20AC;&#x201C;An Extended Analysis Er. Shobhit Gupta [RHCSA, RHCE, MCTS] Assistant Professor, Department of Computer Science & Engineering, Kurukshetra Institute of Technology & Management, Kurukshetra, Haryana, INDIA ________________________________________________________________________________________ Abstract: Computer based applications have become an inherent part of every important sector in our life like office work, gaming, research or any other field. The applications can be system/application software providing domain specific services. Not even a single application can be thought of without a direct or indirect database. It is very important to analyse and choose the best fit database management approach for efficient results. Query management is one important aspect of database management. Majorly, two approaches are used for this purpose: Inline Queries and Stored Procedures. This paper provides extended analysis of performance of both approaches during different operations followed with a recommended one in conclusion. Keywords: Database Management, DBMS, SQL, Inline Query, Stored Procedures ______________________________________________________________________________________ I. Introduction Computer based applications have become an inherent part of every important sector in our life like office work, gaming, research or any other field. The applications can be system/application software providing domain specific services. Not even a single application can be thought of without a direct or indirect database. Various types of database management systems have been there in market since the inception of computing. Database systems evolved from flat files to tables, relational databases, data marts, data warehouses and so on. It is very important to analyse and choose the best fit database management approach for efficient results as required by an organization. Most of the database systems are dependent on queries, general taken as SQL Queries for most of the systems. SQL queries are predefined set of statements which are fired on a database system to perform some operations like Searching, Insertion, Modifications, etc. So, Query management becomes one important aspect of database management. Majorly, two approaches are used for this purpose: Inline Queries and Stored Procedures. This paper provides extended analysis of performance of both approaches during different operations followed with a recommended one in conclusion. II. Literature Survey Thousands of companies depend on the accurate recording, updating and tracking of their data on a minute-tominute basis. Employees use this data to complete accounting reports, calculate sales estimates and invoice customers. The workers access this data through a computerized database. A proven method to manage the relationships between the various database elements is the use of a relational database management system [1]. A database management system is important because it manages data efficiently and allows users to perform multiple tasks with ease. A database management system stores, organizes and manages a large amount of information within a single software application. Use of this system increases efficiency of business operations and reduces overall costs [2]. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Some common relational database management systems that use SQL are: Oracle, Sybase, Microsoft SQL Server, Access, Ingres, etc. Although most database systems use SQL, most of them also have their own additional proprietary extensions that are usually only used on their system [3]. In general, SQL queries are written within the source code of an application and called from within the application. When some data is to be passed in a query, with the information is passed directly through quotes or it is provided through query parameters. In former approach, a lot of security risks exist, SQL Injection attack to be the most popular one. Through parameterized queries, however, security risks are minimized but better approaches were still required for security and performance. SQL Stored Procedures then came into existence in which the code snippet of SQL Query is written and kept within DBMS environment rather than source code. Stored procedures offer several advantages over embedding queries in your Graphical User Interface (GUI). Your first thought may be: "Why tolerate the added development overhead?" After seeing the advantages, you may change your mind [4]. While many programmers still feel that Inline Queries are better. Following section will provide analytical comparison of both approaches. III. Detailed Work and Methodology In order to get an analytical comparison report, a dedicated web server along with database management system was deployed in Microsoft Windows 2008 Server. The database was created holding more than 4 Crore records

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in one table and 25 lakh records in another and a web application was also developed which implements both approaches in question and queries through this database to provide analytical information.

Fig. 1.1 Web Application developed

Fig 1.2 SQL Query within Source Code

Fig. 1.3 SQL query inside Stored Procedure in Database Environment A. Execution Time – Update Table An update query was fired on the table with 25lakh records. Total number of affected records was counted through COUNT function of SQL using Inline queries and Stored Procedures from 8 remote machines. Following observations were drawn:

Fig. 1.4 Time (in seconds) to update Records using Inline Queries Mean Time Taken by 8 machines on Internet (Inline): 74.5 seconds

Fig. 1.5 Time (in seconds) to update Records using Stored Procedures Mean Time Taken by 8 machines on Internet (SP): 61.87 seconds B. Execution Time – INNER JOIN An Inner Join was fired on both tables such that out of 4 crore records, the 25 lakh record matching with other table are returned. Total number of affected records was counted through COUNT function of SQL using Inline queries and Stored Procedures from 8 remote machines.

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Fig. 1.6 Time (in seconds) to complete an Inner Join using Inline Queries Mean Time Taken by 8 machines on Internet (Inline): 203.5 seconds

Fig. 1.7 Time (in seconds) to complete an Inner Join using Stored Procedures Mean Time Taken by 8 machines on Internet (SP): 150.25 seconds C. Execution Time â&#x20AC;&#x201C; Search with Pattern Matching using LIKE and wildcards A search query was fired on the table having of 4 crore records with LIKE clause using the Wildcard: %lh%. Total number of affected records was counted through COUNT function of SQL using Inline queries and Stored Procedures from 8 remote machines.

Fig. 1.8 Time (in seconds) to complete Wildcard based Pattern Search Operation using Inline Queries Mean Time Taken by 8 machines on Internet (Inline): 288.87 seconds

Fig. 1.9 Time (in seconds) to complete Wildcard based Pattern Search Operation using Stored Procedures Mean Time Taken by 8 machines on Internet (SP): 98.125 seconds

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D. Consumption of Resources on Server SQL Query was fired on this database using Inline queries and Stored Procedures over Internet from 8 remote Computers connected through Internet over Broadband. Stored Procedures use precompiled form of its database logic while inline queries are compiled and executed each time they are fired. Also, stored procedures support the feature of information caching for better performance. Following CPU Load observations from a Dual Core CPU were drawn:

Fig. 1.10 CPU Load – Update Table by Inline on 8 remote machines over Internet

Fig. 1.11 CPU Load – Update Table by Stored Procedures on 8 remote machines over Internet

Fig. 1.12 CPU Load - Inner Join by Inline on 8 remote machines over Internet

Fig. 1.13 CPU Load - Inner Join by SP on 8 remote machines over Internet

Fig. 1.14 CPU Load (100%) – Pattern Search by Inline on 8 remote machines over Internet

Fig. 1.15 CPU Load – Pattern Search by SP on 8 remote machines over Internet

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E. Sr. No. 1 2

Observations Summary Task Records counted on local system [5] 1 lakh Records fetched on local system [5]

Records counted on Intranet (Mean of two machines) [5]

1 lakh Records fetched over Intranet (Mean of two machines) [5]

5 6 7 8 9

Time Taken (in seconds) Inline Queries Stored Procedures 89 87 95 93 91

86.5

72.5

Records Counted over Internet (Mean of 8 machines) [5]

81.12

64.87

1 lakh Records fetched Internet (Mean of 8 machines) [5] Update Records over Internet (Mean of 8 machines) Inner Join Over Internet (Mean of 8 machines)

225.87 74.5 203.5

95.5 61.87 150.25

Wildcard based Pattern Searchig over Internet (Mean of 8 machines)

288.87

98.125

Table 1.1: Observation Summary

Fig. 1.16 Observations Summary - Graphical IV. Conclusion So, from the above details statistics and summary chart, it is easy to draw the conclusion that under all parameters and circumstances, using Stored Procedures are definitely a superior approach, especially when load increases in terms of number of users and amount of information required. References [1] [2] [3] [4] [5]

Gerald Hanks, Demand Media, "Why Are Database Management Systems Important to Business Organizations?", AzCentral. Ask.com Sqlcourse.com Parthasarathy Mandayam, "Why use stored procedures?", Techtarget, Feb 2005 Shobhit Gupta, “Inline Queries vs Stored Procedures – An Analytical Comparison”, (Accepted in - A Research Journal of Humanities, Commerce & Sciences of DAV College Ambala), ISSN: 2348-0300, 2015

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net An Analytical Study of Weakly Nonlinear Convection in a Horizontal Mushy Layer Prof. Dr. P. K. Srimani R & D Director (DSI), Former Chairman, B.U. Bangalore, Karnataka, INDIA Mr. R. Parthasarathi Prof. and HOD, Dept. Mathematics, Jain University – CMS, Bangalore, Karnataka, INDIA _______________________________________________________________________________________ Abstract: The present analytical study deals with the problem of weakly nonlinear convective instability in a horizontal mushy layer under suitable assumptions and approximations. By applying a modified perturbation technique, the basic state solutions, solutions to the first and the second orders are determined. The influence of the governing parameters on the profiles of total Rayleigh number, velocity and local solid-fraction are studied. The computations are done for a feasible set of values of the parameters. An excellent correlation is obtained between the present and the available results for the limiting cases. Keywords: Mushy layer, Convection, Chimney formation, local solid fraction, solvability condition. ______________________________________________________________________________________

I. Introduction During the solidification of a binary or multicomponent melt, a region called ‘mush layer’ in which the solid and the liquid phases co-exist, will be formed due to the morphological instability of a solid-liquid interface[1].Thus a mushy layer can be defined as a reactive porous two – phase medium that comprises the solid-matrix as well as the residual liquid. The understanding of the complex interactions between the flow of melt and the solidification is interesting and is of great importance since the transfer of heat and mass associated with the fluid flow has a profound influence on the process of solidification and becomes responsible for the chimney formation which causes imperfections in the resulting solid. The interesting phenomenon that occurs within the solidifying melt is the formation of chimneys which are narrow dendrite-free cylindrical regions or narrow cylindrical regions of zero solid fraction and are very much similar to the imperfections called ‘freckles’ that appear in the casting of metallic alloys [2][3]. The formation of chimneys during the solidification of a binary or a multicomponent alloy constitutes three stages viz., finger, plume and chimney convections[4][5].Actually, during the solidification process the solidification front or the interface between the solid and the liquid becomes highly dendritic due to the morphological instability. As a consequence there will be a formation of a region called ‘mushy layer’ consisting of a partially solidified melt, the dendritic structure of which is quite complex [1]. Then the system becomes unstable due to the density gradient that results from the rejected materials and there will be a transition to convection. Further, as a result of the interaction of the thermal fields and the generated convective motions, chimneys which are responsible for the imperfections in the resulting solid will be formed [2][6][7][8][9].The review article by [10] discusses the striking fluid-mechanical events that take place during the solidification process. Especially in metallurgy, dynamics of sea and geophysics, the mechanism and the process of formation of chimneys which spoil the quality, physical properties and the internal structure of the resulting solid, are important study areas [11][12][13]. In the past three decades the study pertaining to the development of different convective models and analysis for the case of convection in mushy layers has attracted researchers [14][8]. The works connected with the formulation of the governing equations in the study of convection in mush layers, the development of mathematical models and the solution procedure are available [15][16]. The mathematical models describing the characteristics of mushy layer and the related phenomena are exclusively based on the key feature that the length scale of the internal boundaries are extremely small when compared to the macroscopic dimensions of the mushy layer. Linear and weakly nonlinear convective instability in a mushy layer has been studied by quite a number of researchers under different types of assumptions and approximations [17][6][18][19]20]21][22]. Quite a number of works on convective flow in a mushy layer is available. A detailed review on convection in mushy layers is given by[6][22]. Recently [23][24][25] have applied weakly nonlinear evolution approach to study two-dimensional convective motions in a mushy layer with impermeable solidification front under different situations. Finally [26] have studied numerically the effects of inertia on connection in a mushy layer with constant permeability. Analytically [27] have studied the effects of inertia on convection in a passive mushy layer. The main objective behind these studies is to study the history of the solidification process that could yield pure solid and facilitate the suppression of the freckles which have catastrophic effects on the internal structure of the resulting solid. A thorough survey of the

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literature pertaining to the subject reveals that no in depth analytical work is available for the case of convection in a mushy layer with and without constraints. Therefore the present analytical work is carried out to study the effects of large Stefan number, Concentration ratio and the permeability function on the total Rayleigh number, velocity and local solid fraction profiles in a mushy layer near eutectic temperature. The boundaries are assumed to be impervious so that the Darcy’s equation is valid. The paper is organised as follows: Section 1 deals with a brief introduction to the study together with the literature survey. Sections 2, discusses the mathematical formulation. In sections 3 and 4, the basic state, the linear stability and weakly nonlinear analyses are presented. Finally, section 5, presents the results and discussions of the study. II. Mathematical formulation The physical configuration consists of a horizontal mushy layer formed during the solidification of a binary alloy as shown in fig 1. The process of uniform cooling from below of the system results in the upward advancement of the solid – mush and mush – liquid interface with a constant solidification speed V0.In otherwords the mushy layer is sandwiched between the solid and the liquid regions. The study is carried out in a moving frame of reference.

Figure 1. The schematic diagram of the physical system where the bottom boundary of the mushy layer z = 0 is kept at the eutectic temperature T = T E , while the top boundary z=d is kept at the liquidus temperature T L (C0 ) and the mixture of the composition C0 is supplied through the surface. Following are the assumptions made for the study: 1. The top and the bottom boundaries of the mushy layer are assumed to be isothermal non-deformable and impermeable to the fluid flow, so that the mushy layer is kept dynamically isolated from the other components of the system [18]. 2. The solidification front(the frame of reference) is moving upwards with a velocity V0 relative to the solid formed and the solid dendrites within the mushy layer. This makes the basic state to be steady. 3. The temperature T and the composition C of the liquid in the mushy layer are required to satisfy a linear liquidus relationship T = T L(C) = TL(C0) +  (C – C0), Where  is a constant. The liquid is assumed to be Newtonian with a linearized equation of state = [1 + (C- C0)] where L is the density of the liquid, is a reference density, = *–  , *and *are constant expansion coefficients for heat and solute respectively. 4. First following [18], we study a limit in which the thickness of the mushy layer is much less than the diffusion length scale by letting 1. 5. Sec ondly we assume that the compositional ratio is large by writing CR = with CR = 0(1) as 0 which corresponds to the near eutectic approximation introduced by [28]. 6. we consider the limit in which the Stefan number is large[29] by taking S = S/ with S = O(1) as 0 which corresponds to the situation in which the latent heat liberated during the local phase change is much larger than the heat associated with the typical variations of temperature across the mushy layer. Note that the particular scaling allows the destabilisation of the system to an oscillatory mode of convection [20][29]. −1

7. However, that a key implication of the near-eutectic approximation(C=O(δ )) is that the solid fraction is small, and hence the permeability is uniform to the lowest order. As a consequence, we follow [18] and expand the permeability in terms of the small solid fraction Φ, 2

K (Φ)=1+ K1Φ + K2Φ + ··· (1) where, on physical grounds, we demand that K 1, K2, etc. are non-negative. Under the above assumptions and approximations the governing equations of the systems are Conservation of momentum, Conservation of mass, Conservation of heat and solute

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 

= -∇ P –( ρ – ρ0)g

 k

(2)

∇. q = 0 +

(3)

q ∇ T = k ∇2 T +

.

lh 

(4)

q .∇ C =(C – C0 )

(5)

The above set of equations are cast in the dimensionless perturbed form by using the following scale viz., ,∆T,

, for the variables velocity, temperature, time, pressure and length respectively :

2  q ∇θ = ∇ θ,  (∂t − ∂z ) ((1 − Φ) θ + C Φ)+ q .∇θ =0,  K(Φ) q = −∇p − Ra θ k,

(∂t − ∂z )(θ−SΦ)+

 ∇. q =0.

(6) (7) (8) (9)

Equation (8) in the component form is given by K(Φ)u =-

(10a)

K(Φ)v =-

(10b)

K(Φ)w = - R Θ Next, we eliminate the pressure in the above set of equations by applying the following transformation: To start with, the application of the transformation [ + ] - ∇12(10c)

(10c)

yields ∇2 (Kw) - ( u.∇K) = – R.∇12 Θ Next by applying the transformation, [ + ] - ∇12 (10a) we get ∇2(Ku) - ( u.∇K) = + R. Finally by applying the transformation, [ + ] - ∇12 (10b) we get, ∇2(Kv) -

(u.∇K) = R.

(11) (11a) (12) (12b) (13) (13a)

The associated boundary conditions are θ = −1, w =0 @ z =0, θ = 0, w = 0, Φ =0 @ z = δ. (14) The above boundary conditions correspond to impermeable rigid boundaries of the mushy layer. The lower plate, between the solid and the mush, is maintained at the eutectic temperature T E , while the upper boundary between the liquid and the mush (that is, at zero solid fraction Φ), is maintained at the far-ﬁeld liquidus temperature TL(C0 ).The porous medium is such that Darcy’s law holds good. Dimensionless parameters The function K(Φ) appearing in equation (8) measures the variation of permeability with respect to some zero solid-fraction permeability π(0), assumed to be ﬁnite, such that π K(Φ) = (15) π Φ

The dimensionless parameters appearing in (6-8) are the Stefan number S = the concentration ratio

C =

the Rayleigh numberR =

and (Rayleigh number)

(16)

where

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= 1-  Local solid fraction,  – Local liquid fraction ,P – Dynamic pressure, π = π function of the local solid fraction ,  :Dynamic viscosity, t, T,

 , lh , 

, permeability is a

are time, temperature, thermal

diffusivity, specific heat, latent heat/unit mass, Cs: Composition of the solid phase ,C0: Composition of the liquid phase , ,

densities , g = (0,0,g) acceleration due to gravity, π

: The reference permeability, q =

u i + vj +w k , q is the Darcy velocity vector and (u,v,w) are the horizontal and vertical components of q , i,

,k : unit vectors along the x, y and z axes. d : mushy layer thickness, : Expansion coefficient, T∞: Far-field temperature, CE : Eutectic composition. A fifth dimensionless parameter, appearing in the study is the dimensionless mushy layer thickness δ = d/ (ƙV0) , appears in the boundary conditions. Before analysing the linear stability of the system the basic state analysis is carried out as follows: III. Basic state Analysis The steady motionless basic state system is considered here where each of the corresponding dependent variables is designated by a subscript “B”. The basic state variables are assumed to be functions of z only. θ = θB (z) + ε ˆ (x,y,z,t)

ˆ (x,y,z,t) Φ = ΦB(z) + ε  q = 0 + ε qˆ (x,y,z,t) P = PB(Z) + εP(x,y,z,t) K = Kb (ΦB) + εK(Φ) (17) where the perturbation parameter ε << 1 and the perturbed quantities can vary with respect to spatial and time variables. Using (17) in (18) we rescale the variables as 2

(∂t–δ∂z) (θ- S Φ)+ δ ( q ·∇)θ = ∇ θ,

(18)

(∂t–δ∂z) ((1- Φ) θ+ C Φ)+ δ( q ·∇)θ = 0

(19)



ΔK (Φ) q = −∇p − Ra θ kˆ , (20) ∇· q =0. (21) Equations (18 -20) allow a motionless steady basic state solution, depending only on the vertical position.By setting q =0 and ∂t = 0,we get 2

−δ D θB+ S DΦB = ∇ θB,

(22)

−δ D θB + δ D(θB.ΦB) - C D θB

(23)

0 = -D PB –R ƟB From (22 -24), we have the following equations:

(24)

δ D θB (ΦB -1) + δ DΦB (θB - ) = 0 δ

δ (1 - ΦB) D θB + DΦB ( C – δ θB ) = 0 D2 θB + δ D θB -S D ΦB =0 D PB + R θB =0 Boundary conditions are θB = -1 @ z = 0, θB = 0, ΦB = 0 @ z = 1

(25) 26) (27) (28)

On multiplying (27)by ( C - δ θB ) and (26) by S and adding , we get ( C - δ θB) (D 2 θB+ δ D θB - S DΦB) + S ( C (1- ΦB) D θB + ( C - δ θB )DΦB ) = 0 Equations (26) - (28) are solved asymptotically by applying the following expansions θB = θB 0 + δθB1+-----------------ΦB = δΦB0 +δ 2ΦB1 + ---------------On substituting (30) in (29) and on equating the like powers of δ 0 , we get D 2 θB0 = 0 The solution of (31) is given by applying the boundary conditions θB0 = -1 @ z = 0 and θB0 = 0 @ z = 1 θB0 = Z – 1 Now, on equating the like powers ofδ  C D2ƟB1 -ƟB0 D2ƟB0+( C + S ) DƟB0 = 0

(29)

(30) (31) (31a) (32)

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P.K.Srimani et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 11(3), December 2014February 2015, pp. 239-246 2 2 C D ƟB1 + ( C + S ) = 0 (since D ƟB0 = 0).

The solution is given by

ƟB1 = -

(z2 – z ) =>D ƟB1 = (2z – 1 ) => D2 ƟB1 =Ω On equating the like powers ofδ 2 : 2 2 2 C D θB2 -θB0. D θB1 -θB1. D θB0 + ( C + S )D θB1 – [θB0 + S ΦB0] D θB0 = 0 Substituting, and solving, we get

(33)

 θB2 =

(35)

[ - ] + [ - ]+[1 + ]z -1 on multiplying (27) by ( 1 – ΦB) and on subtracting from (26), we get (1 – ΦB) D2 θB –[S( 1 – ΦB) + ( C - δ θB )] DΦB= 0

(34)

(36)

Collecting the coefficients of δ0, and solving we get ΦB0 = [z -1]

(37)

Again by equating the like powers o (δ ): we get, D2 θB2 - ΦB0 D 2 θB1 - ΦB1 D 2 θB0 – ( C + S ) D ΦB1 – [ S ΦB0. + C ΦB0.] D ΦB0 = 0 On solving we get, 2

ΦB1 = -

(z -1) C  + C Finally by substituting the above values in (30), we get θB =(z-1) –δ =-δ

(z2 – z )+δ 2 [

[z -1]+ δ 2[-

 C

[ - ]+

[

(38) (39)

]+[1 -

(z -1)2]+---------

]z -1]+ ---------

(40) ΦB (41)

IV. Weakly nonlinear analysis In this section we first perform the linear stability analysis of the system to find the critical conditions and the growth rate σ and next consider a finite – amplitude perturbation expansion of the equations in order to predict the nonlinear effects on the system. For this purpose we consider the expansions of the perturbation quantities in the form θ = (θ00 + δθ01 + ---------) + ε (θ10 + δθ11 + -----------) + ε2 (θ20 + δθ21 + ----- ---) ΩΦ = ( Φ00 + δ Φ01 + -----) + ε( Φ10 + δ Φ11 + -----) + ε2 ( Φ2(-1) + δ Φ20 + --------)

= ( q 00 + δ q 01 + ---------) + ε ( q 10 + δ q 11+ ---------) + ε2( q 20 + δ q 21 + -----) R = (R00 + δ R01 +--------) + ε (R10 + δ R11 +--------) + ε2 ((R20 + δ R21 +------) (42) From (42) it is observed that the equation is singular at the order of as δ> 0. In fact this is the contribution of the forcing term Φ00from the equation for Φat the order of 2.Therefore the balance is made by adding the term (1/δ)Φ2 - (1) at order of 2. Further,the asymptotic expansion of (42) is meaningful only when 2δ is very much less than 1. In order to study the stability characteristics of the problem, we substitute (42) into (6),(7),(11a),(12a) and (13a) and collecting the terms of order δ-1 and δ0, we get the following results: (43) 00= 0 S( 01 – D ) Φ 00 - R00 w00 + ∇2θ00 = 0 (44) ( 01 – D)C Φ00 + R00 W00 = 0 (45) ∇2 w00 – R00 θ00 = 0 (46) ∇2 u00 –ik R00 D θ00 = 0 (47) Let Ѳ00 = -fk Sin (πz) (48) From (43) - (47) and on solving, we get w00 =

(49)

u00 =

(50)

R00 =

(51)

Φ 00 =

[

+Cos(

)]

Next we consider the system of o(δ) : ∇2 01- R00 (w01+w00D ) - R01 w00+D – sD =0 D -– w01- (R00w00D + R01w00 ) + D -D

(52)

(53) (54)

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P.K.Srimani et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 11(3), December 2014February 2015, pp. 239-246

∇2 01 – R00 R01 + K1. ∇2 00. ) = K1D(D .w00) In (53) to (55), we eliminate W01and so that [(D2 – α 2 )2 (R00)2.Ω] R00 R01 . Ω + R00 K1 Ω (D2 – α 2)( ) - K1 R00 Ω D(D 2 2 2 2 (D – α ){ R00 w00D } Ω - (D – α ) R01 w00 Ω + (D2 – α 2 )D Ω - (D2 – α 2 )S D( =0 Multiplying the above equation by =

(55) )-

(56) and applying the solvability and orthogonality property, we obtain: R00 2 -

R00 +

R00

(57)

where Ω = 1 + On Substituting for R00 and on simplifying we get, R01 = [ ] R00

(58)

Further we have R = R00 + δ RO1 and RC = [1 + ( - ) ] , αc = π ;

(59)

V. Results and discussion In the present study, the analytical solutions for the basic state, which is necessary for the determination of the linearized as well as the weakly nonlinear systems are determined by setting σ = 0. Further, the zeroth order solutions w00 , u00, θ00 and Φ00 are analytically determined for the general case of variable permeability in convection in a horizontal mushy layer. Then considering the asymptotic expansions for the variables in terms of two parameters ε<< 1 and δ < 1 (the mushy layer thickness, the weakly nonlinear system of order δ0 , δ(1), δ(2) are obtained and R01 is determined from the inhomogenous system of differential equations by applying the solvability conditions. The growth rate ϭ01 is also determined in terms of the governing parameters. The graphs of total R = R00 + δ R01, w00(vertical velocity) and Φ00 (local solid fraction) are plotted for the experimental set [30] of values viz., K1 = 2.5,3.5, 4.5 ; C= 1,5;Ω =1.2,1.35556 and1.5 respectively in figs. (2 - 6). In fig. 2, R vs α is plotted, for K1 = 2.5,3.5, 4.5. It is found that R increases withK1 I.e., K1 has a stabilizing influence on the convective system. From figs 3 and 4 it is observed that total R decreases with the increase in the values of Ω and C. In otherwords, these parameters have a destabilizing influence on the system. In figs 5 and 6, the profiles of Φ00 and w00 are presented for values of C=0.5,1,2,4 and Ω =1.35556, 2,3, 4 respectively. The input values for z are 0.0 to 1.0. The profiles clearly indicate the reduction in the nonlinearity as the parameters C and Ω decrease i.e., the system exhibits the destabilizing nature as C and Ω increase. From the above results, it is evident that by a suitable choice of the governing parametersK1, C and Ω, it is possible to have a good control over the formation of chimneys which are responsible for the formation of ‘freckles’ causing imperfections in the resulting solid formed during the solidification process. The present analytical results are in excellent agreement with those of [28][20] in the limiting cases.

Fig. 2 Total R vs α for Ω= 1.2, 1.35556, 1.5

Fig. 3Total R vs α for K1 = 2.5, 3.5 , 4..5

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Fig. 4 Total R vs α for C = 1 and 5

Fig. 5 Φ00 vs α for C = 0.5, 1, 2, 4

Fig. 6 W00 vs α for Ω = 1.35556, 2, 3, 4 References [1] [2] [3] [4] [5] [6]

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Strategic Planning towards Effective Management of New Technology in Open Cast Mines Anand Pd. Sinha, Supriyo Roy Department of Management, Birla Institute of Technology - Mesra, Ranchi, Jharkhand, India ______________________________________________________________________________________ Abstract: Technology has become backbone of corporate sustainability after the pro-market reforms due to immense competition worldwide in prime sectors. Technology Management is a practice that entails classification, selection, adoption and exploitation of the technologies needed to maintain organization â&#x20AC;&#x2122;s current and future survival. Manufacturing sector though invested lot in technological part; is still lacking as per their expectation in terms of overall performance. Coal mining sector in India is one of the core sectors which are far behind comparing with global standards inspite of implementation and use of best technologies. Open cast coal mining industries warrants for state of the art technology which have two aspects: first is the selection of appropriate / suitable technology and second is the effective management of them. Present article highlights strategies and methods adopted in a coal mining industry for effective management of new technology and its implementation. It attempts to focus on effective management of new technology by identifying various factors required for the same in opencast coal mining industries. This research also directs to understand and assess the reasons for low productivity and suggest suitable remedial measures towards effective management during implementation of new technology. Key words: Technology Management, Opencast Coal Mining, Effective management of technology. _______________________________________________________________________________________ I. Introduction Technology is essentially a starting point for knowledge; is required for taking initiative and decision making. It provides new tools to deal with knowledge and a result will have far reaching implication on future decisionmaking process. With rising complexity and factor of globalization, technology has gained overriding objective in the fast changing competitive environment. In the contemporary, business environment needs latest technology is imperative for maintaining quality standards. Business house demand two aspect of technology: first one is the selection of appropriate / suitable technology and second one is effective management of the same. Suitable technology is indicative to be a good match between technology utilized and resources required for its optimal use. Simply taking up of new technologies is a difficult task due to innumerable alternatives available both indigenously as well as internationally. However, second aspect needs to be more focused to understand the effective management of technology for capitalizing it to the maximum extent. Researchers feel that the coal mining industries under government control lacks effective management of new technology and not able to justify the returns on investment. Technology cannot play itself but it can bring a change and has to be supported by appropriate interventions and an advanced human skill. There is no denying of the fact that the wrong choice of technology leads to dismal consequences affecting the overall health of the organization nonetheless the fact also lies in the effective management of technology. Indian coal industries have witnessed a series of technological changes but it is still struggling in extracting coal suitable for the domestic consumption rather depending on imported coal. Therefore, assessment and evaluation criteria with respect to its cost effectiveness, availability of raw material and skill availability are needed to be established. Indian open cast coal mining is far behind in global standards in spite of implementation and use of best technologies. The way in which internal planning and implementation processes are managed could greatly influence the outcome of new technology. Technology implementation and planning refers to extent how the organization has strategically designed the deployment of new technology(s) prior to its implementation. The processes incorporated within this design influence overall effectiveness of technology deployment and utilization. Throughout the implementation process, effective management means supporting the project team, selecting right technology and designing and providing appropriate training. This will ensure that new technology will complement existing processes and systems and will allow more productivity throughout. Present study highlights the development of technology in Indian open cast coal mining industries primarily. This study also explores general strategies and methods adopted in a Coal mining industry for effective management of technology and its implementation. For this, we follow survey based methodology followed by analyses. For domain for data collection, we take one of the largest public sector coal units in the state of Jharkhand; namely Central Coalfield Limited under Coal India Limited along with its seven Open cast mines and its designing part (named CMPDI). After pre testing, reliability and validity, data collected are put into

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statistical analysis and factors effecting management of technology are finally identified by using Factor Analysis in Statistical Package for Social Science (SPSS) platform. II. Technology Management - Literature support Technology comes from two Greek words: Techne (skill needed to make something) and loges (knowledge of something). Technology is knowledge of how something is made and is essentially a starting point for knowledge (Soloman, 1990). It provides new tools to deal with knowledge and a result will have far reaching implication on the future decision-making process. Other view relating to Technology consists to the branch of knowledge that deals with industrial arts, applied science and engineering related to processes, products, tools and techniques and systems employed in the creation of goods or services (Narayana, 2001). Rising complexity crisis and globalization factor, technology has gained overriding objective in today’s fast changing competitive environment. In the Contemporary, business environment needs latest technology is imperative for maintaining quality standards in order to remain in the business line. Organizational Success largely depends upon the efficient use and application of effective technology. Technology and its effective management are essential for overall success of any organization. Successful conceptualization and implementation of technology require coordination of a wide array of activities, information and expertise (Roy and Singh, 2014). Since business opportunities are time bounded; an organization needs to act quickly for availing the benefits of new and innovative technology in an efficient manner. These challenging developments in the business environment have heightened the need for effective management and control of procedure and technology. Thus, effective and efficient achievement of any goal at corporate or individual level requires a systematic and well planned process of decision-making. Effective management requires the setting of clear objective to perform effectively or efficiently (Bhalla, 1987).One of the major criteria for setting clear objectives is the overall audit of scope. Therefore objective should be such that it provides a clear direction to the people who have to contribute and perform for achievement. It is always desirable to have a participatory approach to set objectives. However management aspiration and expectations should be kept in view while adopting a participatory approach. Every organization which is working in a highly competitive environment aspires to excel by improving its effectiveness and maintain performance (Chaudhuri and Moulik, 1986). However performance in any organization depends upon dedicated and skillful team of human resources that makes it happen. Productivity refers to accomplishment of objectives through utilization of resources (such as capital, workforce, machinery, infrastructure etc.). It refers to relationship between inputs and outputs or efficiency with which organizational objectives are achieved. Another important factor is team effectiveness which refers to efficient achievement of well-defined set of objective. Successful organizations continuously keep working towards improving their team’s effectiveness by becoming responsive to the fast changing internal and external environment (David and Kirit, 2001). This task requires a diversity of skills and talents that is needed to be complemented amongst team members. Organized efforts in the field of technology management began 1950s’ onwards when R&D as well as modern management ideas were developed (this was a period characterized by plentiful resources to R&D). Management of innovation started functioning during 1970’s; there was an interest in the entire corporate world to understand innovation and its proper application. During twentieth century, grow of technology shows opposite direction as a result of impact of global competition and economic crisis of United States. Management of technology focuses on the principles of strategy and organization involved in technology choices, guided by the purpose of creating value for investors (Narayana, 2001). It is an interdisciplinary field that integrates science, engineering and management knowledge and practice. The focus is on technology as the primary factor in wealth creation, wealth creation involves more than just money. It may encompass factors such as enhancement of knowledge, intellect, capital, effective exploitation of resources, preservation of the natural environment and other factors that may contribute to raising the standard of living and quality of life. Managing technology implies managing the system that enables the creation, acquisition and exploitation of technology (Khalil, 2000). Technology is the most influential factor in a wealth-creation system; there are other factors that contribute to the system. Management of technology is an interdisciplinary field because it involves combining knowledge from science, engineering and business administration fields (Khalil, 1992). It impacts different functional entities of the cooperation: research and development, design, production, finance, personnel and information. Its domain involves both the operational and strategic interests of the organizations. The operational aspect deals with day-to-day activities of organization, while strategic dimension focuses on long term issues (Berman, 1992). Technology generates wealth when it is commercialized or used to achieve a desired strategic or operational objective for an organization (Gaynor, 1996). While the underlying premise for the management of technology is the most influential factors that contribute to the system. Management of technology treats technology as the seed of the wealth- creation system and with proper nourishment and good environment seed grows to become a healthy tree (Boskin and Lau, 1992). There are three important factors that a successful management of technology has to tackle. First, there is always a time lag between the development

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of a technology and the commercialization of a product or service borne out of that technology. Second, it is very hard to have insight into the future when making evaluations and planning. Third is the readiness and abilities of engineers to draft ideas and concepts and manage development (Natarajan, 1999). It is a potential weak spot in management systems by putting emphasis on the strategic objective of the organization. It guides management in efforts to improve productivity, increase effectiveness and strengthen the competitive position of the enterprise. Managing technology involves continuous effort in creating technology, developing novel products and services and successfully marketing them (Ford, 1988). This requires great creativity along with a system designed to exploit. The effective management of technology is essential if all the potential benefits for individuals and organizations are to be realized. It has the capability to transform products and processes and can make a huge contribution to organizational performance and even to national well-being. Effective management needs to make complex decisions associated with identification and evaluation of technologies, developing new or improved products and processes and integrating technology with other business processes, and to manage change required by technology implementation (Ulhoi, 1996). Effective management includes the diffusion of innovation; strategic design; power, politics and influence; and the relationship between technology and the individual, organization, and society. Sometimes, adopting technology seems an attractive option for those of us who are faced with an improvement challenge (Floyd, 1997). However Technology is not a panacea for the skills we lack or for every improvement issue we encounter. Instead, it is a tool that complements our abilities, allowing us to do more and to become more productive. Technology is the productive power, which has been both the development and destruction play a critical role in reshaping the world (Daim et. al., 2014). On the other hand we can say that it is one of the prime factors of production therefore the effective management of technology is essential to the optimum utilization of natural resources. III. Reasons Affecting Production of Coal Initial talking with the concerned, visits to the production floor and talking with experts, the following reasons were find that affect production of coal within CCL.  Outdated mining technologies are adding to the problems on the existing technologies. The cost and time run over due to lack of structural, tactical and strategic issues are one of the greater concerns which add to the problem.  Technology change calls for acquisition, development, utilization and maintenance of adequate human resource.  Old Heavy earth moving machinery has not been replaced at desire scale, which has caused increase in population of old fleet of HEMM with poor reliability and efficiency.  Inadequate drilling capacities, backlog in overburden removal, mismatch between excavation and transportation capacities, low availability and under-utilization of HEMM etc.  Frequent stoppage of coal transportation due to siding and other illegal activities. The issue of obtaining environmental and forest clearances is delaying many mining projects. For the above problems identified, we talk with concerned persons regarding technology installation. The following technology has been installed within CCL for Open Cast Mines for last couple of years. Sl. No. 1

Open Cast Mines Ramgargh (Rajrappa OC)

KDH Hesalong OC

Installed Technology within CCL, Ranchi, Jharkhand Installation and commissioning of 10 Cu. Mtrs. and 25 Cu. Mtrs. shovels and 85 T dumpers, Pithead coking coal beneficiation plant. Installation and commissioning of 10 Cu. Mtrs. and 25 Cu. Mtrs. shovels and 85 T dumpers.

3 4

Piperwar OC Jharkhand OC

Installation and commissioning of mobile Inpit coal crushing and conveying Installation and commissioning of 10 Cu. Mtrs. and 25 Cu. Mtrs. Shovels and 85 T Dumpers

Urimari OC

6 7

Ashoka Expansions OC Amlo OC

Installation and Commissioning of 10 Cu. Mtrs. and 25 Cu. Mtrs. Shovels and 85 T Dumpers, Rapid Loading System Introduction of Surface Minor in Ashoka expansion. Installation and Commissioning of 10 Cu. Mtrs. and 25 Cu. Mtrs. Shovels and 85 T Dumpers

IV. Formulation of problem with specific objective Coal has been recognized as the most important source of energy for electricity generation and industries such as steel, cement, fertilizers and chemicals are major sectors of coal consumption. So in order to satisfy the coal demand, the Indian coal industry needs more investment and private players to raise its production level. The coal washeries have to take bigger role in the industry to produce less moisture and ash-based coal to sustain in strict environment regulations. The other problem is coal quality and transport. The quality of Indian coal is poor over the past decades. Run-ofmine (ROM) coals typically have the high ash content (ranging from 40% to 50%), high moisture content (420%), low sulfur content (0.2-0.7%), and low calorific values (between 10.5 and 20.9 MJ/kg). Low calorific value implies more coal usage to deliver the same amount of electricity. Nearly 65% of non-coking coal in India

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are low quality (grade E or below) and amount of ash in coal increases as one moves from core of the coal seam to its floor. In 1997, Ministry of Environment and Forests (MoEF) mandated the use of beneficiated coals with ash content of 34% (or lower) in power plants located beyond 1000 km from their coal source, and plants located in critically polluted, urban ecologically sensitive areas. Another constraint is lack of appropriate contractual agreements between suppliers and consumers. Coal supply is not guaranteed at any particular quality (sizing, ash content, calorific value, etc.) and there is no penalty for non-compliance. Coal producers in India have not taken the responsibility of certifying quality at consumer-end, but rather at the supply end. The coal transporters do not take overall responsibility for either the quality or quantity. In context of present research work and stated facts despite of having ample coal deposit, imported machinery / technology as well as ample demand in market, actual production of coal is less than the targeted one. Research gaps classify issues and the areas highlighted above in previous section. Basic gaps which have been taken into consideration are listed below:  There is a noticeable gap between forecasted and actual output of coal in context to CCL.  There is a significance mismatch from procurement of technology to implementation to its maintenance.  There is a significant gap in terms of Infrastructure, strategic and operational issues related to management. The indigenous development of mining technologies will likely require government support of research, development, demonstration, and deployment of the technologies, as development and deployment of these technologies require long time scales and sufficient investment. V. Research Methodology Present research is based on survey based and instruments for that that were used to collect data is by Questionnaire designing, Personal interview and information collected during interactions with experts working in leading open cast mines. Questionnaires used for getting information were prepared in such way to cover all aspects under study. Interview method was used to collect information from the respondents to get their perception about the effective management of technology in the following open cast mines situated at the state of Jharkhand. Instrument development is covered into four stages: Item generation, Pre-pilot study, Pilot study and Large-scale Data Analysis. In the first stage of item generation, items were generated based on literature review, along with discussion and interviews with experts and practitioners working under this field. In the pre-pilot study, these items are reviewed by senior level experts and re-evaluated through structured interviews with some practitioners who were asked to comment on the appropriateness of the research. As the interactions were held with senior most experts, information (data) provided by them elicited valuable feedback about this topic. Based on the feedback collected, redundant items are eliminated and new items are added. For first three stages, basic requirement for a good measurement is content validity, a judgment by experts of the extent to which a scale truly measures the concept that it intended to measure study (Flynn et al., 1990). To assess content validity a ‘dry run’ was performed and subsequently questionnaires were administered to practitioners and experts. Based on their feedback and suggestion(s), final versions of the questionnaires were sent to respondent. In the context of present economic liberalization, globalization and free market this study assumes greater importance as management of technology will be the hallmark of policy. In CCL it has been studied that the adoptions of state of art technology comprehend the present need; still they are lacking with effective management of the technology installed. Employees are also very reluctant of being at par with the technology installed. Some of the important factors related to selection and implementation of new technology are identified after talking with shop floor managers, senior level decision making experts within CCL. Sl. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Factors effecting mgmt. of Technology Planning for Technology Selection of Technology Technological Skills Financial Feasibility Cost and Benefit Analysis Real time Technological Advancement Managing HEMM Technology Supply chain issues Waste reduction by applying new technology Real time transfer of technological change Socio-Economic issue on new Technology Maintenance of overall Equipments Continuous Monitoring of Quality Proper Utilization of Machines Real time Training for Technical up-gradation Safety needs for continuous technology

Lit. support lead by Authors’/ Practitioners’ Steele, 1989; Roy and Singh,2014 Allan et. al., 2013 Fredrick, 1987 Sun, 1993 Momaya and Ajitabh, 2005 Moustafa,1990 Perrino and Tipping, 1989 Machado, 1992; Mashelkar, 2001 Joshi, 1991 Moustafa, 1990; Sharif Nawaz, 1983 Millet and Stephen, 1990 Monica Maria, 2014 Prasad,1995 Lan, P and McCarthy, 2003 Gregory,1995

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Level of Mgmt. for adoption of new technology Technological barrier due to Land Acquisition Technological Effect on Environment Proper Management Of Manpower Market Feasibility

Khalil, 2000 Akhilesh,2013 Anders et. al., 1997; Bowonder and Miyake,2000 Bright. 1969 Burgelmam et. al., 2001; Dodgson et. al., 2008

The above factors identified were based on the production report generated by the CCL on the installed technology considered for the study. A. Factor Analysis Communalities which show how much of the variance in the variables has been accounted for by the extracted factors (Flynn, and Sakakibara, 1990). For instance over 93% of the variance in Maintenance of Equipments is accounted for while 67% of the variance in Management of Manpower is accounted for. Communalities Factors

Initial

Extraction

Top Level

1.000

.864

Middle Level

1.000

.495

Selection of Indigenous Technology

1.000

.489

Selection of Foreign Technology

1.000

.714

Market

Feasibility

1.000

.573

Financial Feasibility

1.000

.580

Cost and Benefit Analysis

1.000

.495

Technological Advancement

1.000

.908

Continuous Monitoring of Quality

1.000

.559

Capacity Utilization of Machine

1.000

.701

Real time Training Needs

1.000

.695

Management Of HEMM

1.000

.785

Management Of Manpower

1,000

.679

Land Acquisition

1.000

.541

Technological Effect Environmental

1.000

.934

Supply Chain Issue

1.000

.867

Waste Reduction

1.000

.445

Socio-Economic

1.000

.605

Transfer Of Technological Change

1.000

.528

Policy Implication

1.000

.623

Planning for New Technology

1.000

.923

Maintenance of Equipments

1.000

.931

Technological Skills

1.000

.722

Safety Requirements

1.000

.408

*Extraction Method: Principal Component Analysis.

The next item shows all the factors extractable from the analysis along with their eigen values, the percent of variance attributable to each factor, and the cumulative variance of the factor and the previous factors. Notice that the first factor accounts for 12.853% of the variance, the second 9.173% and the third 8.603 and up to ten 4.188%. All the remaining factors are not significant. Total Variance Explained Initial Eigen values

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Component Total

% of Variance Cumulative % Total

% of Variance Cumulative %

3.085

12.853

3.085

12.853

2.299

9.578

2.202

9.173

22.026

2.202

9.173

22.026

2.260

9.415

18.994

2.065

8.603

30.629

2.065

8.603

30.629

1.918

7.991

26.985

1.654

6.892

37.521

1.654

6.892

37.521

1.910

7.957

34.942

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1.369

5.705

43.226

1.369

5.705

43.226

1.578

6.575

41.517

1.240

5.168

48.393

1.240

5.168

48.393

1.434

5.976

47.492

1.188

4.950

53.343

1.188

4.950

53.343

1.203

5.013

52.505

1.136

4.733

58.076

1.136

4.733

58.076

1.168

4.866

57.371

1.121

4.670

62.746

1.121

4.670

62.746

1.161

4.838

62.208

1.005

4.188

66.935

1.005

4.188

66.935

1.134

4.726

66.935

.977

4.072

71.007

.863

3.596

74.603

.846

3.523

78.127

.807

3.363

81.490

.719

2.995

84.485

.704

2.933

87.418

.686

2.858

90.276

.628

2.617

92.893

.518

2.157

95.050

.473

1.969

97.020

.455

1.894

98.914

.113

.471

99.385

.099

.414

99.799

.048

.201

100.000

Rotated Component Matrix 1

Planning for New Technology

.956

.009

.083

.032

-.005

.009

.023

-.020

-.002

-.034

Technological Advancement

.948

.011

.076

.010

-.005

-.011

-.007

-.031

-.013

-.036

Supply Chain Issue

-.006

.924

.037

.015

-.011

.048

-.065

-.040

.031

-.040

Top Level

.008

.918

.017

.003

-.077

.066

-.067

-.037

-.010

-.062

Waste Reduction

.062

.574

.053

-.025

.120

.004

.206

.209

.052

.073

Policy Implication

.112

.046

.740

-.015

.154

.076

-.015

.174

.027

.016

Financial Feasibility

-.142

.048

.721

-.033

-.158

.002

.035

-.098

-.022

.018

Market

.355

-.027

.643

-.035

.122

.088

-.074

.003

.037

.042

Middle Level

.340

.221

.400

-.021

.207

.085

-1.705

.259

.145

-.179

Technological effect on Environment

.021

-.002

-.040

.963

.054

-.013

-.015

.012

.023

-.026

Maintenance of Equipments

.020

-.007

-.035

.955

.107

-.017

-.014

.016

-.056

-.038

Management Of Manpower

.010

.023

.149

.104

.783

-.081

.075

-.078

.047

-.105

Land Acquisition

-.121

-.014

.039

.166

.588

.172

-.341

.055

-.035

.014

Safety Requirements

.143

-.006

-.149

-.069

.483

.106

.011

.332

.074

.000

Cost and Benefit Analysis

.342

.116

.215

.045

.363

-.061

.192

-.016

-.261

.275

Technological Skills

.056

.158

.061

.040

-.071

.801

-.028

.146

-.059

.131

Real time Training Needs

-.054

-.048

.099

-.082

.181

.754

.087

-.121

-.046

-.218

Socio-Economic

-.023

.062

.030

.003

.118

-.120

-.751

-.076

-.016

.047

Selection of Indigenous Technology

-.047

.318

.025

-.029

.224

-.250

.470

-.031

-.209

-.081

Management Of HEMM

-.075

.065

.105

.045

.038

.011

.049

.869

-.011

.055

Selection of Foreign Technology

.000

.022

.067

-.053

.025

-.169

-.162

.121

.791

-.104

Capacity Utilization of Machine

-.020

.090

4.385 .045

.059

.127

.367

-.279

.698

.286

Continuous Monitoring Of Quality

.053

-.112

.192

-.054

-.031

-.112

.026

.057

.003

.614

Transfer Of Technological Change

.194

-.081

.313

.020

.060

-.106

.167

.010

-.013

-.585

Feasibility

Extraction Method: Principal Component Analysis Rotation Method : Varimax with Kaiser Normalization Rotation converged in 12 iterations

After analysis of the various factors only ten factors have accommodated for the effective management of new technology and If these interacting factors can be managed properly it is obvious that equipments reliability will be improved readily, reduction of operational cost and profit maximization will be the end results.

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C. Scree Plot The Scree plot (shown in Appendix-1) is a graph of the eigen values against all the factors (Cattel, 1966). The graph is useful for determining how many factors to retain. The point of interest is where the curve starts to flatten. It can be seen that the curve begins to flatten between factors 10 and 11. Note also that factor 11 has an eigen value of less than 1, so only Ten factors have been retained. VI. Conclusion and Future direction Technology has become backbone of corporate sustainability after pro-market reforms due to immense competition. Indian prime industries are facing a lot of competition nowadays due to the problem of globalization. To keep in race, every industry has to be up to date especially in the area of technology involved in it. Mining industries warrants for state of the art technology in real time basis. In our exposition towards management of technology, we will view firms as open systems: sets of interrelated activities that interface with the environment. A firm is viewed as a whole, and any activity that a firm does is meaningful only to the extent that it contributes something to the whole function of the firm. Further, firm interfaces and directed with environment at multiple fronts - customers, competitors, government, markets and so on. To develop ideas about management of technology within an open-systems view, we will employ four basic concepts: firm as a value chain, industries as competitive domains, forms of technological change and ultimately creation of value in the line of competitive advantage. Technological developments play an important role to influence the mining productivity. The application of motive power and mechanical improvement to the process of production has accelerated the pace of industrialization to an unprecedented degree. The present study highlights the importance of technology and its proper management towards organizational performance. Taking the most viable area in manufacturing like coal, present research analyzed the problem of technological imbalance from management point of view of whether the company has ably harnessed the benefits of technology installed or not. Most of the technology adopted so far by our present domain / area of concern is almost imported with latest technology involvement. Several technological factors including degree of mechanization, technical know-how, product design etc. have a significant implication in overall efficiency. Improvement in any of the technological factors will contribute towards increase in industrial productivity. Effective management of technology is essential if all the potential benefits for organizations are to be realized. It has the capability to transform products and processes and can make a huge contribution to organizational performance. Effective management needs to make complex decisions associated with identification and evaluation of technologies, developing new or improved products and processes and integrating technology with other business processes to manage change required by technology implementation. In this study, an attempt has been made empirically to identify the factor(s) that influence management of technology. It is expected that the present study will provide a valuable insight into the process of management of technology in a competitive market and help steel companies operating in mature markets to formulate effective strategies towards gaining competitive advantage in coming days of operation. The finding of our present research can further be developed for studying the same effect for medium and small size companies related to other primary domain. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Allan C. Wexler, J. De Loecker (2013). Impact of productive efficiency through Reallocation and Technology - evidence from US steel Industry. Centre for policy studies - Princeton University, First version 2012, current version 2013. Akhilesh, K. B. (Ed.), (2013). Emerging Dimensions of Technology Management. Springer Verlag, 2013. Anders, D. Soren, G., Holm, C. (1997). Management of Technology in a complex world. International Journal of Materials and product technology, Vol. 12, No. 4-6, pp. 239-259. Bowonder, B. and Miyake, T. (2000). Technology Management: A Knowledge Ecology Perspective. International Journal of Technology Management, 19 (7/8), pp 662-684. Bright, J.R. (1969). Some Management Lessons from Technological Innovation Research. Long Range Planning, Vol.-2, No.-1, pp. 36-41. Burgelman, R.A., Maidique, A. and Wheelwright, S.C. (2001). Strategic Management of Technology and Innovation. New York, USA, McGraw-Hill. Berman, E. M. (1992). Technological Competitiveness in the Global Economy. International Journal of Technology Management, Vol.7, No. 4(5), pp. 347-358. Betz, Fredrick (1987). Managing Technology. Prentice Hall, Englewood Cliffs, NJ, USA. Boskin, M. J., and Lau, J. (1992). Capital Technology and Economic Growth. Stanford University Press. Bhalla, Sushil K. (1987). The Effective Management of Technology. Battelle Press, Columbus OH, USA. Chaudhuri S., Moulik T.(1986). Learning by doing, technology transfer to an Indian manufacturing Firms. Economic and Political weekly, Vol-XXI, No. 8. Cattel, R.B. (1966). The Scree Test for the Number of Factors, Multivariate Behavioral Research, No. - 1, pp 245-276, California, July-August, 2007. David, B. and Kirit, V. (2001). Meeting Technology needs of enterprises for national Competitiveness. UNIDO, Viyana, Austria, pp. 7-9. Dodgson, M. Gann, D. and Salter, A. (2008). The Management Technological Innovation Strategy and Practice. UK, Oxford University press.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net ANALYSIS OF BANK’S UNSTRUCTURED DATA USING MAP REDUCE TECHNIQUE Jeny George1, A. Karthika 1, Dr. Ulagamuthalvi V2 Student, Faculty of Computing, Sathyabama University,Chennai, INDIA 2 Asst.Professor, Faculty of Computing, Sathyabama University,Chennai, INDIA _________________________________________________________________________________________ Abstract: Data mining techniques have always been efficient and is still very much implementable, but Big Data concept is emerging. Data mining can process only structured data whereas Big Data deals with unstructured data. This system talks about tracking down and analysing the transactions of users who have multiple accounts in more than one bank. We particularly specify on transactions done in a specific or suspicious manner and generate a report on it using the hadoop tool. The multiple accounts and tracked down using the map reduce concept. 1

Keywords: Map reduce, Hadoop, Big data, Data mining, Unstructured data __________________________________________________________________________________________ I. Introduction Big data refers to extremely large data sets which makes it difficult for a typical database software tool to capture, store, handle and put into use. The reasons such an outbreak of Big Data is the increase of storage capacities, increase of processing power and abundant availability of data. It is capable of organizing information coming from multiple, heterogeneous, independent sources with complex and developing relationships and hence keeps growing. All of the world’s data almost 90% of it has been generated over the last two years since the origin of Information Technology. Almost all of this of data have been generated from social networking sites (billions of unstructured data). There are many data types in Big Data like Banking, Insurance, Securities and Investment Services, Construction, Retail, Health care, Education etc. The large volume of available datasets allows us to apply more sophisticated models and get more accurate predictions about the future. Researchers have analysed influential and susceptible behaviour in social network using big data, similarly researchers have also done a thorough analysis of collective behaviour using swarm intelligence using large data sets concept [1][2]. Also papers and investigations on network analysis in social networks, social network and influences and also how twitter mood predicts the stock market etc have also been done which essentially proves that big data can be used for various analysis that can sound funny in the beginning but prove to be quite effective in the current scenario [3][4][5]. Also many algorithms have been introduced to mine big data throughout these years of the boom of data but none as such as the map reduce has been proven to be as effective as them. As you can see modern researchers also use this technique to mine large data sets [7]. No major development or analysis has been done on the banking sector which is what we have highlighted in our project. Big Data in action step wise: (i) Acquisition, (ii) Extraction, (iii) Integration, (iv) Analysis, (v) Interpretation, (iv) Decision. In our country in most banks there are rules where people have to submit government proof when they transact money above a certain huge amount. Most people transact money on the margin line from various multiple accounts so as to avoid such submissions. This lot of people are who we analyse and separate in our project paper. It insists on three tier architecture: (a) Big Data implementation in multi system approach, (b) Application deployment-Banking, and (c) Extraction of useful information from unstructured data. This paper is mainly implemented for banking domain. There will be two major departments: (1) Bank server for adding new clients and maintaining their accounts. During registration every user has to provide an ID proof to create an account in any bank. (2) Accounts monitoring server will monitor every user and their account status in different banks. This server will retrieve users who maintain and transact more than Rs. 50,000 / Annum in all 3 accounts in different banks using the same ID proof and thus map them and reduce those particular accounts. II. Methodology The system design explains the flow of the control and how we have implemented the project: A. User Account Creation Initially, the user has to create his own account credentials and is only authenticated to access the network. Once the user creates as such, he is asked to sign in and request a particular service from the Service Provider. Based on it, the service will be processed and a response is send. All this information will be stored in the database of the Data Service Provider. In this system, we design an Interface to communicate with the server using the

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programming Languages like Java/ .Net.If access is allowed by the server the user can access the articular services available to him/her by the service provider . B. Bank’s Server Big Data Service Provider contains unstructured data abundant amounts of it in its database. Also, the Service Provider manages all the users details to validate them when they sign in into their account. These details will be stored in the database of the Service Provider. Also the Data Server redirects the users request to the Resource Assigning Module to process the particular request. The request of all the users in unison will be processed by the Resource Assigning Module. To connect to the client and with the other modules in the network, the server will establish a relation between them. This is why we are going to create the Interface as mentioned earlier. Also the Service Provider will send the user’s request to the Resource Assigning Module in First in First out (FIFO) manner. C. Centralized server A sole sign-on protocol for the web, the CAS’s purpose is to allow a user to access more than one application when the user provides their details (such as user id and password) only once. It also allows web applications to allow users access without gaining access to a user's secure detail, such as a password. It is also software package that implements this particular protocol. D. Mapping of User Account In this module we map or separate the users who have account in more than three banks with the same id proof and credentials. They are mapped by using Hadoop Map Reduce technique. The Hadoop tool basically implements the map-reduce algorithm to analyse the input of data. Map() procedure: It performs filtering and sorting(such as sorting students by their grades, sorting account holders using their unique customer Id Reduce() procedure: It performs a summary operation(segregating account holders based on their transaction amount MapReduce system arranges the process by marshalling the distributed servers, parallelly handling multiple kinds, administrating all connections and data transfer between various parts of the system.In this paper, Map() procedure sort out the users who used the same ID for more than three bank accounts and Reduce() procedure segregates the users who made transaction more than Rs.50,000-Rs.1,00,000 per annum in all the three bank accounts. E. Account Transaction Review In this module we are getting information about the users who have accounts in three different banks and we also filter the transaction done by them. Finally we review the information transacted by the user that is, if they transact more than Rs.50,000-Rs.1,00,00/ANNUM in all the three accounts. We create a report based on their transactions in a day, month or a year depending on the threshold specified. F. Tracking of suspicious users By extracting the vital information from the unstructured data for transaction of bank service and tax service we analyse the ratio through our application, and produce the best output for both transaction and unpaid tax amount. III. Architecture Diagram The diagram here shows the flow of the system in a gist. A user who has multiple accounts is tracked down using a unique id generated every time he signs in his account using the same address id. From this we generate his transactions and analyse them.

IV. Result and Discussions This is an example of a particular users transactions. So similarly when we search for a particular users transaction or log in to the admin of the bank and search for a specific user, based on the threshold specified the whole set of his transactions is generated. So in the tool when you type for a user it is checked if he has multiple

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accounts and if so it is map and reduced and a report of his transactions is created. With this report we can analyse the amount of money hes transacted in a month.

V. Conclusion We have therefore found that big data can prove to be very useful not only in social media sites and networks but also in banking applications, medical sectors etc. We have successfully implemented the system and have been succesful in generating reports of users with multiple accounts and their transactions. Similar work can be done in the future for medical, insurance applications as well where we analyse common diseases or frauds respectively. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

S. Aral and D. Walker, “Identifying Influential and Susceptible Members of Social Networks,” Science, vol. 337, pp. 337-341, 2012. S. Banerjee and N. Agarwal, “Analyzing Collective Behavior from Blogs Using Swarm Intelligence,” Knowledge and Information Systems, vol. 33, no. 3, pp. 523-547, Dec. 2012. J. Bollen, H. Mao, and X. Zeng, “Twitter Mood Predicts the Stock Market,” J. Computational Science, vol. 2, no. 1, pp. 1-8, 2011. S. Borgatti, A. Mehra, D. Brass, and G. Labianca, “Network Analysis in the Social Sciences,” Science, vol. 323, pp. 892-895, 2009. D. Centola, “The Spread of Behavior in an Online Social Network Experiment,” Science, vol. 329, pp. 1194-1197, 2010. E. Birney, “The Making of ENCODE: Lessons for Big-Data Projects,” Nature, vol. 489, pp. 49-51, 2012. C.T. Chu, S.K. Kim, Y.A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng, and K Olukotun, “Map-Reduce for Machine Learning on Multicore,” Proc. 20th Ann. Conf. Neural Information Processing Systems (NIPS ’06), pp. 281-288, 2006. E.Y. Chang, H. Bai, and K. Zhu, “Parallel Algorithms for Mining Large-Scale Rich-Media Data,” Proc. 17th ACM Int’l Conf. Multimedia, (MM ’09,) pp. 917-918, 2009. Y.-C. Chen, W.-C. Peng, and S.-Y. Lee, “Efficient Algorithms for Influence Maximization in Social Networks,” Knowledge and Information Systems, vol. 33, no. 3, pp. 577-601, Dec. 2012.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net SOCIAL AWARENESS THROUGH NEW MEDIA Niket Mehta Research Scholar and Assistant Professor, Department of Animation and Multimedia, Birla Institute of Technology, Mesra, Ranchi, Extension Centre Noida, Uttar Pradesh, INDIA Dr. Suparna Dutta Associate Professor, (Humanities), Department of Management, Birla Institute of Technology, Mesra, Ranchi, Extension Centre Noida, Uttar Pradesh, INDIA Dr. Asit Bandyopadhyay Associate Professor, Department of Management, Jaypee Business School, Noida, INDIA __________________________________________________________________________________________ Abstract: Today we are living in a networked world where everybody is 'connected' to others through various modes of communication. New tools and techniques of communication have made this world a small place. The prevalent new media culture of computer-mediated forms of production, distribution and communication has revolutionized the communication. It has eased all the stages of communication, including acquisition, manipulating, storage and distribution. Current technological advancements in the field of communication have paved ways for digital multimedia-rich communication and communication has become more informative and rich with entertainment and engaging content. After experiencing the Internet revolution, we are already taking our initial steps into the virtual world revolution. Just like the World Wide Web had forced content creators to reconsider how they craft and distribute their messages, the rapidly developing virtual world, or 3D Internet, is changing the game again. There is strong need to design and develop effective digital modes or content for communication to address social concerns. This paper explores and brings conclusions through literature review and qualitative studies about the possibilities of social message communication through digital ways. Keywords: New media, digital communication, social message, innovate, customization communication, visual communication _______________________________________________________________________________________ I. DIGITAL WAYS OF COMMUNICATION Communication enables man to ever remain a capable and sensitive social being. Communication also has the potential to make man more responsible as well as conscious and aware. However, it is not possible to ignore that we cannot communicate without the help of technology any more. We have become a generation of digital communication consumers and producers. New internet applications enable even cursory users who have little technical knowledge to construct, share and broadcast their own media and information contents, as they do, for example, on social networking websites. These social media applications also make it possible to showcase the collaborative efforts of potentially millions of users. These new technologies have brought us many wonderful things – perhaps not happiness or contentment, but at least computers, the internet, and new ways of creating and delivering information and art. Yet these advances still remain human tools. The future points towards a communications-driven economy where economic opportunities are not limited by time, distance or geography. In the future, innovations in telecommuting and teleconferencing will reduce our need to commute and travel long distances, saving money, time and reducing environmental impact. E-commerce and advances in the technologies that enable distance learning and virtual doctors’ visits, mean more people will have access to quality products, services and resources, regardless of where they live. And this personalisation of the things we create and how they are consumed for example, e-books, streaming video, music, etc. will continue to transform the global economy. This “communications-driven economy” is not so far off. Imagine for a moment a world where instead of hearing the thump of the morning newspaper against your front stoop, you take in the day’s news on your tablet. This electronic version of newspaper is environment friendly also apart from being graphically rich it is easy and faster to send and receive as well. It can be multimedia rich and interactive where readers can watch videos, give instant feedback and create content as well. User generated content is the best gift of new media and regardless of their levels of technical expertise; users can handle technologies in more active ways. Users build and maintain social networks, they tag and rank information and get involved in to virtual world. They do all these things in collaboration, pooling knowledge and constructing content that they share with each other, which is subsequently re-mixed, re-distributed and re-consumed. This growing phenomenon suggests that users are pleased in significant ways by the ability to play an active role in generating content, rather than only passively consuming that which is created for them by others. New media technologies work differently as per users’ tastes. For example, the history of email has taught us that users may use

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computer-mediated technologies and fashion them for their own purposes, which sometimes supersede or are at odds with the original purposes of designers. Email which was incorporated as a convenience for systems operators and computer scientists to facilitate database and other resource sharing, but its use subsequently by managers, researchers and ultimately many other types of individuals has overshadowed all other network applications in volume of traffic. We should explore the possibilities to use this new digital media for the benefit of mankind and should spread social awareness through it. Let's take an example where new media helped in the development of a nation: - For much of modern history, China has been remote, racist, and self contained with little interest or involvement with Western nations and their affairs. All of that, of course, has changed. With a vibrant economy and spectacular growth and modernization, China has become thoroughly integrated in globalization becoming a major player in world trade and finance and communication technologies have played a great role in it. India too is not an exception which has a huge number of internet and smart phone users. Technology now eventually dominates the lives of children through toys and electronic games as well. Given the immense popularity of computer games, as well as the increasing role played by the digital technologies in childhood activities, it is not surprising that the world of play has come to exist at the borders of technological production and consumption. Since India is the third largest consumer of Internet and 76% of Indian population uses mobile phones as compared to 75.32 % of Chinese population and 103.9% of USA population. That’s why new interactive technology should be tapped for communication. II. NEW COMMUNICATION TRENDS New Digital Media options like the YouTube, Facebook, Twitter, Mobile Phones, Video Games, etc. are being used extensively by everyone including social workers, government officials, and activists to spread the message. Yet, most of the times this communication remains a mechanical action only. Consideration remains focused on mechanical details like ‘user friendliness’. One takes care of the font and the format but the form that matters is only beginning to assert itself. This is where innovation has to work– the interdisciplinary domain which takes into focus both the heart that feels and the mind that think– the left as well as the right hemisphere of the human brain. For example, the author has done a study where he used an interactive computer based video game to spread the message about female feticide to a sample population in a problem affected area in Haryana state of India. Author observed that in-spite of the fact that the area which was of a small town and villages was not very advanced in terms of infrastructure and social conditions yet common people had understanding of computers and smartphones and could understand and enjoy the digital content very well. But if the content is designed as per their tastes, knowledge and culture then it will leave more impact on them. In fact, in India small town people are considered a good consumer of mobile internet and information. They are quite active on social networking sites and explore the information on internet. While talking to a doctor in a small town, researcher found out that patients nowadays are exploring the disease on internet and coming to doctors with great knowledge and expectations, which is a good as well as a bad effect. Doctors feel that half knowledge is a dangerous thing and it becomes difficult to convince such patients. New communication tools and techniques have made end users more self-aware and conscious. Smartphones and mobile applications are acting as virtual opinion leaders. For example, these days a viral disease “Swine Flu” is spreading and people are sharing information on whats app – an instant messenger app on mobile phones regarding this flu – like symptoms, precautions, preventive measures etc. This surely helps to spread the awareness and this awareness leads to better health and many other advantages. Similarly there are groups of users on social networks or chatting mobile apps where people communicate about their interests and social concerns which help in building awareness and healthy society. No doubt, these are being used for the sake of fun and entertainment also by many but there are positive and negative sides of everything. Another example is of Facebook pages where people like some page of particular interest and get related information and updates from the administrator. Facebook has groups option also for example Green Yatra group which is working to spread environment awareness and each such features by one or other company is different and has its own qualities. These are the tools to attract the online users towards some particular news, concern, product and event, etc. New digital modes of communication are multimedia rich hence more attractive and appealing provided they have been designed aesthetically. For example, In India a political party won election because of its successful social media campaign which was designed by professionals and it touched the hearts of common Indians. Another worth mentioning example is of a social campaign ‘Save Our Tigers’ by a leading mobile operator Aircel in association with World Wildlife Fund (WWF) India in 2010. This campaign was targeted to save the gradually dying tigers in India. Throughout the campaign, a fact that only 1,411 Royal Bengal Tigers are left; was emphasized upon to create a stir. Though people were aware of the fact that the tigers were an endangered lot and few of the species are left in the country, not everyone had an exact idea of how many were actually to be found and the extent of the damage or its consequences. Aircel highlighted the number, 1,411 in its communication and succeeded to bring people together and lots of strong personalities and organizations came forward which brought the system into action and thankfully the tigers could be protected and awareness had been generated. While mentioning new ways of communication how can we ignore the power of video games,

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researcher had conducted a study using a game Food Force II which was developed by United Nations to spread the awareness about food wastage and food management. When players played this game, even if they did not like the game, they liked the message given through the game and when they complete the game it was assured that they got the message since they could not complete the game without solving the given tasks. III. INTERACTIVE COMMUNICATION There's an old saying in biology: "Ontogeny Recapitulates Phylogeny” which points to the fact that the human embryo goes through successive stages that closely resemble fish, reptiles, small mammals, then man. Same is true with communication which has reached to current stage by crossing many stages. We cannot compare linear (for example – story, drama, movies) and non-linear communication (for example – interactive games) to previous forms of media which are broadly non-interactive. Interactive entertainment is a fundamentally different proposition than linear, involving quite different psychological mechanisms. Interactive games are there since ages. Although this history stretches back as far as the beginnings of human culture yet when we think of games today, we tend to speak of the digital games that have so recently captured our imaginations. Digital games are something you do, something you do to your head, a world that you enter, and, to a certain extent, they are something you “become”. Digital games which are interactive, participatory; entertainment activity are a window to a new kind of intimacy with machines that is characteristic of the nascent computer culture. The holding power of video game is almost hypnotic fascination. Digital games, as one of the first, best developed, and most popular truly digital mediums embody a wealth of knowledge about interface, aesthetic, and interactivity issues. Historically, video games have been on the technological cutting edge of technically of what is possible, whether it is building online communities on the Internet, creating rich worlds using 3D graphics cards, or allowing dynamic synchronous interaction play by streaming information over the Internet. Indeed, even a cursory glance at the latest games can blow us away by what is currently possible with technology and inspired by the sleek interface or production values games contain. Digital Games have an explicit and carefully thought-out educational purpose and are not intended to be played primarily for amusement. Contemporary developments in gaming, particularly interactive stories, digital authoring tools, and collaborative worlds, suggest powerful new opportunities for educational media. Digital games evoke powerful emotional reactions in their players, such as fear, power, aggression, wonder, or joy. In a game emotions are created by balancing a number of game components, such as character traits, game rewards, obstacles, game narrative, competition with other humans, and opportunities for collaboration with other players. Understanding the dynamics behind these design considerations might be useful for those who design interactive digital learning environments. Digital game playing occurs in rich socio-cultural contexts, bringing friends and family together. Digital games which are legally recognised by United States as an art can also be called multimedia art. The computer game is an art form because it presents its audience with fantasy experiences that stimulate emotion. Computer acts as a medium for emotional communication art. When we communicate through emotions and we get the feedback also then communication become perfect and its digital games which can make our communication multimedia rich, can inculcate emotions into it and gives the feedback also. Digital games are another form of entertainment, a form of cultural expression – especially for the younger generation. You can hunt down a civilisation, you can explore continents... there’s so much you can do. And there’s something for everyone. Games fail in education because of mismatch between the goals of games and the objectives of school-based learning. Efforts to integrate games into the curriculum may fail either because games designed to educate do not engage their intended audience, or because truly engaging games do not provide enough educational value. In digital games the communication is rapid and in both directions. The game tells you what you need to know, and you respond very quickly with what you want the game to do. Give players tools and information enough to solve a problem set up by you and that is what game is all about. Author has observed in a study conducted using Tetris game on teenagers that gaming is an entertaining experience, games are a good learning experience, create curiosity, makes the players goal-oriented, people react by emotional, facial, body gestures during game when they play games, make them take part in game-play, games are participatory and immersive, rewards in the form of score games motivate players, games motivate and sound effects played role in making players engaged in game. Games are a major cultural force. Games that are too hard kind may bore us and games that are too easy may also kind of bore us. As we age, games move from one to the other. We all play games in our lives. To play any game you need to follow certain rules but we have forgotten those rules. A small child is sent to school at three so that she gains knowledge and discipline to play this game later in her life. At this age children are pure and open to receive and retain all information downloaded into them. When we play games in life also so why not to utilize them in communication also? Opinion leaders and experts of various fields also believe that games can serve as a great source of communication. A scientist shared his experiences with author that how once they created a cartoon character and made some animations to communicate children about some science principles. If non interactive animation can serve the purpose of teaching then non-interactive applications can surely prove more useful. An app developer gave suggestions that safety of women can be ensured through mobile apps. A woman can download

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this app on her mobile and can turn it on if she doubts some danger. She can send alarm which will automatically detect her location and alert the police and concerned security officials. Similarly there are apps which can help in getting blood from blood bank, helps to locate someone or some information in case of emergency and most importantly the entertaining representation of information in the multimedia application makes them best tools for communication. IV. CONCLUSION Digital games have the capacity to take us to amazing new worlds with fantastic characters and fully realized interactive environments. Games are designed by teams of professional game developers who work long hours at specialized tasks. The technological and business aspects of these digital games are mind-boggling. Games are best tool to teach something and when we intend to communicate message for social awareness we cannot do it straight forwardly through some lecture, print or television advertisement or even by a video or animation film. Social messaging is important because development cannot come only through infrastructure and economic development, social development has to accompany economic and infrastructure development. If games are used to communicate social messages which can be done indirectly through games as a hidden message in game we can be assured that message has been delivered, learned and adapted by the player of game because player will not be able to complete the game until he finishes the task or challenges offered to him through game play which will demand him to fulfil certain conditions and he can fulfil those conditions by solving the difficulty offered to him in the game or in different levels of game. Game designer can offer various levels of challenges to communicate different messages and it can be done in an interactive, participatory and entertaining way through interactive games. That’s why new interactive technology should be used for communication because current generation of people across the globe appears to have easy access to such technology enabled options like the internet, telecommunication, smart phones and other such gadgets of communication and 'infotainment' and they are quick adopters of technology as well as new forms of communication and for them interactive communication can be a best option. REFERENCES [1] [2] [3]

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[9] [10] [11] [12] [13] [14] [15] [16]

[17] [18]

Choudhary Vidhi (2013) - Choudhary Vidhi, Livemint.com, Use of video apps on mobiles more popular in smaller towns, http://www.livemint.com/ Digital Media Across Asia, India Case Study, accessed from http://comm215.wetpaint.com/page/India+%3A+Case+Studies Drypen (2010), Aircel, partnering with World Wildlife Fund (WWF) India, has launched the 'Save Our Tigers' initiative (Campaign ), accessed from http://drypen.in/marketing/aircel-partnering-with-world-wildlife-fund-wwf-india-has-launched-thesave-our-tigers-initiative-campaign.html Eric Feigenbaum, Electronic Methods of Communication in Business, Demand Media accessed from http://smallbusiness.chron.com/electronic-methods-communication-business-2934.html Freeman David (2003) - Creating Emotion in Games: The Craft and Art of Emotioneering, New Riders Publishing USA Games For Change, Accessed from http://www.gamesforchange.org/ Green yatra.com, http://greenyatra.org/ Has the introduction of electronic means of communication, shifted the relationship between speech and writing?, accessed from http://iain12790.hubpages.com/hub/Has-the-introduction-of-electronic-means-of-communication-shifted-the-relationshipbetween-speech-and-writing Internet world Stats.com (2013), Accessed from http://www.internetworldstats.com/top20.htm on 17 February, 2013 James F. Scotton and William A. Hachten (2010) - New Media for a New China, Wiley- Blackwell Publication, ISBN 978-14051-8797-8 Kim Krause Berg (April 2011), Using Social Awareness Streams To Learn What People Care About accessed from http://searchengineland.com/using-social-awareness-streams-to-learn-what-people-care-about-74904 Koster Raph (2005), A Theory of Fun for Game Design, Paraglyph Press, USA, ISBN 1-932111-97-2 Meaghan Edelstein (May 2010), 8 Tips for a Successful Social Media Cause Campaign, accessed from http://mashable.com/2010/05/10/social-cause-campaign/ Protalinski Emil (2011) - Techspot.com, On May 9, 2011, 9:00 AM, The US legally recognizes video games as an art form, accessed from http://www.techspot.com/ on June 16, 2013 SEETHA, accessed from http://seeta.in/j/products/food-force-ii.html on August 19, 2014 Squire Kurt (2003) - Squire Kurt (2003), Video Games in Education, Comparative Media Studies Department, 14N-205, Massachusetts Institute of Technology, Cambridge, MA. 02139 USA, Accessed from http://citeseerx.ist.psu.edu/ on 23 May 2011 Telecom Tiger.com (2012), Aircel-NDTV Tiger initiative concludes “Save Our Tigers” Telethon at Ranthambore, accessed from http://www.telecomtiger.com/ Tom Van Vleck, The Risks of Electronic Communication accessed from http://www.multicians.org/thvv/emailbad.html

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net IDENTIFYING GENETIC MUTATION RARE GENETIC DISORDER BY ANALYZING CHARACTERISTICS OF GENOTYPE-PHENOTYPE BY IMPLEMENTING APRIORI ALGORITHM Bipin nair B J1 ,Ratheesh A2, Koushik K S3 Department of Computer Sciences Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham, Mysore Campus, Karnataka, India Abstract: An The purpose of the paper ―Identifying genetic mutation rare genetic disorder by analyzing characteristics of genotype-phenotype by implementing data mining algorithm's ‖aims at reducing the complexities involved in determining the mutation occurred in human DNA sequence. The Data mining algorithm is chosen from specific areas like association. The algorithms are grouped in such way that it can be adapt to user requirements. The visualization provided for the mined outputs are represented in graph .The available data mining tools require expert users to carry out experiments. The scope of this relies in the area of visualization of the mined data. The visualizations can be made interactive such that the outputs can be easily interpreted by the user with less effort. Keywords: DNA; mutation; genotype; phenotype; genetic; I. Introduction Data Mining is the process of analyzing data from different prospective and summarizing it into useful information .Data Mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationship identified. Technically Data Mining is the process of finding correlations or patterns among dozens of fields in a large relational database. Data mining often involves the analysis of data stored in a data warehouse. One of the major data mining techniques is association. It is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items of genetics disease symptoms in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. Manually analyzing the genetic variation is very difficult. Since many genetics shares similar symptoms, it is very difficult to identify the genetic disorder accurately. To analyze genetic variation in medical labs it requires amino acid analyzer which is very expensive.

Figure 1 A sample flow diagram for working of the algorithm

II. Overview This paper deals with the analysis of a mutation is a change of the nucleotide sequence of the genome of an organism, virus, or extra chromosomal genetic element. Mutations result from unrepaired damage to DNA or to RNA genomes (typically caused by radiation or chemical mutagens); Mutation can result in several different types of change in sequences. Manually analyzing the genetic variation is very difficult. Since many genetics shares similar symptoms, it is very difficult to identify the genetic disorder accurately. To analyze genetic variation in medical labs it requires amino acid analyzer which is very expensive. So, implementing the data mining algorithm in such a way that it can handle the genetic data sets and to visualize the result accurately in an understandable manner III. Problem Statement Identifying genetic mutation rare genetic disorder by analyzing characteristics of genotype-phenotype by implementing apriori algorithm. There are many data mining tools available today. There are excellent tools that are available. But they lack in some points. They are,

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• • • • • • • •

Sometimes expert support is required to use these tools. The dataset should have a common format. If not, it will ask for the conversion to its own format. The naive user finds difficulty to do this step. They provide limited user interaction. The processed output of these tools is not completely understandable. Manually analyzing the genetic variation is very difficult. Since many genetics shares similar symptoms. It is very difficult to identify the genetic disorder accurately. To analyze genetic variation in medical labs it requires amino acid analyzer which is very expensive.

IV. Problem Formulation The proposed paper helps the user to work with the major data mining algorithms in an easy way, and the user is provided with an interactive and effective visualization that helps to make a useful decision. It mainly overcomes the limitations of the existing work in terms of user involvement and visualization. The Proposed work performs, The algorithm handles text as well as numeric data. • Developing an understanding of the application domain, relevant prior knowledge and the goals of the end-user. • Selecting a dataset on which discovery is to be performed. • The system will be user friendly; a user will know what is happening to their input data. • Data Pre-processing: This stage includes operations for dimension reduction (such as feature selection and sampling); data cleansing (such as handling missing values, removal of noise or outliers); and data transformation (such as discretization of numerical attributes and attribute extraction). • Prediction of genetic disorder undergoes various procedures such as  Uploading dataset: In this stage the dataset which is to be examined should be uploaded.  Pre-Process: In this stage elimination of the records whose attribute information are missed and other pre-processing is done.  Analysis: In this stage the outcomes are clearly analyzed to provide remedial measure and other suggestion like gene therapy required to the patient will be presented.  Visualize: In this stage visualization of the output is seen. • •

• • • •

The visualization will be easily understandable for a user. To analyze genetic variation in medical labs it doesn’t requires amino acid analyzer which is very expensive Choosing the data mining algorithm. This stage includes selecting the specific method to be used for searching patterns. Employing the data mining algorithm. Generate the frequent pattern of symptoms. Selects algorithm to analyze genetic variation in DNA sequence. Evaluating and interpreting the mined patterns

• •

Visualization Module Request Handler

•

Related Work

The request handler consists of a controller class which controls the requests and responses. Controllers provide access to the application behavior which is typically defined by a service interface .Controllers interpret user input and transforms such inputs into a sensible model which will be represented to the user by the view. Controller is a single method that is responsible for handling a request and retrieving an appropriate model and view. The DB Controller controls the relational data store .The details of the user are stored in a local data base Each Algorithm has tree formatter. • Input • Output • Visualization A) Apriori Algorithm • It is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items of genetics disease symptoms in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

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â&#x20AC;˘ Apriori algorithm Pseudo code: Input: A patient data Set Output: Frequent symptoms set Method: Ck: Candidate itemset of size k Lk : frequent itemset of size k L1 = {frequent items}; for (k = 1; Lk !=MAX; k++) do begin Ck+1 = candidates generated from Lk; for each transaction t in database do increment the count of all candidates in Ck+1 that are contained in t Lk+1 = candidates in Ck+1 with min_support End Return k Lk; VI.

Experiment Result Figure 2 Taking input

Figure 3 preprocessed data set after avoiding anomalies

Figure 4 Frequent pattern generated using apriori

VII. Literature Survey Implementation of the tree scanning method to detect associations between genetic haplotypes and quantitative traits, utilizing the evolutionary history of the haplotypes, in samples of unrelated individuals [17]. Sub-sumption,properties,class description are taken into account for data selection before data mining. Each of this is scenario is illustrated [18] GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait [19]. To develop phylogenetic substitution models to test for associations between evolutionary rate of genotype and phenotype. In this work introduce a new method for rating hybrid rate matrices between genotype and phenotype [20]

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The multiple-to-one association rules with stringent FDR level for aerobic, anaerobic, facultative, endospore and Gram-negative phenotype contain significantly larger numbers of COGs than those by pairwise methods [21] VIII. Conclusion The paper Analysis, The advantage of the proposed system is to improve the efficiency of the existing system by the reducing the complexities involved in determining the mutation occurred in human DNA sequence. This result has a profound impact in medical sciences. This work will help to identify common symptoms observed in same category of genetic disorder in an efficient way so that it will reduce the through put and in improve efficiency. The proposed idea will help physicians to diagnose the genetic disorder and develop strategies for its therapy. This work is part of a research paper, so there were time constraints in the implementation of the paper. As the technologies being used for this were changed in order to come up with the best result, all that has been achieved within this time is the final paper. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11] [12] [13] [14] [16] [17]

[18]

[19]

[20]

[21]

Data Mining- Introductory and Advanced Topics Data Mining: Concepts and Techniques by Jiawei Han A Comparative Analysis of Data Mining Tools in Agent Based Systems by Sharon Christa, K. Lakshmi Madhuri, V. Suma CSE5230 Tutorial The Na ve ayes lassifier The Apriori Algorithm – a Tutorial by Markus Hegland DATA MINING: Theory and Practices by Dr.Shyam Divakar and Dr.K.P.Soman. Naive Bayes Classifier example - Eric Meisner lecture05-NaiveBayes-2up.pdfAn Improved k-Nearest Neighbor Classification Using Genetic Algorithm ,N. Suguna1, and Dr. K. Thanushkodi2 International Journal of Computer Trends and Technology (IJCTT) - volume4Issue4 –April 2013 ENHANCED DBSCAN ALGORITHM by Priyamvada Paliwal#1, Meghna Sharma. An Introduction to Neural Networks by Vincent Cheung and Kevin Cannons. Jiawei Han and Micheline Kamber, University of lllinois at urbana-champaign, Concepts and Techniques, Data mining, second edition(2006), [3]Barandela, R., Sánchez, J.S., García, V., Rangel, E. Strategies for Learning in Class Imbalance Problems. Pattern Recognition 2003, 36(3), pp.849-851 K.P Soman, Shyam Diwakar, V.Ajay, data mining theory and practice, Amrita Vishwa Vidyapeetham, (2010). Data Mining: Theory and Practices by Dr.Shyam Divakar and Dr.K.P.Soman. Apriori Algorithm Review for Finals Presentation by SE 157B, Spring Semester 2007 Professor Lee By Gaurang Negandhi. PDF-”Using TF-IDF to Determine Word Relevance in Document Queries” by Juan Ramos, Department of Computer Science, Rutgers University, 23515 BPO Way, Piscataway, NJ, 08855 [15] Figure 1 – Schematic drawing made through Rational Rose. Figure 2 to 5- Resulting Snapshots obtained by implementing algorithms Naïve Bayes and KNN. David Posada,Taylor J. Maxwelland Alan R. TempletonTreeScan: a bioinformatic application to search forgeno-type/phenotype associations using haplotype trees, Variagenics, Inc., 60 Hampshire Street, Cambridge, MA 02139, USA and Department of Biology, WashingtonUniversity, St Louis, MO 63130-4899, USAReceived on November 5, 2004; revised on January 10, 2005; accepted on January 25, 2005Advance Access publication January 28, 2005, Vol. 21 no. 9 2005, pages 2130–2132 doi:10.1093/bioinformatics/bti293 [18] AdrienCoulet, MalikaSmaïl-Tabbone, Pascale Benlian, AmedeoNapoliand Marie-Dominique Devignes ,‖ Ontolo-gy-guided data preparation for discovering genotype-phenotype relationships, Address:KIKA Medical, Paris, F-75012, France,LORIA (UMR 7503 CNRS-INPL-INRIA-Nancy2-UHP),Vandoeuvrelès-Nancy, F- 54506, France andUniversi-té Pierre et Marie Curie Paris6, INSERM UMRS 538 Biochimie-BiologieMoléculaire,Paris, F-75571, FranceEmail: AdrienCoulet* adrien.coulet@loria.fr; MalikaSmaïl-Tabbone - malika.smail@loria.fr; Pascale Benlian - pascale.benlian@sat.ap-hop-paris.fr; Amedeo Napoli - amedeo.napoli@loria.fr; Marie-Dominique Devignes – marie-dominique.devignes@loria.frCorresponding author Ming-Huei Chen and Qiong Yang ,‖ GWAF: an R package for genome-wide association analyses withfamily da-ta‖, Department of Neurology, Boston University School of Medicine, Boston, MA 118,The National Heart, Lung, lood Institute’s Framingham Heart Study, Framingham, MA 01702 andDepartment of Biostatistics, BostonUni-versity School of Public Health, Boston, MA 02118, USAReceived on September 28, 2009; revised on December 18, 2009; accepted on December 21, 2009Advance Access publication December 29, 2009, Vol. 26 no. 4 2010, pages 580– 581 doi:10.1093/bioinformatics/btp710. Timothy D. O’ onnor and Nicholas I. Mundy,‖ Genotype–phenotype associations: substitution models to detect evolutionary associations between phenotypic variables andgenotypic evolutionary rate‖, Department of Zoology, Uni-versity of Cambridge, Cambridge B2 3EJ, UK, Vol. 25 ISMB 2009, pages i94– i100doi:10.1093/bioinformatics/btp231. Makio Tamura and PatrikD’haeseleer,‖ Microbial genotype–phenotype mapping by class association rule mining‖, Lawrence Livermore National Laboratory, Computing Applications and Research Department/Chemistry, Materi-als,Earth and Life Sciences Department, Microbial Systems Biology Group, Livermore, CA 94550, USA Received on October 8, 2007; revised on February 28, 2008; accepted on April 26, 2008 Advance Access publication May 8, 2008, Vol. 24 no. 13 2008, pages 1523–1529 doi:10.1093/bioinformatics/btn210

Acknowledgments We would like to thank the anonymous reviewers for their very helpful comments and suggestions and also we extend our gratitude to Dr.Vikas Modi and family Amrita krupa Hospital Mysore, for helpful discussion.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Mining Frequent and Similar Patterns with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) Data Mining Technique Spits Warnars Database, Datawarehouse and Data Mining Research Center, Human Computer Interaction Department, Surya University, Jl. Boulevard Gading Serpong blok O/1 Summarecon Serpong, Tangerang, 15810, INDONESIA. Abstract: Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) is a novel idea which is influenced by Attribute Oriented Induction (AOI) and Emerging Pattern (EP). AOI-HEP discovers patterns such as Total Subsumption HEP (TSHEP), Subsumption Overlapping HEP (SOHEP) and Total Overlapping HEP (TOHEP), include frequent and similar patterns. Mining TSHEP, SOHEP, TOHEP, frequent and similar patterns for each dataset is influenced by learning on high level concept in one of chosen attribute. The experiments used four datasets from UCI machine learning repository and most datasets have SOHEP but not TSHEP and TOHEP and the most rarely found were TOHEP. There are total twenty two High level Emerging Pattern (HEP) where four HEP are TSHEP, sixteen HEP are SOHEP and two HEP are TOHEP, and there are five frequent and four similar patterns from the experiments. Moreover, the experiment showed that adult and breast cancer datasets are interested to mine frequent pattern while breast cancer and IPUMS datasets are interested to mine similar pattern. However, census dataset is not interested to be mined for both frequent and similar patterns. AOI-HEP is suitable for dealing with large dataset since can handle million tuples in dataset in one digit seconds. Keywords: Attribute-oriented induction; Emerging pattern; High Emerging Pattern; Frequent pattern; Similar pattern

I. Introduction This paper proposes Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) [19, 20] (as a hybrid approach which is influenced by two data mining techniques i.e. Attribute Oriented Induction (AOI) [4,11] and Emerging Pattern (EP) [6,7,10,17,18]. AOI influences AOI-HEP by using AOI characteristic rule algorithm which was run twice with two input datasets, derived from the same dataset in order to create two rulesets which are then processed with High level Emerging Pattern (HEP) algorithm. EP influences AOI-HEP by extending growth rate equation and propose HEP algorithm which is not influenced by border-based algorithm. EP was proposed earlier by a border-based algorithm and influences most other EP mining algorithms. The border-based algorithm avoids the long process naive algorithms do to get the counts of all itemsets in a large collection of candidates, by manipulating only borders of some two collections and derive all EPs whose support satisfies a minimum support threshold in dataset [6]. The first proposed AOI-HEP was only to mine Total Subsumption HEP (TSHEP) and Subsumption Overlapping HEP (SOHEP) [19], and was extended to mine frequent pattern from both of TSHEP and SOHEP [20]. Meanwhile, this paper proposes extension AOI-HEP with Total Overlapping HEP (TOHEP), mine both of frequent and similar patterns. Firstly, the HEP algorithm starts with Cartesian product between two rulesets which eliminates rules in rulesets with a metric similarity using the categorization of attribute comparison. Secondly, the output rules between two rulesets from metric similarity are discriminated with growth rate to find ratio of supports between rules from two rulesets. The categorization of attribute comparisons is based on similarity hierarchy level and values which have three options in how they subsume each other. These are Total Subsumption HEP (TSHEP), Subsumption Overlapping HEP (SOHEP) and Total Overlapping HEP (TOHEP). From certain similarity hierarchy level and values, we can mine frequent and similar patterns. The main purpose or motivation of proposing AOI-HEP which is influenced with AOI and EP is to use its typical strength of extracting important high-level emerging knowledge from data. The typical strength of AOI is using concept hierarchy [2] to produce high-level data, and moreover, AOI is recognized as an important mining technique since has been tested successfully against large relational database and can learn different kinds of rules. Meanwhile, the typical strength of EP is using growth rate as ratio of the supports in one dataset to another dataset. In addition, EP is recognized as a powerful mining technique to discriminate datasets. AOI concerns with high level data whereas EP concerns with low level data. The new framework, AOI-HEP, is able to produces high level emerging patterns which discriminates two datasets. AOI-HEP will be better than AOI since AOI-HEP

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pattern results are less than AOI pattern results and AOI-HEP will be better than EP since AOI-HEP pattern results are on high level while EP pattern results are on low level. The paper is organized as follows: Section 2 shows AOI-HEP framework which show combination AOI and HEP. Section 3 describes representation rules and rulesets, TSHEP, SOHEP and TOHEP definitions. Section 4 presents HEP algorithm as implementation part of AOI-HEP framework. Moreover, section 5 defines metric similarity function C{ , } and determine concept mining for TSHEP, SOHEP, TOHEP, frequent and similar patterns. Furthermore, section 6 discusses growth rate function GR{ , } as used in current EP but have two the same or different high level itemset instead of one low level itemset. Meanwhile, section 7 discusses experiments for four UCI repository datasets with each user defined concept hierarchies, include experimental mining for TSHEP, SOHEP, TOHEP, frequent and similar patterns. Conclusion is given in section 8. II. AOI-HEP Framework Figure 1 shows the proposed AOI-HEP framework where traditional AOI characteristic rule algorithm is run twice with two datasets D1 and D2 (horizontal partitions of the dataset). AOI uses concept hierarchy as background knowledge for data generalization. AOI eliminates distinct attributes and tuples until they are less or equal than attribute and rules thresholds respectively [11]. AOI’s outputs are rulesets and from datasets D1 and D2 respectively. Rulesets and are inputs for High level Emerging Pattern (HEP) which include two functions i.e. similarity function C{ , } and growth rate function GR{ , }. The C{ , } function is a metric similarity function which applies cartesian product between rulesets and , and eliminate the cartesian product by determining the type of HEP i.e. either TSHEP, SOHEP or TOHEP [19]. Figure 1. AOI-HEP Framework. Concept hierarchies

D1 D2

High level Emerging Pattern (HEP)

Frequent

AOI Characteristic rule

{

}

{

}

Subsumption Threshold Attribute Threshold

Similar

}

GR{

Overlap Threshold

}

GrowthRate HEP pattern SLV HEP pattern%

GrowthRate Threshold

Rules Threshold

III. HEP Definition For High level Emerging Patterns (HEP), let D1 and D2 be horizontal partitions of some dataset with p attributes and . Rulesets { } and { } from datasets D1 and D2 are represented as in figure 2. In figure 2 each ruleset consists of n rules where n  rules threshold. Each rule in a ruleset is represented by attributes , where | is number of tuples forming the rule and m is the number of attributes in a ruleset as in equation 1. Figure 2 shows the representation of rulesets vertically where and each rule horizontally where . For example we have used rule in ruleset 1 and rule in ruleset 2. where all attributes are member of rule in ruleset 1 and where all attributes are member of rule in ruleset 2. If there are four attributes (m=4 in equation 1) then rule and rule . Figure 2. Representation rule and rulesets.

A. Definition of Total Subsumption HEP (TSHEP) For Total Subsumption HEP (TSHEP) we say rule is totally subsumed by rule if then . This means rule is TSHEP by rule ( , rule is a subset of rule ) if each attribute in is subsumed by each attribute in ( ). Based on example four attributes for rules and if each attribute in is subsumed by each attribute in then . B. Definition of Total Overlapping HEP (TOHEP) Meanwhile, for Total Overlapping HEP (TOHEP) we say rule totally overlaps with rule if then . This means rule is TOHEP with rule ( , rule is overlap with rule ) if

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each attribute in is overlap with each attribute in ( ). Based on example four attributes for rules and , if each attribute in is overlap with each attribute in { } then . C. Definition of Subsumption Overlapping HEP (SOHEP) Moreover, for Subsumption Overlapping HEP (SOHEP) we say rule is subsumed by and overlaps with rule : if and then and . This means rule is SOHEP with rule ( ,rule is a proper-subset of rule ) and ( , rule overlaps with rule ), if some attributes from 1 to m1 in are subsumed by some attributes from 1 to m1 in ( ) and if some attributes from m1+1 to m in are overlap with some attributes from m1+1 to m in ( ), where m1 is the number subsumption attribute and m is the number of attributes in a ruleset as in equation 1. Based on example four attributes for rules and , if the first two attributes in are subsumed by the first two attributes in and certainly the last two attributes in are overlap with the last two attributes in { } then and . IV. HEP Algorithm Figure 3 shows the HEP algorithm as part of AOI-HEP framework in figure 1. The HEP algorithm has inputs such as rulesets and , subs_threshold, overlap_threshold, GR_threshold,num_attr, |D2| , |D1|, Frequent and Similar. The HEP algorithm inputs are in accordance with inputs for HEP in AOI-HEP framework figure 1 where for HEP in figure 1 there are rulesets and inputs, subs_threshold, overlap_threshold, Frequent and Similar for C{ , } function, GR_threshold for GR{ , } function. The three thresholds i.e.: subs_threshold, overlap_threshold and GR_threshold have default value 0 and for subs_threshold and overlap_threshold have maximum value 100. Moreover, num_attr input is the number attributes in rulesets and as m in equation 1. The outputs from HEP algorithm are in accordance with the HEP outputs shown in figure 1 and they are GrowthRate, HEP pattern, SLV and HEP pattern%. The outputs are printed in line 17 in HEP algorithm. Figure 3. AOI-HEP Algorithm. HEP algorithm Input: { } , { }, subs_threshold, overlap_Threshold, GR_threshold, num_attr,|D2|,|D1|, Frequent, Similar Output: growth rate, HEP pattern, SLV, HEP pattern% 1.

While( noAllANY(

2. 3. 4. 5.

{While ( noAllANY( )) {SLV=0, over=0, subs=0, F=0,S=0 for x=1 to num_attr {if [x]== [x] and [x]==”ANY”

[x]==

8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

))

and [x] and

SLV=SLV+2.1, over=over+1,S++

[x]!=”ANY” subsump by

[x]

SLV=SLV+2,

over=over+1

SLV=SLV+0.4,

subs=subs+1

if != [x] and subsump by [x] SLV=SLV+0.5, subs=subs+1,F++} subs_=subs/num_attr*100 over_=over/num_attr*100 if subs_>subs_threshold and over_>over_threshold if subs>0 and over==0 HEP pattern=”TSHEP”, HEP pattern%=subs_ if subs>0 and over>0 HEP pattern=”SOHEP”, HEP pattern%=subs_+over_ if subs==0 and over>0 HEP pattern=”TOHEP”, HEP pattern%=over_ growth rate=( [x+1]/|D2|) / ( [x+1]/|D1|) if growth rate > GR_threshold and/or (Frequent and F==x or F==x-1) and/or (Similar and S<x-1) print growth rate, HEP pattern,SLV,HEP pattern% } }

V. Metric Similarity This section presents the metric similarity function C{ , } between rulesets { } and { }. As mention in section 2, the C{ , } function is a metric similarity function which apply cartesian product between rulesets and , and eliminate the cartesian product by determining type of HEP. The determining type of HEP is applied by summing categorization of attribute comparison value and hierarchy level based on subsumption and overlap thresholds. To derive similarity hierarchy level value (SLV) in the HEP algorithm, firstly, we determine categories of attribute values between the rulesets as shown in figure 4. The categorization is based on similarity hierarchy level and the values shown in equation 1 as LV. Secondly, by summing the attribute categorizations or LV values, we get SLV (equation 1) as the similarity between the two rules. The two steps described above are shown between line numbers 4 and 8 in the HEP algorithm of figure 3.

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Figure 4. Comparing rule 1of ruleset 2 { } and rule 1 of ruleset 1 { }

(1)

SLV=

where: SLV =similarity value based on the similarity of attributes hierarchy level and values M = number of attributes in a ruleset, where m > 1 (number of attributes in concept hierarchies - 1) i = attribute position LVi = categorization of attributes comparison based on similarity hierarchy level and values, and the options are : a. If hierarchy level is different and the attribute in rule of ruleset R2 is subsumed by the attribute in rule of ruleset R1, LV=0.4. b. If hierarchy level is different and the attribute in rule of ruleset R1 is subsumed by the attribute in rule of ruleset R2, LV=0.5. c. If hierarchy level and values are the same and the attributes values are not ANY, LV=2. d. If hierarchy level and values are the same and the attributes values are ANY, LV=2.1. The four categorization of attribute comparisons or LV in equation 1 is based on two main categorizations i.e. subsumption (LV=0.4 or LV=0.5) and overlapping (LV=2 or LV=2.1). For each LV option values 0.4,0.5,2 and 2.1 are user defined number, where option numbers 0.4 and 0.5 as values for subsumption categorization (minimum categorization) and option numbers 2 and 2.1 as values for overlapping categorization (maximum categorization). After the similarity between the two rules (SLV) has been derived, then we can determine type of HEP between TSHEP, SOHEP or TOHEP and mining frequent and similar patterns. A. Mining TSHEP, SOHEP and TOHEP Determining type of HEP between TSHEP, SOHEP or TOHEP is shown between line 12 and 14 in figure 3 which is categorized with variables over and subs. Variable over represents the overlapping (LV=2 or LV=2.1) and variable subs represents the subsumption (LV=0.4 or LV=0.5) which are possibly having increment as shown between line number 5 and 6, and number 7 and 8 in figure 3 respectively. The mining between TSHEP, SOHEP or TOHEP can be filtered when the variables over and subs are limited with over_threshold and subs_threshold as inputs HEP algorithm respectively as shown in line number 11 figure 3. TSHEP and TOHEP are composition subsumption (LV=0.4 or LV=0.5) and overlapping (LV=2.0 or LV=2.1) respectively, whilst SOHEP as composition between subsumption (LV=0.4 or LV=0.5) and overlapping (LV=2.0 or LV=2.1) have minimum and maximum SLV values as shown in figure 5. Figure 5. Composition subsumption and overlapping for mining patterns Frequent Patterns SLV=(m-1)*0.5+0.4

SLV=(m-1)*0.5+2.1

TSHEP SLV=m*0.4

SLV=m*0.5

Similar Patterns SLV=(m-1)*2+0.4

SOHEP SLV=(m-1)*0.4+2

SLV=(m-1)*2.1+0.5

SLV=(m-1)*2.1+2

TOHEP SLV=m*2

SLV=m*2.1

TheLV=0.4 two arrow lines in LV=0.5 figure 5 show the influence of two main categorizations subsumption LV=2 and overlapping. LV=2.1 Subsumption Overlapping The overlapping arrow line shows the influence overlapping from LV=2.1 (maximum value for overlapping categorization) until LV=0.5 (maximum value for subsumption categorization). Whilst subsumption arrow line shows the influence subsumption from LV=0.4 (minimum value for subsumption categorization) until LV=2 (minimum value for overlapping categorization). SLV is categorized as TSHEP when have all subsumption LV values (LV=0.4 or LV=0.5) where minimum and maximum SLV values between m*0.4 and m*0.5. Meanwhile, SLV is categorized as SOHEP when have combination subsumption and overlapping LV values (LV=0.4 or LV=0.5 and LV=2 or LV=2.1) where minimum and maximum SLV values between (m-1)*0.4+2 and (m1)*2.1+0.5. Moreover, SLV is categorized as TOHEP when have all overlapping LV values (LV=2 or LV=2.1) where minimum and maximum SLV values between m*2 and m*2.1.

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B. Mining Frequent pattern Frequent pattern is a combination of feature patterns that appear in dataset with frequency not less than a userspecified threshold [12,13] and the frequent pattern synonym with large pattern was first proposed for market basket analysis in the form of association rules [1]. With frequent pattern we can have strong/sharp discrimination power where have large growth rate and support in target (D2) dataset and other support in contrasting (D1) dataset is small [6,8,14]. From frequent patterns, we can create a discrimination rule and are interested in mining the frequent pattern with strong/sharp discrimination power. In EP, the strength of discrimination power is expressed by its large growth rate and support in target (D2) dataset [6,8]. This is called an essential Emerging Patterns (eEP) [8]. In AOI-HEP, the strength of discrimination power is expressed by its large growth rate and support in target (D2) dataset and expressed by subsumption LV=0.5 where R2 in target (D2) dataset is superset with large support and R1 in contrasting (D1) dataset is subset with low support. Since frequent pattern in AOI-HEP are expressed by value LV=0.5 then frequent pattern can be mined from TSHEP or SOHEP as shown in figure 5, where minimum and maximum SLV values between (m-1)*0.5+0.4 and (m-1)*0.5+2.1, showed that not all frequent pattern have the same LV=0.5 values. Frequent pattern without the same LV=0.5 values, have been allocated to percentage value of (m-1)/m*100 and it is accordance where two parts of objects are similar if they are similar in all features (full matching similarity) or if the percentage of similar features is greater than the 80% [5] or if they are similar in at least 90% of the features [15]. Indeed, frequent similarity subsumption LV=0.5 at percentage value of (m-1)/m*100 shows that at least LV values have greater than (m-1)/m*100. The HEP algorithm in figure 3 shows the process of mining frequent pattern with strong discrimination power, which is executed by giving condition true to input frequent variable. Moreover, variable counter F, will be incremented when have subsumption LV=0.5 as shown in line number 8. In line number 16, if input Frequent variable is true and variable F=x or F=x-1 then the output will be categorized as frequent pattern with strong discrimination power, where x is m in equation 1. F=x represents to TSHEP with full similarity subsumption LV=0.5, while F=x-1 represents to TSHEP or SOHEP with frequent similarity subsumption LV=0.5. C. Mining Similar pattern Similar patterns are interesting to mine because similarity pattern between datasets show the equality pattern which can represent similar behavior patterns. There are many examples of the important similar patterns in data mining process. In business, it is important to discover companies with similar patterns such as similar growth patterns, similar product selling patterns and etc. In education, it is important to discover students with similar patterns such as similar student behavior patterns, similar student progress patterns and etc. In banking system, it is important to discover customer with similar patterns such as similar customer behavior patterns, similar customer loan patterns and etc. Searching similar patterns are important and can be used for segmentation or prediction. For example in banking system, banking segmentation and banking prediction with similar banking transaction could help to show banking transaction prediction, with similar customer behavior patterns could help to uncover fraud, and loan prediction [16]. The similarity patterns can be measured with similarity two or more attributes or by calculating distance with euclidean distance or manhattan distance [3]. In AOI-HEP, similar patterns are shown by overlapping LV=2.0 or LV=2.1 and as shown in figure 5, similar pattern are mined from SOHEP or TOHEP, having minimum and maximum SLV values between (m-1)*2+0.4 and (m-1)*2.1+2. As mentioned before, that two parts of objects are similar if they are similar in all features (full matching similarity) or if the percentage of similar features is greater than the 80% [5] or if they are similar in at least 90% of the features [15]. Therefore, AOI-HEP similar pattern are interested to SOHEP or TOHEP with frequent overlapping LV=2.0 or frequent combination overlapping LV=2.0 and LV=2.1 at percentage value of (m-1)/m*100 where m as in equation 1. However, AOI-HEP similar pattern are not interested to SOHEP or TOHEP with frequent overlapping LV=2.1 at percentage value of (m-1)/m*100, where LV=2.1 is ANY and means nothing. Moreover, AOI-HEP similar pattern are not interested to TOHEP with full similarity overlapping LV=2.1 and shown in line number 1 and 2 in HEP algorithm figure 3 which show the exclusion rule with ANY values in all attributes in rulesets. Similar like frequent pattern, discrimination rule can be created from similar pattern. The HEP algorithm in figure 3 shows the process mining similar pattern is executed by giving condition true to input similar variable. Moreover, variable counter S will be incremented when have overlapping LV=2.1 as shown in line number 5. In line number 16, if input Similar variable is true and variable S<x-1 then the output will be categorized as similar pattern, where x is m in equation 1. S<x-1 represents to SOHEP with frequent similarity overlapping LV=2.1 < x-1 where SOHEP with frequent similarity overlapping LV=2.1 at percentage value of (m-1)/m*100 is not interesting (for instance SOHEP with SLV=2.1+2.1+2.1+0.5). VI. HEP Growth Rate Besides eliminating patterns with similarity function C{ , }, the large number of HEP (Cartesian product between rulesets) is eliminated by the growth rate function GR{ , } with given a GrowthRate threshold. Growthrate is a standard function used in Emerging Patterns (EP) [6], and the difference in our approach is discovering high level emerging pattern with the same or different itemset instead of low level pattern with the

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same itemset. As mentioned in section 3, rulesets are AOI outputs and each of rule in ruleset has as the number of tuples forming the rule (figure 2). Because of rule in ruleset has as the number of tuples, then there is no Jumping High level Emerging Patterns (JHEP), where JHEP is related as a term of Jumping EP (JEP). JEP is EP with support is 0 in one dataset and more than 0 in the other dataset or EP as special type of EP which is having infinite growth rate (  ). Growth rate GR{ , } is shown in figure 1 and in line number 15 in the HEP algorithm in figure 3 is used to discriminate between datasets D2 and D1. This growth rate can be calculated using equation 2. We can define that a HEP is a ruleset whose support changes from one ruleset in dataset D1 to another ruleset in dataset D2. In other words, HEP is a ruleset whose strength of high level rule Y of ruleset R1 in dataset D1 changes to high level rule X of ruleset R2 in dataset D2. Conventionally, this is defined as follows: GR(X,Y) =

(2)

where: X = High level rule of ruleset R2 in dataset D2. Y = High level rule of ruleset R1 in dataset D1. D2 = Dataset D2. D1 = Dataset D1. |D2| = Total number of instances in dataset D2. |D1| = Total number of instances in dataset D1. Count R2(X) = Number of high level rule X of ruleset R2 in dataset D2. Count R1(Y) = Number of high level rule Y of ruleset R1 in dataset D1. Support D2(X) = Composition number of high level rule X of ruleset R2 in D2. Support D1(Y) = Composition number of high level rule Y of ruleset R1 in D1. VII. Experimental evaluation The experiments used four datasets from the UCI machine learning repository: adult, breast cancer, census, and IPUMS datasets with the number of instances 48842, 569, 2458285 and 256932 respectively [9]. Each dataset has concept hierarchies built from five chosen attributes with a minimum concept level of three. The attributes in concept hierarchies for adult dataset include workclass, education, marital-status, occupation, and native-country attributes. The attributes in concept hierarchies for the breast cancer dataset contains attributes i.e. clump thickness, cell size, cell shape, bare nuclei and normal nucleoli attributes. Meanwhile, class, marital status, means, relat1 and yearsch attributes, were given to concept hierarchies for the Census dataset. Finally, the attributes in concept hierarchies for the IPUMS dataset consists of relateg, marst, educrec, migrat5g and tranwork attributes. Each dataset was divided into two sub datasets based on learning the high level concept in one of their attributes. Learning the high level concept in one of their five chosen attributes for concept hierarchies, makes the parameter m in equation 1 has value 4, where value 4 comes from five chosen attributes for concept hierarchies minus 1 and 1 is the attribute for the learning concept. In the adult dataset, we learn by discriminating between the “government” (4289 instances) and “non government” (14 instances) concepts of the “workclass” attribute in datasets D2 and D1 respectively. In the breast cancer dataset, we learn by discriminating between “aboutaverclump” (533 instances) and “aboveaverclump” (289 instances) concepts of the “clump thickness” attribute in datasets D2 and D1 respectively. Meanwhile Census dataset learns “green” (1980 instances) and “no green” (809 instances) concepts of the “means” attribute for datasets D2 and D1 respectively. Finally, the IPUMS dataset learns “unmarried” (140124 instances) and “married” (77453 instances) concepts of the “marst” attribute as datasets D2 and D1 respectively. Experiments were carried out by a java application as shown in figure 6. The experiments were tested on Intel(R) Atom(TM) CPU N550 (1.50 GHz) with 1.00 GB RAM. The AOI-HEP application has an input dataset and corresponding concept hierarchies in the form of flat files respectively. The AOI-HEP application was run 4 times as the number of experimental datasets and with the attribute and rule thresholds 6 which were chosen based on the preliminary experiments done on all datasets such that to get meaningful numbers of rules, a higher threshold is preferable after trial experiments. The AOI algorithm is part of the AOI-HEP application which is combined with the HEP algorithm. Running AOI-HEP application with input adult, breast cancer, census and IPUMS datasets have running time of approximately 3, 3, 4 and 13 seconds respectively. Incredibly, the extraordinary running time of 13 seconds with the input IPUMS dataset happened because IPUMS has huge instances learning dataset’s unmarried and married concepts with 140124 and 77453 instances respectively. In running AOI-HEP application, each dataset has rulesets R2 and R1 based on learning concepts in one chosen of its attribute. Since there is page’s limit for paper publishing, then we limit to rulesets R2 and R1 for only adult dataset as shown in tables 1 and 2 respectively. Rulesets R2 and R1 in table 1 and 2 are the result learning from “government” and “non government” concepts from the same “workclass” attribute of adult dataset. Ruleset R2 or R1 as shown in tables 1 or 2 has 6 tuples

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(rules) include number of instances for each tuple (rule) and has four attributes (m in equation 1) as representation rules and rulesets in figure 2. Table I Ruleset R2 for learning government concept from “workclass” attribute of adult dataset No 0 1 2 3 4 5

Education Intermediate ANY Advanced Advanced Basic Advanced

Marital ANY ANY ANY ANY Married-spouse Married-spouse

Occupation ANY ANY ANY ANY Services Services

Country ANY America Asia Europe Europe Antartica

Number of instances 3454 786 30 17 1 1

Table II Ruleset R1 for learning non government concept from “workclass” attribute of adult dataset No 0 1 2 3 4 5

Education 7th-8th HS-grad HS-grad Assoc-adm Some-college Some-college

Marital Widowed Never-married Married-civ-spouse Married-civ-spouse Married-civ-spouse Married-spouse-absent

Occupation Tools ANY ANY Tools ANY Tools

Country United-states United-states ANY United-states United-states United-states

Number of instances 1 4 5 1 2 1

Figure 6 Screen display for AOI-HEP application

Overall, the results for running the AOI-HEP application for four experimental datasets can be seen in table 5 where the adult dataset has two TSHEP, four SOHEP and no TOHEP, the breast cancer dataset has no TSHEP, two SOHEP and no TOHEP, whilst the census dataset has two TSHEP, six SOHEP and no TOHEP and the IPUMS dataset has no TSHEP, four SOHEP and two TOHEP. Due to page’s limit for paper publishing, then we limit only to adult dataset which has two TSHEP and four SOHEP as shown in tables 3 and 4 respectively. Tables 3 and 4 are outputs which are stated in line number 17 HEP algorithm in figure 3. Tables 3 and 4 have number of growth rates grouped either as TSHEP or TOHEP, where growth rate is discrimination between rulesets R2 and R1 as mentioned in equation 2. Tables 3 and 4 have position rulesets R2(X) and R1(Y), support D2(X), support D1(Y), Growth rate, HEP pattern and HEP%, where parameters X and Y, R2(X), R1(Y), support D2(X) and support D1(Y) refer to equation 2. Position ruleset R2(X) and R1(Y) in tables 3 and 4 refer to position tuple (rule) in tables 1 and 2 respectively since they are from the same dataset (adult dataset), where R2(X) and R1(Y) for learning government and non government concepts respectively from the same “workclass” attribute of adult dataset. Table 5 shows SLV and growth rate values (SLV/growth rate) with equations 1 and 2 whilst figure 7 shows the SLV values composition. Table III TSHEP from adult dataset N o

R2(X) R1(Y)

1 2

0 0

3 5

Support D2(X)

Support D1(Y)

HEP Pattern

HEP %

3454/4289=0.80532 3454/4289=0.80532

1/14=0.07143 1/14=0.07143

11.27442 11.27442

0.5+0.5+0.5+0.5=2 0.5+0.5+0.5+0.5=2

100% 100%

Table IV SOHEP from adult dataset N o 1 2 3 4

R2(X) R1(Y) 0 0 0 1

1 2 4 2

Support D2(X)

Support D1(Y)

HEP Pattern

HEP %

3454/4289=0.80532 3454/4289=0.80532 3454/4289=0.80532 786/4289=0.18326

4/14=0.28571 5/14=0.35714 2/14=0.14286 5/14=0.35714

2.81861 2.25488 5.63721 0.51313

0.5+0.5+2.1+0.5=3.6 0.5+0.5+2.1+2.1=5.2 0.5+0.5+2.1+0.5=3.6 0.5+0.5+2.1+0.4=3.5

75%+25% 50%+50% 75%+25% 75%+25%

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Table V Composition SLV values and Growth rate for four experimental datasets Adult TSHEP SOHEP 2/11.274 3.6/2.818 2/11.274 5.2/2.255 0 3.6/5.637 0 3.5/0.513 0 0 0 0

Breast Cancer SOHEP 3.5/10.302 6.5/0.677 0 0 0 0

Census TSHEP SOHEP 1.8/0.697 5/0.088 1.7/0.057 3.4/0.275 0 3.4/0.123 0 5/0.379 0 4.9/0.272 0 3.2/0.009

IPUMS SOHEP TOHEP 5.1/0.629 8.2/1.530 6.7/3.466 8.2/0.446 5/0.851 0 5/0.261 0 0 0 0 0

Figure 7. Composition SLV values for four experimental datasets

A. Experimental mining TSHEP, SOHEP and TOHEP The graph in figure 7 shows the consistency between minimum and maximum SLV values for TSHEP, SOHEP and TOHEP in figure 5, where TSHEP, SOHEP and TOHEP have small, medium and high SLV values respectively. The graph in figure 7 shows the position TSHEP at the bottom of graph (below SLV=2) which indicates that TSHEP have small SLV values. Firstly, TSHEP for adult and census datasets are consistent where minimum and maximum SLV value between m*0.4 (SLV=4*0.4=1.6) and m*0.5 (SLV=4*0.5=2). The SOHEP position in the middle of the graph (between SLV=3 and SLV=7) indicates that SOHEP have medium SLV values and secondly, SOHEP for all four experimental datasets are consistent where minimum and maximum SLV value between (m-1)*0.4+2 (SLV=(4-1)*0.4+2=3.2) and (m-1)*2.1+0.5 (SLV=(4-1)*2.1+ 0.5= 6.8). Lastly, TOHEP position at the upper part of the graph (above SLV=8) indicates that TOHEP have high SLV values and thirdly, TOHEP for IPUMS dataset is consistent where minimum and maximum SLV value between m*2 (SLV= 4*2=8) and m*2.1 (SLV=4*2.1=8.4). B. Experimental mining frequent pattern The graph in figure 7 shows the consistency between minimum and maximum SLV values for frequent pattern in figure 5, where position frequent pattern between TSHEP and SOHEP. Frequent pattern for all four experimental datasets are consistent where minimum and maximum SLV value between (m-1)*0.5+0.4 (SLV=(4-1)*0.5+0.4=1.9) and (m-1)*0.5+2.1 (SLV=(4-1)*0.5+ 2.1= 3.6). From running results of AOI-HEP application for four experimental datasets in tables 5, there are nine candidate frequent patterns based on minimum and maximum SLV values between 1.9 and 3.6. However, only five frequent patterns as shown in table 6 which fulfilled frequent pattern with strong discrimination power where having large growth rate and support in target (D2) dataset as mentioned in sub section 5.2. For example of frequent pattern which did not fulfil as strong discrimination power is the fourth result in table 4, where support in target (D2) dataset (0.18326) is lower than in contrasting (D1) dataset (0.35714), even it has SLV value=3.5 which fulfilled as frequent pattern and furthermore it has small growth rate (0.513). Tables between 7 and 11 show ruleset relation between rulesets and for each frequent pattern in table 6. Table VI Frequent patterns from four experimental datasets N o 1

Dataset

HEP

Adult

TSHEP

Adult

TSHEP

Adult

SOHEP

Adult

SOHEP

Breast cancer

SOHEP

= Growth rate = 11.2744 = 11.2744 = 2.81861 = 5.63721 = 10.30286

SLV 0.5+0.5+0.5+0.5=2 0.5+0.5+0.5+0.5=2 0.5+0.5+2.1+0.5=3.6 0.5+0.5+2.1+0.5=3.6 2.0+0.5+0.5+0.5=3.5

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Table VII TSHEP in adult dataset for rulesets Rulesets

Education Intermediate Assoc-adm 0.5

to with GR=(3454/4289)/(1/14) = 0.80532/0.07143 = 11.27442

Marital ANY Married-civ-spouse

Occupation ANY Tools

Country ANY United-states

0.5

Instances 3454 1

Table VIII TSHEP in adult dataset for rulesets to with GR=(3454/4289)/(1/14) = 0.80532/0.07143 = 11.27442 Rulesets

Education Intermediate Some-college 0.5

Marital ANY Married-spouse-absent 0.5

Occupation ANY Tools 0.5

Country ANY United-states 0.5

Instances 3454 1

Table IX Frequent subsumptionSOHEP in adult dataset for rulesets GR=(3454/4289)/(4/14) = 0.80532/0.28571 = 2.81861 Rulesets

Education Intermediate HS-Grad

Marital ANY

Occupation ANY

Country ANY

Instances

Never-married

ANY

United-states

0.5

2.1

0.5

Education Intermediate Some-college

Marital ANY

Occupation ANY

Country ANY

Instances 3454

Married-civ-spouse

ANY

United-states

0.5

2.1

0.5

Table XI Frequent subsumptionSOHEP in breast cancer dataset for rulesets GR=(19/533)/(1/289)= 0.03565/0.00346=10.30206 Rulesets

Cell Size VeryLargeSize VeryLargeSize

2.0

Cell Shape ANY smallShape 0.5

Bare Nuclei ANY MediumNuclei 0.5

with

3454

Table X Frequent subsumptionSOHEP in adult dataset for rulesets GR=(3454/4289)/(2/14) = 0.80532/0.14286=5.63721 Rulesets

Normal Nucleoli

Instances

ANY

VeryLargeNucleoli

with

0.5

Here are listing of discriminant rule for each of the frequent pattern in table 6 which are detailed between tables 7 and 11: a. There are 11.2744 growth rate for TSHEP adult dataset with 80.53% frequent pattern in government workclass with intermediate education and 7.14% infrequent pattern in non government workclass with assoc-adm education, married-civ-spouse marital status, tools occupation and from the United States. b. There are 11.2744 growth rates for TSHEP adult dataset with 80.53% frequent pattern in government workclass with an intermediate education and 7.14% infrequent pattern in non government workclass with some college education, married-spouse-absent marital status, tools occupation and from the United States. c. There are 2.81861 growth rates for SOHEP adult dataset with 80.53% frequent pattern in government workclass with an intermediate education and 28.57% infrequent pattern in non government workclass with HS-Grad education, Never-married marital status and from the United States. d. There are 5.63721 growth rates for SOHEP adult dataset with 80.53% frequent pattern in government workclass with intermediate education and 14.28% infrequent pattern in non government workclass with some college education, married-civ-spouse marital status and from the United States. e. There are 10.30206 growth rates for SOHEP breast cancer dataset with 3.56% frequent pattern in AboutAverClump “clump thickness” with VeryLargeSize “Cell Size” and 0.34% infrequent pattern in AboveAverClump “clump thickness” with VeryLargeSize “Cell Size”, SmallShape “Cell shape”, mediumNuclei “Bare Nuclei” and VeryLargeNucleoli “Normal Nucleoli”. Experimental mining similar pattern The graph in figure 7 shows the consistency between minimum and maximum SLV values for similar pattern in figure 5, where position similar pattern between SOHEP and TOHEP. Similar pattern for all four experimental datasets are consistent where minimum and maximum SLV value between (m-1)*2+0.4 (SLV=(41)*2+0.4=6.4) and (m-1)*2.1+2 (SLV=(4-1)*2.1+ 2=8.3). From running results of AOI-HEP application for four experimental datasets in tables 5, there are four similar patterns which are fulfilled minimum and maximum SLV values between 6.4 and 8.3, as shown in table 12. Tables between 13 and 16 show ruleset relation between rulesets and for each similar pattern in table 12.

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Table XII Similar patterns from four experimental datasets No

Dataset

HEP

IPUMS

TOHEP

IPUMS

TOHEP

Breast cancer

SOHEP

IPUMS

SOHEP

SLV

= Growth rate = 1.530 = 0.446

2.1+2.0+2.0+2.1=8.2 2.1+2.0+2.0+2.1=8.2 2.0+2.0+0.4+2.1=6.5

= 0.67777 = 3.46636

2.1+2.0+0.5+2.1=6.7

Table XIII TOHEP in IPUMS dataset for rulesets to with GR=(6356/140124)/(2296/77453)= 0.045/0.029= 1.530 Rulesets

Relateg ANY ANY

2.1

Educrec Primary School Primary School 2.0

Migrat5g Not-known Not-known 2.0

Tranwork ANY ANY 2.1

Instances 6356 2296

Table XIV TOHEP in IPUMS dataset for rulesets to with GR=(4603/140124)/(5706/77453) = 0.033/0.074=0.446 Rulesets

Relateg ANY ANY

2.1

Educrec College College 2.0

Migrat5g Not-known Not-known 2.0

Tranwork ANY ANY 2.1

Instances 4603 5706

Table XV Frequent overlapping SOHEP in breast cancer dataset for rulesets GR=(5/533)/(4/289) = 0.00938/0.01384 =0.67777 Rulesets

Cell Size largeSize largeSize

2.0

Cell Shape VeryLargeShape VeryLargeShape 2.0

Bare Nuclei Normal Nucleoli VeryLargeNuclei ANY ANY ANY 0.4 2.1

Relateg ANY ANY

2.1

Educrec Secondary School Secondary School 2.0

Migrat5g ANY Not-known 0.5

Tranwork ANY ANY 2.1

with

Instances 5 4

Table 16. Frequent overlapping SOHEP in IPUMS dataset for rulesets to GR=(7632/140124)/(1217/77453) = 0.05447/0.01571=3.46636 Rulesets

with

Instances 7632 1217

Here are listing of discrimination rule for each of the similar pattern in table 12 which are detailed between tables 13 and 16: a. There are 1.53 growth rates similar patterns for TOHEP IPUMS dataset with 4.5% unmarried “marital status” and 2.9% Married “marital status” with a similar pattern in the Primary School education and Notknown “Migration status”. b. There are 0.446 growth rates similar patterns for TOHEP IPUMS dataset with 3.3% unmarried “marital status” and 7.4% Married “marital status” with a similar pattern in College education and Not-known “Migration status”. c. There are 0.6777 growth rates similar patterns for SOHEP breast cancer dataset with 0.938% AboutAverClump “clump thickness” and 1.384% AboveAverClump “clump thickness” with similar pattern largeSize “Cell Size” and VeryLargeShape “Cell shape”. d. There are 3.46636 growth rates similar patterns for SOHEP IPUMS dataset with 5.447% unmarried “marital status” and 1.571% Married “marital status” with Not-known “Migration status” and there is a similar pattern in Secondary School education. VIII.Conclusion AOI-HEP has been successfully implemented using four large real datasets from UCI machine learning repository and discovered TSHEP, SOHEP, TOHEP, frequent and similar patterns. The experiments showed that there are five frequent and four similar patterns from twenty two HEP, where all frequent patterns and two similar patterns have strong discrimination rules with growth rate values between 1.530 and 11.2744 respectively. Since AOI-HEP can strongly discriminate high-level data, assuredly AOI-HEP can be implemented to discriminate datasets such as finding bad and good customers for banking loan systems or credit card applicants and etc. Moreover, since AOI-HEP can mine similar patterns, certainly AOI-HEP can be implemented to mine similar patterns, for instance, mining similar customer loan patterns and etc. AOI-HEP can be extended to learn other knowledge patterns such as characteristic, classification, data evolution regularities, association and cluster description. Moreover, AOI-HEP knowledge discovery can be extended to

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mine disjoint as dissimilar pattern, inverse the discovery learning, learning from more than two datasets and learning multidimensional view. In the future, this AOI-HEP should be compared with current data mining technique in order to improve the performance and patterns which can be mined. IX. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11] [12] [13] [14]

[15] [16] [17]

[18] [19] [20] [21] [22]

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