International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue IV Apr 2023- Available at www.ijraset.com

DOI: 10.1109/TIP.2020.3043387

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[77] Wang, N., Zheng, H., Zheng, B.: Underwater image restoration via maximum attenuation identification. IEEE Access, 5, 18941-18952 (2017). DOI: 10.1109/ACCESS.2017.2753796

[78] Rossiter, D.G.: Past, present & future of information technology in pedometrics. Geoderma, 324, 131-137 (2018). https://doi.org/10.1016/j.geoderma.2018.03.009

[79] Kapoor, R., Gupta, R., Son, L.H., Kumar, R., Jha, S.: Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization. Multimedia Tools and Applications, 78(16), 23281-23307 (2019). https://doi.org/10.1007/s11042-019-7574-8

[80] Liu, Y., Chen, X., Wang, Z, Wang, Z.J., Ward, R.K., Wang, X.: Deep learning for pixel-level image fusion: Recent advances and future prospects. Information Fusion, 42, 158-173 (2018). https://doi.org/10.1016/j.inffus.2017.10.007

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[83] Shi, Z., Guo, B., Zhao, M., Zhang, C.: Nighttime low illumination image enhancement with single image using bright/dark channel prior. EURASIP Journal on Image and Video Processing, 2018(1), 1-15 (2018). https://doi.org/10.1186/s13640-018-0251-4

[84] Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90 (2018). https://doi.org/10.1016/j.compag.2018.02.016

[85] Murtaza, G., Shuib, L., Abdul Wahab, A.W., Mujtaba, G., Nweke, H.F., Al-garadi, M.A., Zulfiqar, F., Raza, G., Azmi, N.A.: Deep learningbased breast cancer classification through medical imaging modalities: state of the art and research challenges. Artificial Intelligence Review, 53(3), 1655-1720 (2020). https://doi.org/10.1007/s10462-019-09716-5

[86] Tsukada, M., Kondo, M., Matsutani, H.: A neural network-based on-device learning anomaly detector for edge devices. IEEE Transactions on Computers, 69(7), 1027-1044 (2020). DOI: 10.1109/TC.2020.2973631

[87] Gush, T., Bukhari, S.B.A., Haider, R., Admasie, S., Oh, Y.S., Cho, G.J., Kim, C.H.: Fault detection and location in a microgrid using mathematical morphology and recursive least square methods. International Journal of Electrical Power & Energy Systems, 102, 324-331 (2018). https://doi.org/10.1016/j.ijepes.2018.04.009

[88] Zhang, S., Li, X., Zong, M., Zhu, X., Wang, R.: Efficient kNN classification with different numbers of nearest neighbors. IEEE transactions on neural networks and learning systems, 29(5), 1774-1785 (2017). DOI: 10.1109/TNNLS.2017.2673241

[89] Zheng, X., Wang, Y., Wang, G., Liu, J.: Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 107, 55-71 (2018). https://doi.org/10.1016/j.micron.2018.01.010

[90] Ma, L., Li, M., Ma, X., Cheng, L., Du, P., Liu, Y.: A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293 (2017). https://doi.org/10.1016/j.isprsjprs.2017.06.001

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[92] Jimenez, F.P., Miyatake, M.N., Medina, K.T., Perez, G.S., Meana, H.P.: An inverse halftoning algorithms based on neural networks and atomic functions. IEEE Latin America Transactions, 15(3), 488-495 (2017). DOI: 10.1109/TLA.2017.7867599

[93] Halim, A., Wiryawan, B., Loneragan, N.R., Hordyk, A., Sondita, M.F.A., White, A.T., Koeshendrajana, S., Ruchimat, T., Pomeroy, R.S., Yuni, C.: Developing a functional definition of small-scale fisheries in support of marine capture fisheries management in Indonesia. Marine Policy, 100, 238-248 (2019). https://doi.org/10.1016/j.marpol.2018.11.044

[94] Mata, W., Chanmalee, T., Punyasuk, N., Thitamadee, S.: Simple PCR-RFLP detection method for genus-and species-authentication of four types of tuna used in canned tuna industry. Food Control, 108, 106842 (2020). https://doi.org/10.1016/j.foodcont.2019.106842