2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 227-237, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Gagan Kumar Koduru 1 * , S. Kalaimagal 2 , M. Srilakshmi Preethi 3 , G. L. Narasamba Vanguri 4 , Shivanadhuni Spandana 5 , M. Syed Rabiya 6 , M. Rajesh 7
Doi: https://doi.org/10.54216/JISIoT.180117
The amount of marine data is such that it is pointless, and at times infeasible, to attempt training deep learning models on personal workstations. In this work, we present the advantages of cloud based distributed learning in training of deep learning (DL) model and management of big data. Moreover, large volumes of marine big data are classically through wire networks, which are costly, if at all deployable, to maintain. This research propose novel technique in marine life analysis based on remote sensing image using edge cloud IoT model and machine learning algorithms. Here the edge cloud IoT model has been used for collecting remote sensing image in marine life analysis. This remote sensing image has been processed for noise removal as well as normalization. Then this image is feature extracted as well as classified utilizing principal Gaussian convolutional fuzzy encoder with Bayesian reinforcement Markova algorithm. Experimental analysis has been carried out in terms of classification accuracy, average precision, recall, F1 score, AUC for various marine life dataset. proposed technique obtained 97% Classification accuracy, 95% Average precision, 93% Recall, 88% AUC, 94% F1 SCORE.
Marine life analysis , Remote sensing image , Edge cloud IoT , Machine learning algorithms , Fuzzy encoder
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