Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3947 2019 2019 Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India Gagan Gagan Professor, Department of AI & DS, Panimalar Engineering College, Chennai, Tamil Nadu, India S. Kalaimagal Assistant Professor, CSE-Cyber Security, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India M. Srilakshmi Preethi Assistant Professor, Department of Information Technology, Aditya University, Surampalem, Andhra Pradesh, India G. L. Narasamba Vanguri Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India Shivanadhuni Spandana Assistant Professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India M. Syed Rabiya Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India M. Rajesh 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. 2026 2026 227 237 10.54216/JISIoT.180117 https://www.americaspg.com/articleinfo/18/show/3947