Edge Cloud IoT Model Based Marine Life Analysis Using
Machine Learning Algorithms

 

 

 

Gagan Kumar Koduru1,*, S. Kalaimagal2, M. Srilakshmi Preethi3, G. L. Narasamba Vanguri4,
Shivanadhuni Spandana5, M. Syed Rabiya6, M. Rajesh7

 

1Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India

 

2Professor, Department of AI & DS, Panimalar Engineering College, Chennai, Tamil Nadu, India

 

3Assistant Professor, CSE-Cyber Security, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India

 

4Assistant Professor, Department of Information Technology, Aditya University, Surampalem, Andhra Pradesh, India

 

5Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500043, Telangana, India

 

6Assistant Professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

 

7Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India

 

Text Box: Abstract 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. Emails: gagan.koduru@gmail.com; drsivamunikalaimagal@gmail.com; preethinaveen22@gmail.com; gayatrijeedigunta05@gmail.com; s.spandana@klh.edu.in; drsyedrabiyam@veltech.edu.in; rajesmano@gmail.com

 

Received: March 30, 2025 Revised: June 07, 2025 Accepted: July 17, 2025

Keywords: Marine life analysis; Remote sensing image; Edge cloud IoT; Machine learning algorithms; Fuzzy encoder