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Research Article |

Parallel Mirrors Based Marine Predator Optimization Algorithm with Deep Learning Model for Quality and Shelf-Life Prediction of Shrimp

Author(s): K. Prema1 and J. Visumathi2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2

Publisher : FOREX Publication

Published : 30 April 2023

e-ISSN : 2347-470X

Page(s) : 262-271

DOI: https://doi.org/10.37391/IJEER.110204

Abstract

Automatic classification and assessment of shrimp freshness plays a major role in aquaculture industry. Shrimp is one of the highly perishable seafood, because of its flavor and excellent nutritional content. Given the high amount of industrial production, determining the freshness of shrimp quickly and precisely is difficult. Instead of using feature-engineering-based techniques, a novel hybrid classification approach is proposed by combining the strength of convolutional neural networks (CNN) and Marine Predators Algorithm (MPA) for shrimp freshness diagnosis. In order to choose the best hyperparameter values, marine predator algorithm is improved using Parallel Mirrors Technique (PMPA). The proposed methodology employs a pretrained CNN model termed EfficientNet (ENet), which is combined with the PMPA algorithm to form the PMPA-ENet architecture. The proposed approach yields high performance while also reducing computational complexity. The result showed that proposed model achieved an accuracy and F-score of 98.62% and 97.25% for assessment of freshness in shrimp. PMPA's effectiveness in determining optimal values is compared to four different meta-heuristic algorithms hybridized with ENet including Particle Swarm Optimization (PSO), Simple Genetic Algorithm (SGA), Whale Optimization Algorithm (WOA), and traditional Marine Predator Algorithm (MPA). The results indicated that PMPA-ENet algorithm provides better classification compared with other algorithms

Keywords: Deep learning, Hyperparameters optimization, Parallel mirrors technique, Marine predator’s algorithm, Shrimp freshness diagnosis.




K. Prema*, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, India; Email: premak@veltech.edu.in

J. Visumathi, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, India

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K. Prema and J. Visumathi (2023), Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease PredictionParallel Mirrors Based Marine Predator Optimization Algorithm with Deep Learning Model for Quality and Shelf-Life Prediction of Shrimp. IJEER 11(2), 262-271. DOI: 10.37391/IJEER.110204.