Hybrid Approach of CNN and SVM for Shrimp Freshness Diagnosis in Aquaculture Monitoring System using IoT based Learning Support System,ERICDATA高等教育知識庫
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篇名
Hybrid Approach of CNN and SVM for Shrimp Freshness Diagnosis in Aquaculture Monitoring System using IoT based Learning Support System
並列篇名
Hybrid Approach of CNN and SVM for Shrimp Freshness Diagnosis in Aquaculture Monitoring System using IoT based Learning Support System
作者 K. PremaJ. Visumathi
英文摘要

Intelligent monitoring and spoilage detection of meat products is one of the most efficient approach which ensures that the food is consumed when it is fresh and avoids health hazards. Shrimp is most popular in terms of nutrition and exquisite nature. Shrimp has its own biochemical components like protein, carbohydrate, lipid and amino acids. However, the quality and freshness of shrimp is hindered in the post-harvested phase due to storage, handling and processing. The objective of this work is to propose an IoT- enabled real time vision-based support system for diagnosis of shrimp freshness, which is capable of performing assessment of quality and freshness using effective deep learning framework based on convolutional neural networks (CNN) and Support Vector Machine (SVM). The proposed model was measured with metrics such as precision, accuracy, F1 score which is respectively compared with the classical model (CNN with SoftMax) respectively. The comparisons shows that the hybrid model achieves 96.2% which is better than the classic model 94.7%. Based on this, it is observed that hybrid model using CNN and SVM found to be a better approach, which makes a difference to decrease the quality misfortune and help in advancement of criticism framework in industry 4.0.

 

起訖頁 801-810
關鍵詞 Convolutional neural network (CNN)Deep learning (DL)Shrimp freshness diagnosisSupport vector machine (SVM)
刊名 網際網路技術學刊  
期數 202207 (23:4期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022072304015  複製DOI
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