P. Anantha Prabha1,*, G. Suchitra2, R. Saravanan3
Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3065-3079, 2023, DOI:10.32604/iasc.2023.030017
Abstract Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist. Manual observation and identification take time and are always contingent on the involvement of experts. A system is proposed to alleviate this challenge that uses transfer learning techniques to classify the cephalopods automatically. In the proposed method, only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition. First, the efficiency of the chosen models is determined by evaluating their performance and comparing the findings. Second, the models are fine-tuned by adding dense layers and tweaking hyperparameters to improve… More >
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