Apply of Deep Learning Techniques to Classify Poisonous Squid and Non-Venomous Squid
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Abstract
This research presents the development model for image classification of poisonous squid and non-venomous squid based on supervised learning deep learning technique. The image dataset to train model contains 200 images. There are 100 images per class and set up epoch equal to 10. We experimentally compare the performance of two algorithms, which are Artificial Neural Network and Convolutional Neural Network. The experiment results show that the classification performance of Convolutional Neural Network is better than Artificial Neural Network in all measures. The model is giving 92.50% Accuracy, 100.00% Precision, 85.00% Recall, and 91.89% F1-score.
The obtained model is used to develop the information system for classification of poisonous squid and non-venomous squid in order that users can take advantage to reduce the dangers of consumption that may occur in the future.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
ลิขสิทธิ์ต้นฉบับที่ได้รับการตีพิมพ์ในวารสารนวัตกรรมวิทยาศาสตร์เพื่อการพัฒนาอย่างยั่งยืนถือเป็นกรรมสิทธิ์ของคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต ห้ามผู้ใดนำข้อความทั้งหมดหรือบางส่วนไปพิมพ์ซ้ำ เว้นแต่จะได้รับอนุญาตอย่างเป็นลายลักษณ์อักษรจากคณะวิทยาศาสตร์และเทคโนโลยี มหาวิทยาลัยสวนดุสิต นอกจากนี้ เนื้อหาที่ปรากฎในบทความเป็นความรับผิดชอบของผู้เขียน ทั้งนี้ไม่รวมความผิดพลาดอันเกิดจากเทคนิคการพิมพ์
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