Industrial Fish Classifier with Deep Artificial Neural Network

Today, machine learning-based decision support systems play a facilitating role in almost every aspect of life. With the integration of these intelligent systems into industrial production systems, fast and effective production solutions emerge. Today, machine learning-based artificial intelligence technologies mostly offer solutions based on the image processing approach. In this study, a new artificial intelligence model that can effectively classify different fish species is proposed with the YOLO image processing algorithm, a deep artificial neural network based on the image processing approach. A real-time land support solution that can be easily integrated into industrial applications is presented with the model trained with an original fish data set.

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Publication Details

Journal
Orclever Proceedings of Research and Development
Published
2024-12-31
DOI
https://doi.org/10.56038/oprd.v5i1.504
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

Industrial Fish Classifier with Deep Artificial Neural Network

Ersin Alaybeyoğlu, Yasemin Erkan, Mahamat Ahmat Issamadine
1 links
Orclever Proceedings of Research and Development
Water Quality Monitoring Technologies
article

Industrial Fish Classifier with Deep Artificial Neural Network

Ersin Alaybeyoğlu, Yasemin Erkan, Mahamat Ahmat Issamadine
article en

Abstract

Today, machine learning-based decision support systems play a facilitating role in almost every aspect of life. With the integration of these intelligent systems into industrial production systems, fast and effective production solutions emerge. Today, machine learning-based artificial intelligence technologies mostly offer solutions based on the image processing approach. In this study, a new artificial intelligence model that can effectively classify different fish species is proposed with the YOLO image processing algorithm, a deep artificial neural network based on the image processing approach. A real-time land support solution that can be easily integrated into industrial applications is presented with the model trained with an original fish data set.

Orclever Proceedings of Research and DevelopmentVol. 5(1)
Bartin University (TR)
Openalex Percentile: Top 75%
Water Quality Monitoring Technologies
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