[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119872-en":3,"doc-seo-119872-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119872,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING","Automated fish identification plays a critical role in food safety by distinguishing edible species from poisonous ones. Human visual judgment can lead to mislabeling and risk of food poisoning, especially when fish share similar shapes and sizes. The proposed system uses 300 fish images from 20 species and extracts hybrid features, then applies three supervised machine learning models: k-nearest neighbor, support vector machine, and neural networks. The system is evaluated using precision, sensitivity, F1-score, and accuracy, achieving 91.1%, 92.2%, and 94.4% respectively.","|  |  | Iraqi Journal for Computers and Informatics | Vol. [49], Issue [2], Year (2023 ) |  |  |\n| --- | --- | --- | --- | --- | --- |\n\nEDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING  \nIsraa Mohammed Hassoon1  \n1Department of Mathematics, Collage of Science, mustansiriyah university, Baghdad-Iraq[isrmo9@uomustansiriyah.edu.iq](isrmo9@uomustansiriyah.edu.iq)  \nShaymaa Akram Hantoosh2  \n2Middle Technical University,  \nContinuous Education Center, Baghdad, Iraq [shymmaakram35@mtu.edu.iq](shymmaakram35@mtu.edu.iq)  \nAbstract - Automated fish identification system has a beneficial role in various fields. Fish species can usually be identified based on visual observation and human experiences. False appreciation can cause food poisoning. The proposed system aims to efficiently and effectively identify edible fish from poisonous ones based on three machine learning (ML) techniques. A total of 300 fish images are used, collected from 20 species with differences in shapes, sizes, and colors. Hybrid features were extracted and then fed to three types of ML techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NN). The 300 fish images are divided into two: 70% for training and 30% for testing. The accuracy rates for the presented system were 91.1%, 92.2%, and 94.4% for KNN, SVM, and NNs, respectively. The proposed system is evaluated using four terms: precision, sensitivity, F1-score, and accuracy. Results show that the proposed approach achieved higher accuracy compared with other recent pertinent studies.  \nIndex Terms -Edible Fish, High Order Statistical Features, Machine Learning, Poisonous Fish, Second Order Statistical Features.  \nI. INTRODUCTION  \nVarious types of fish found in seas and rivers occasionally have the same shapes and sizes, resulting in difficulty distinguishing them from one another. Given that consumers are looking for fresh or high-quality fish, the huge challenge facing fishers is the laborious method of manually confirming the fish species with high valuable, fresh, edible, dangerous, invasive, or poisonous. Another challenge is underwater environment, where the task of identifying fish species is challenged by the complexity of the background, water turbidity, and light dispersal in deep water [1] . Distinguishing one fish species from another based on external semblance is still difficult because of the considerable similarity. Current research remains incapable of identifying which fish are dangerous or not. Accordingly, a reliable and authentic identification of fish is crucial to avert fish mislabeling in markets [2]. The best possible techniques for fish authentication are to identify fish species using morphometric and morphological characteristics [3] . Detecting edible, poisonous, or dangerous fish is a complicated and critical task, and the use of wrong information caused by lack of experience may lead to death in many cases; hence, particular knowledge of taxonomy framework and fish biological proprieties are required [4] . The correct identification of fish species (i.e., edible, poisonous  \nfish) helps in economic development and the preservation of human life. Accordingly, technology that can automatically detect poisonous fish must evolve to reduce waste of time and provide considerably accurate results. Machine learning (ML) plays an essential role in classification problems. In ML, computer programs can be updated automatically throughout experience. ML algorithms require large amounts of data and highly consume resources [5]. Pretrained data help to minimize data inadequacy consistent with a particular task. This technology is utilized by scientists to scrutinize large data and to automate complex tasks. ML techniques show better performance than traditional forecasting. Furthermore, ML techniques were used for defect identification in various fields [6] . ML techniques are classified into four classes: supervised, unsupervised, semi-supervised, and rei","cbCaipAxXyaFWcZM","https://ap.wps.com/l/cbCaipAxXyaFWcZM","pdf",895585,1,11,"English","en",105,"# Introduction\n# Related Studies\n# Methods and Materials\n# Experimental Results\n# Conclusions and Future Research","[{\"question\":\"Why is edible fish identification difficult using traditional approaches?\",\"answer\":\"Many fish species share similar external shapes and sizes, making manual recognition challenging. Underwater conditions such as background complexity, turbidity, and light dispersion further increase difficulty.\"},{\"question\":\"What dataset and features does the proposed system use?\",\"answer\":\"The study uses 300 fish images collected from 20 species. Hybrid features are extracted from the images, including second-order and high-order statistical features.\"},{\"question\":\"Which machine learning models are evaluated and what accuracy is achieved?\",\"answer\":\"Three models are tested: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NNs). The reported accuracy rates are 91.1% for KNN, 92.2% for SVM, and 94.4% for NNs.\"}]","EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING | PDF",1785726752,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"edible-fish-identification-based-on-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/edible-fish-identification-based-on-machine-learning/119872/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is edible fish identification difficult using traditional approaches?","Question",{"text":75,"@type":76},"Many fish species share similar external shapes and sizes, making manual recognition challenging. Underwater conditions such as background complexity, turbidity, and light dispersion further increase difficulty.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and features does the proposed system use?",{"text":80,"@type":76},"The study uses 300 fish images collected from 20 species. Hybrid features are extracted from the images, including second-order and high-order statistical features.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are evaluated and what accuracy is achieved?",{"text":84,"@type":76},"Three models are tested: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NNs). 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