[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119895-en":3,"doc-seo-119895-105":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},119895,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING - Abstract and Machine Learning Evaluation","Automated fish identification system supports safer food decisions by distinguishing edible species from poisonous ones, reducing the risk of food poisoning caused by visual misjudgment and limited human expertise. A dataset of 300 fish images from 20 species with varying shapes, sizes, and colors is used to build a classification pipeline. Hybrid features are extracted and evaluated with three supervised machine learning methods: k-nearest neighbor, support vector machine, and neural networks. Images are split 70% training and 30% testing, achieving accuracies of 91.1%, 92.2%, and 94.4% respectively, and assessed via precision, sensitivity, F1-score, and accuracy.","|  |  | 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, College of Science, University of Mustansiriyah (UOM), Baghdad-Iraq  \n[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-supervi","cbCaipGa8iGZb2Bg","https://ap.wps.com/l/cbCaipGa8iGZb2Bg","pdf",1789892,1,11,"English","en",105,"# Introduction\n## Related Studies\n## Methods and Materials\n## Experimental Results\n## Conclusions and Future Research","[{\"question\":\"What problem does the proposed system address?\",\"answer\":\"It addresses the difficulty of distinguishing edible fish from poisonous fish, which can lead to food poisoning and even severe harm when mislabeling occurs.\"},{\"question\":\"Which machine learning techniques are used for fish classification?\",\"answer\":\"The study evaluates three techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NNs).\"},{\"question\":\"How is model performance measured and what accuracies are reported?\",\"answer\":\"Performance is evaluated using precision, sensitivity, F1-score, and accuracy. Reported accuracies are 91.1% for KNN, 92.2% for SVM, and 94.4% for NNs.\"}]","EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING - Abstract and Machine Learning Evaluation | PDF",1785726881,28,{"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-abstract-and-machine-learning-evaluation","",{"@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-abstract-and-machine-learning-evaluation/119895/",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},"What problem does the proposed system address?","Question",{"text":75,"@type":76},"It addresses the difficulty of distinguishing edible fish from poisonous fish, which can lead to food poisoning and even severe harm when mislabeling occurs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques are used for fish classification?",{"text":80,"@type":76},"The study evaluates three techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NNs).",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured and what accuracies are reported?",{"text":84,"@type":76},"Performance is evaluated using precision, sensitivity, F1-score, and accuracy. 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