[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126083-en":3,"doc-seo-126083-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126083,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Deep learning and machine learning classification technique for integrated forecasting","Artificial intelligence is leveraged to forecast potential fishing zones (PFZs) to support sustainable smart fisheries. The approach integrates ARIMA and random forest models to locate viable fishing zones in deep marine seas using monitoring data from Indian fishing fleets between 2017 and 2019. Validation with expert label datasets yields 98% detection accuracy, while the proposed system additionally uses salinity and dissolved oxygen to identify suitable zones. Comparative experiments assess machine-learning and deep-learning methods, showing 94% accuracy for CECT versus 92% for a convolutional neural network under an 80% training and 20% testing split.","Deep learning and machine learning classification technique for  \nintegrated forecasting  \nVigilson Prem Monickaraj1, Sterlin Rani Devakadacham2, Nithyadevi Shanmugam3, Nithya Nandhakumar4, Manjunathan Alagarsamy5, Kannadhasan Suriyan6  \n1Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Tamil Nadu, India 2Department of Computer Science and Engineering, R.M.D. Engineering College, Tamil Nadu, India 3Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Tamil Nadu, India 4Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Tamil Nadu, India 5Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Tamil Nadu, India 6Department of Electronics and Communication Engineering, Study World College of Engineering, Tamil Nadu, India  \nArticle history:  \nReceived Dec 10, 2022 Revised Dec 25, 2023 Accepted Jan 27, 2024  \nKeywords:  \nArtificial intelligence Classification technique Convolutional neural network Financial derivatives feature potential fishing zone  \nCorresponding Author:  \nSmart fisheries are increasingly using artificial intelligence (AI) technologies to increase their sustainability. The potential fishing zone (PFZ) forecasts several fish aggregation zones throughout the duration of the prediction in any sea. The autoregressive integrated moving average (ARIMA) and random forest model are used in the current study to provide a technique for locating viable fishing zones in deep marine seas. A significant amount of data was gathered for the database's creation, including monitoring information for Indian fishing fleets from 2017 to 2019. Using expert label datasets for validation, it was discovered that the model's detection accuracy was 98% . Our method uses salinity and dissolved oxygen, two crucial markers of water quality, to identify suitable fishing zones for the first time. In the current research, a system was created to identify and map the quantity of fishing activity. The tests use a number of parameter measurements to evaluate the contrast-enhanced computed tomography (CECT) approach to machine learning (ML) and deep learning (DL) methodologies. The findings showed that the CECT had a 94% accuracy rate compared to a convolutional neural network's 92% accuracy rate for the 80% training data and 20% testing data.  \nThis is an open access article under the CC BY-SA license.  \nVigilson Prem Monickaraj  \nR.M.K. College of Engineering and Technology Chennai, Tamil Nadu, India [Email: vigiprem@gmail.com](Email: vigiprem@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMany parties with interest in the issue are turning to artificial intelligence-based smart fisheries to help alleviate the issue of declining fish populations [1] . Since 2018, the United Nations (UN), the European Unions (EU), and several state governments have proclaimed a purportedly new \"AI era\" [2] . Since 2017, the UN has had an artificial intelligence programme for global governance. In order to make sure that the Sustainable Development Goals (SDGs) benefit everyone and promote the SDGs, AI was utilised to evaluate the SDGs. Because of its superior resources, practical living places, and rich biodiversity, the coastal marine environment is essential to India's economy. India's exclusive economic zone (EEZ), which includes islands and extends 7517 km of coastline, is a key area for research and the utilisation of shared resources. Its totalarea is 2.5 million km2. The marine fishing sector employs about 14 million people and generates revenue by exporting to untapped markets. Despite having a harvestable potential of 3.93 million tonnes, India produces  \naround 2.94 million tonnes of marine fisheries annually [3], [4] . Finding the best fishing spots still presents a challenge for anglers.  \nBoth the maritime characteristics visible in satellite photos and data from ground truth sources ","cbCainrXHfwJIXdL","https://ap.wps.com/l/cbCainrXHfwJIXdL","pdf",399731,9,1,7,"English","en",105,"# Article Info\n# ABSTRACT\n# 1. 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