[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126835-en":3,"doc-seo-126835-105":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},126835,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","IoT Revolutionized - How Machine Learning is Transforming Data, Applications, and Industries","Integrating machine learning (ML) with the Internet of Things (IoT) reveals hidden patterns and insights from large-scale sensor data, helping IoT operate ubiquitously and support intelligent decision-making without explicit programming. ML is positioned as essential for meeting future needs of businesses, governments, and individuals. The paper reviews and categorizes ML-enabled IoT research from data, application, and industry perspectives, highlighting advanced methods and emerging trends while addressing key challenges and opportunities.","IoT Revolutionized: How Machine Learning is Transforming Data, Applications, and Industries  \nTaiwo Abdulahi Akintayo 1, Raphael Aduramimo Olusola 2, Ewemade Cornelius Enabulele 3, Ayodele Oyesanya 4, Samuel Ayanwunmi Olanrewaju 5, Moyosore Owoeye Celestina 6, Balogun Oluwaseyi Sulaimon 7, Ogechukwu Ada Lorretta Anoliefo 8,  \nAyanwunmi Victor Olumide 9, Olamilekan Jamiu Ridwan 10, Adedokun Seyi Adediran 11  \n1 National Centre of Artificial Intelligence and Robotics  \nNo 28, Port Harcourt Crescent, Off Gimbiya Street, P. M. B 564, Area 11, Garki, Abuja, Nigeria 2 Olusegun Agagu University of Science and Technology  \nKm. 6, Okitipupa-Igbokoda Road, P. M. B. 353, Okitipupa, Ondo State, Nigeria 3 The Federal University of Technology Akure  \nP. M. B. 704, Akure, Ondo State, Nigeria 4 Federal University of Technology, Minna  \nGidan kwanu, P.M.B 65, Minna, Niger State, Nigeria  \n5 Yaba College of Technology  \n443 Herbert Macaulay Wy, Abule ijesha, Lagos State, Nigeria  \n6 Federal University, Oye-Ekiti  \nP. M. B 373, Km 3 Oye – Afao Road, Ekiti State, Nigeria  \n7 Tai Solarin University of Education Ijagun Road, Ijebu Ode, Ogun State Nigeria 8 East Tennessee State University  \n1276 Gilbreath Drive 109 Burgin E. Dossett Hall Johnson City, Tennessee, USA  \n9 University ofIlorin Ilorin, Kwara State, Nigera 10 Florida State University  \n222 South Copeland Street, Suite 424, Tallahassee, Florida, USA 11 LadokeAkintola University of Technology  \nOld Oyo/ Ilorin Rd, 210214, Ogbomosho, Oyo-State, Nigeria  \nDOI: 10.22178/pos.105-30  \nLСC Subject Category: T58.5-58.64  \nReceived 21.05.2024 Accepted 25.06.2024 Published online 30.06.2024  \nCorresponding Author: Taiwo Abdulahi Akintayo  \n[taiwoabdulahi15@gmail.com](taiwoabdulahi15@gmail.com)  \nAbstract. Integrating machine learning (ML) with the Internet of Things (IoT) reveals hidden patterns and insights from extensive sensor data, enabling IoT to become omnipresent and make intelligent decisions without explicit programming. ML is essential for IoT to meet the future needs of businesses, governments, and individuals. IoT aims to sense its environment and automate decision-making through intelligent methods, emulating human decisions. This paper reviews and categorises existing literature on ML-enabled IoT from three perspectives: data, applications, and industries. We examine advanced methods and applications by reviewing various sources, emphasising how ML and IoT work together to create more innovative environments. We also discuss emerging trends such as the Internet of Behavior, pandemic management, autonomous vehicles, edge and fog computing, and lightweight deep learning. Furthermore, we identify challenges to  \n© 2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License   \nIoT in four categories: technological, individual, business, and societal. This paper aims to leverage IoT opportunities and address challenges for a more prosperous and sustainable future.  \nKeywords: Internet of Things (IoT); Machine Learning (ML); Sensor Data; Intelligent Decision-Making; Data Analysis; Smart Environments; Internet of Behavior.  \nINTRODUCTION  \nThe Internet of Things (IoT) has transformed how we live and work, generating vast amounts of data from connected devices [1]. Machine Learning (ML) is a critical technology that enables IoT systems to learn from this data and make intelligent decisions without explicit programming [2]. Integrating ML and IoT can potentially revolutionise various industries, including healthcare, manufacturing, and transportation [3]. As IoT devices become increasingly ubiquitous, the amount of sensor data generated continues to grow exponentially [4–7]. ML algorithms can be applied to this data to uncover hidden patterns and insights, enabling IoT systems to become more intelligent and autonomous [8]. However, challenges are associated with integrating ML and IoT, including data quality issues, privacy concerns, and the need for more advance","cbCait5H7HIlj5m0","https://ap.wps.com/l/cbCait5H7HIlj5m0","pdf",586905,1,7,"English","en",105,"# Introduction\n## ML-enabled IoT data growth and decision-making\n# Results and Discussion\n## Data production and data fusion\n## Machine learning for IoT intelligence","[{\"question\":\"How does machine learning enhance Internet of Things systems?\",\"answer\":\"Machine learning enables IoT systems to learn from sensor data and make intelligent decisions without explicit programming, uncovering hidden patterns and predicting events.\"},{\"question\":\"What perspectives does the paper use to review ML-enabled IoT?\",\"answer\":\"It reviews and categorizes existing literature from three perspectives: data, applications, and industries.\"},{\"question\":\"What are key challenges mentioned in integrating ML with IoT?\",\"answer\":\"Challenges include data quality issues, privacy concerns, and the need for more advanced analytics tools, along with the difficulty of managing ubiquitous environments.\"}]","IoT Revolutionized - 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