Comparative study of IoT- and AI-based computing disease detection approaches
The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the re...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000377 |
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| author | Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik |
| author_facet | Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik |
| author_sort | Wasiur Rhmann |
| collection | DOAJ |
| description | The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. |
| format | Article |
| id | doaj-art-2d2ee8d30f9b4481a9751526a312a4bf |
| institution | Kabale University |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-2d2ee8d30f9b4481a9751526a312a4bf2025-02-08T05:01:25ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-03-018194106Comparative study of IoT- and AI-based computing disease detection approachesWasiur Rhmann0Jalaluddin Khan1Ghufran Ahmad Khan2Zubair Ashraf3Babita Pandey4Mohammad Ahmar Khan5Ashraf Ali6Amaan Ishrat7Abdulrahman Abdullah Alghamdi8Bilal Ahamad9Mohammad Khaja Shaik10Department of Computer Application, Lovely Professional University, Phagwara, Punjab, 144411, India; Corresponding author.Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, 522502, India; Corresponding author.Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, 522502, IndiaSchool of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466114, MP IndiaBabasaheb Bhimrao Ambedkar University, Lucknow, 226025, IndiaCollege of Commerce and Business Administration, Dhofar University, Salalah, 211, OmanFaculty of Computer Studies, Arab Open University, 18211, BahrainBabasaheb Bhimrao Ambedkar University, Lucknow, 226025, India; Department of Computer Application, Shri Ramswaroop Memorial University, Lucknow, 225003, IndiaCollege of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaCollege of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi ArabiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, 522502, IndiaThe emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.http://www.sciencedirect.com/science/article/pii/S2666764924000377Deep learningInternet of Things (IoT)Cloud computingFog computingEdge computing |
| spellingShingle | Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Comparative study of IoT- and AI-based computing disease detection approaches Data Science and Management Deep learning Internet of Things (IoT) Cloud computing Fog computing Edge computing |
| title | Comparative study of IoT- and AI-based computing disease detection approaches |
| title_full | Comparative study of IoT- and AI-based computing disease detection approaches |
| title_fullStr | Comparative study of IoT- and AI-based computing disease detection approaches |
| title_full_unstemmed | Comparative study of IoT- and AI-based computing disease detection approaches |
| title_short | Comparative study of IoT- and AI-based computing disease detection approaches |
| title_sort | comparative study of iot and ai based computing disease detection approaches |
| topic | Deep learning Internet of Things (IoT) Cloud computing Fog computing Edge computing |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000377 |
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