IOT enabled hybrid model with learning ability for E-health care systems
One of the most cutting-edge technologies over the years is the Internet of Things (IoT), which is a major force behind the paradigm shift away from conventional medical practises. The goal of IoT-based eHealth is to provide healthcare services that are more effective and individualised through cont...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266591742200201X |
| _version_ | 1811303611195457536 |
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| author | Nagendra Singh S.P. Sasirekha Amol Dhakne B.V. Sai Thrinath D. Ramya R. Thiagarajan |
| author_facet | Nagendra Singh S.P. Sasirekha Amol Dhakne B.V. Sai Thrinath D. Ramya R. Thiagarajan |
| author_sort | Nagendra Singh |
| collection | DOAJ |
| description | One of the most cutting-edge technologies over the years is the Internet of Things (IoT), which is a major force behind the paradigm shift away from conventional medical practises. The goal of IoT-based eHealth is to provide healthcare services that are more effective and individualised through continuous data exchange between linked devices and enhanced data analytics. The IoT and decision-making systems are the main areas of focus of this programme, which seeks to deliver intelligent and proactive healthcare. By considering the huge array of physiological characteristics and applying potent analytical tools like cluster analysis, it is possible to obtain more insight into health-data. In this study, e-health technologies and remote patient monitoring were developed to assist patients in avoiding hospital visits, especially during viral epidemics. This project will use IoT and artificial intelligence (AI) technology to address these problems. The study's objective is to select the most appropriate and effective number of hidden layers and activation function types for the deep net (NN). Describe the patient data sent using IoT protocols next. NN analyses the information from the patient's medical sensors to choose the optimal option. The diagnosis is then communicated to the physician. The proposed technology enables patients to autonomously recognise and forecast the sickness while also supporting clinicians in remote disease discovery and analysis without requiring patients to attend the hospital. |
| first_indexed | 2024-04-13T07:51:00Z |
| format | Article |
| id | doaj.art-2b4af43a48d4455486d86d249681f145 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-13T07:51:00Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-2b4af43a48d4455486d86d249681f1452022-12-22T02:55:32ZengElsevierMeasurement: Sensors2665-91742022-12-0124100567IOT enabled hybrid model with learning ability for E-health care systemsNagendra Singh0S.P. Sasirekha1Amol Dhakne2B.V. Sai Thrinath3D. Ramya4R. Thiagarajan5Department of Electrical Engineering, Trinity College of Engineering and Technology, Karimnagar, Telangana, India; Corresponding author.Department of CSE, Karpagam Academy of Higher Education, Eachanari, Coimbatore, IndiaDepartment of Computer Engineering, Dr. D. Y. Patil Institute of Engineering, Management and Research, Akurdi, Pune SPPU, Pune, IndiaDepartment of EEE, Sree Vidyanikethan Engineering College, Tirupati, IndiaDepartment of EEE, Sathyabama Institute of Science and Technology, Chennai, IndiaDepartment of IT Prathyusha Engineering College, IndiaOne of the most cutting-edge technologies over the years is the Internet of Things (IoT), which is a major force behind the paradigm shift away from conventional medical practises. The goal of IoT-based eHealth is to provide healthcare services that are more effective and individualised through continuous data exchange between linked devices and enhanced data analytics. The IoT and decision-making systems are the main areas of focus of this programme, which seeks to deliver intelligent and proactive healthcare. By considering the huge array of physiological characteristics and applying potent analytical tools like cluster analysis, it is possible to obtain more insight into health-data. In this study, e-health technologies and remote patient monitoring were developed to assist patients in avoiding hospital visits, especially during viral epidemics. This project will use IoT and artificial intelligence (AI) technology to address these problems. The study's objective is to select the most appropriate and effective number of hidden layers and activation function types for the deep net (NN). Describe the patient data sent using IoT protocols next. NN analyses the information from the patient's medical sensors to choose the optimal option. The diagnosis is then communicated to the physician. The proposed technology enables patients to autonomously recognise and forecast the sickness while also supporting clinicians in remote disease discovery and analysis without requiring patients to attend the hospital.http://www.sciencedirect.com/science/article/pii/S266591742200201XNeural networksSensorsMedical-careMonitoringIoT devices and eHealth services |
| spellingShingle | Nagendra Singh S.P. Sasirekha Amol Dhakne B.V. Sai Thrinath D. Ramya R. Thiagarajan IOT enabled hybrid model with learning ability for E-health care systems Measurement: Sensors Neural networks Sensors Medical-care Monitoring IoT devices and eHealth services |
| title | IOT enabled hybrid model with learning ability for E-health care systems |
| title_full | IOT enabled hybrid model with learning ability for E-health care systems |
| title_fullStr | IOT enabled hybrid model with learning ability for E-health care systems |
| title_full_unstemmed | IOT enabled hybrid model with learning ability for E-health care systems |
| title_short | IOT enabled hybrid model with learning ability for E-health care systems |
| title_sort | iot enabled hybrid model with learning ability for e health care systems |
| topic | Neural networks Sensors Medical-care Monitoring IoT devices and eHealth services |
| url | http://www.sciencedirect.com/science/article/pii/S266591742200201X |
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