Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR
Abstract Rapid advancements in healthcare technologies necessitate efficient and secure remote patient monitoring systems. This research develops an intelligent system that combines ANN technology and 5G infrastructure with MCDM methods based on Choquet Integral Fuzzy VIKOR to improve medical data a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93829-1 |
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| author | Seelammal Chinnaperumal Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Doaa Sami Khafaga Sekar Kidambi Raju Marwa M. Eid El-Sayed M. El-kenawy |
| author_facet | Seelammal Chinnaperumal Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Doaa Sami Khafaga Sekar Kidambi Raju Marwa M. Eid El-Sayed M. El-kenawy |
| author_sort | Seelammal Chinnaperumal |
| collection | DOAJ |
| description | Abstract Rapid advancements in healthcare technologies necessitate efficient and secure remote patient monitoring systems. This research develops an intelligent system that combines ANN technology and 5G infrastructure with MCDM methods based on Choquet Integral Fuzzy VIKOR to improve medical data acquisition processes. Physical Layer Security (PLS) is a main emphasis point since it protects transmitted healthcare data from eavesdroppers and cyber intruders. The proposed model implements Reinforcement Learning with Hyper-parameter tuning and Lasso regression to obtain a 97.25% accuracy level, which exceeds Physical-Layer Authentication with Superimposed Independent authentication Tags PLA-SIT (97%), Flexible Physical Layer Authentication FPLA (96.8%) and Privacy-Embedded Lightweight and Efficient Automated PLA (95.3%). The proposed model surpasses both CNN-based mechanisms by 94.7%, Shamir’s Secret Sharing Algorithm by 90.7%, and the Blowfish Algorithm by 82.3%. The enhanced quality of service alongside reliability produces the model as a dependable solution for MIoT applications that will exist in the next generation. |
| format | Article |
| id | doaj-art-7d8a7abb5af6408b8cf298e33acf2679 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7d8a7abb5af6408b8cf298e33acf26792025-08-20T02:52:17ZengNature PortfolioScientific Reports2045-23222025-03-0115113210.1038/s41598-025-93829-1Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKORSeelammal Chinnaperumal0Muthusamy Periyasamy1Amel Ali Alhussan2Subhash Kannan3Doaa Sami Khafaga4Sekar Kidambi Raju5Marwa M. Eid6El-Sayed M. El-kenawy7Department of Computer Science and Engineering, Solamalai College of EngineeringDepartment of Cyber Security, Paavai Engineering College (Autonomous)Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityK. Ramakrishnan College of Engineering (Autonomous)Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversitySchool of Computing, SASTRA Deemed UniversityJadara Research Center, Jadara UniversityApplied Science Research Center, Applied Science Private UniversityAbstract Rapid advancements in healthcare technologies necessitate efficient and secure remote patient monitoring systems. This research develops an intelligent system that combines ANN technology and 5G infrastructure with MCDM methods based on Choquet Integral Fuzzy VIKOR to improve medical data acquisition processes. Physical Layer Security (PLS) is a main emphasis point since it protects transmitted healthcare data from eavesdroppers and cyber intruders. The proposed model implements Reinforcement Learning with Hyper-parameter tuning and Lasso regression to obtain a 97.25% accuracy level, which exceeds Physical-Layer Authentication with Superimposed Independent authentication Tags PLA-SIT (97%), Flexible Physical Layer Authentication FPLA (96.8%) and Privacy-Embedded Lightweight and Efficient Automated PLA (95.3%). The proposed model surpasses both CNN-based mechanisms by 94.7%, Shamir’s Secret Sharing Algorithm by 90.7%, and the Blowfish Algorithm by 82.3%. The enhanced quality of service alongside reliability produces the model as a dependable solution for MIoT applications that will exist in the next generation.https://doi.org/10.1038/s41598-025-93829-1Remote healthcare monitoringHealthcare authenticationMedical IoTPhysical layer security5G networksLiteNet (CNN) |
| spellingShingle | Seelammal Chinnaperumal Muthusamy Periyasamy Amel Ali Alhussan Subhash Kannan Doaa Sami Khafaga Sekar Kidambi Raju Marwa M. Eid El-Sayed M. El-kenawy Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR Scientific Reports Remote healthcare monitoring Healthcare authentication Medical IoT Physical layer security 5G networks LiteNet (CNN) |
| title | Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR |
| title_full | Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR |
| title_fullStr | Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR |
| title_full_unstemmed | Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR |
| title_short | Secure and intelligent 5G-enabled remote patient monitoring using ANN and Choquet integral fuzzy VIKOR |
| title_sort | secure and intelligent 5g enabled remote patient monitoring using ann and choquet integral fuzzy vikor |
| topic | Remote healthcare monitoring Healthcare authentication Medical IoT Physical layer security 5G networks LiteNet (CNN) |
| url | https://doi.org/10.1038/s41598-025-93829-1 |
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