References
- Alsuhibany SA, Abdel-Khalek S, Algarni A, Fayomi A, Gupta D, Kumar V, Mansour RF, Abd El-Latif AA. 2021. Ensemble of deep learning based clinical decision support system for chronic kidney disease diagnosis in medical internet of things environment. Comput Intell Neurosci. 2021:1–13. doi: 10.1155/2021/4931450.
- Arulanthu P, Perumal E. 2020. An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction. Int J Imaging Syst Technol. 30(3):815–827. doi: 10.1002/ima.22424.
- Asghari P, Rahmani AM, Javadi HS. 2019. A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform. Trans Emerging Tel Tech. 30(6):3637. doi: 10.1002/ett.3637.
- Braik MS. 2021. Chameleon swarm algorithm: a bio-inspired optimizer for solving engineering design problems. Expert Syst Appl. 174:114685. doi: 10.1016/j.eswa.2021.114685.
- Cheng L, Wu XH, Wang Y. 2018. Artificial flora (AF) optimization algorithm. Appl Sci. 8(3):329. doi: 10.3390/app8030329.
- Dermatology Data Set. [ accessed 2023 March.] https://archive.ics.uci.edu/ml/datasets/dermatology.
- Ibrahim M, Elhafiz R. 2023. Modeling an intrusion detection using recurrent neural networks. J Eng Res. 11(1):100013. doi: 10.1016/j.jer.2023.100013.
- Kavitha D, Ravikumar S. 2021. IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring. Trans Emerging Tel Tech. 32(1):e4132. doi: 10.1002/ett.4132.
- Khan MA, Algarni F. 2020. A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access. 8:122259–122269. doi: 10.1109/ACCESS.2020.3006424.
- Liu W, Liu Z. 2011. A method of SVM with normalization in intrusion detection. Procedia Environ Sci. 11:256–262. doi: 10.1016/j.proenv.2011.12.040.
- Mansour RF, El Amraoui A, Nouaouri I, Díaz VG, Gupta D, Kumar S. 2021. Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems. IEEE Access. 9:45137–45146. doi: 10.1109/ACCESS.2021.3066365.
- Medsker LR, Jain LC. 2001. Recurrent neural networks. Design Appl. 5:64–67.
- Nagavelli U, Samanta D, Chakraborty P. 2022. Machine learning technology-based heart disease detection models. J Healthc Eng. 2022:1–9. doi: 10.1155/2022/7351061.
- Obilor EI, Amadi EC. 2018. Test for significance of Pearson’s correlation coefficient. Int J Innovative Math Stat Energy Policies. 6(1):11–23.
- Patro SG, Sahu KK. 2015. Normalization: a preprocessing stage. IARJSET. 20–22. arXiv preprint arXiv:1503.06462 doi: 10.17148/IARJSET.2015.2305.
- Peng S, Han W, Jia G. 2022. Pearson correlation and transfer entropy in the Chinese stock market with time delay. Data Sci Manag. 5(3):117–123. doi: 10.1016/j.dsm.2022.08.001.
- Shah SC, Kumar S 2018. A Markov chain based link lifetime prediction in mobile ad hoc networks. In: 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). p. 28–33. Barcelona, Spain.
- Vellameeran FA, Brindha T. 2022. A new variant of deep belief network assisted with optimal feature selection for heart disease diagnosis using IoT wearable medical devices. Comput Methods Biomech Biomed Engin. 25(4):387–411. doi: 10.1080/10255842.2021.1955360.
- Yadav AK, Tripathi S. 2017. QMRPRNS: design of QoS multicast routing protocol using reliable node selection scheme for MANETs. Peer-To-Peer Netw Appl. 10(4):897–909. doi: 10.1007/s12083-016-0441-8.