Abstract
Having enhancement of automation of sensor, huge information such as the big data are examined and has become yet another worldview for huge scope of data handling. The is a requirement of continuous investigation for getting to and handling huge information in a quick manner. High health care costs and large infected population costs alongside the expansion of Information and correspondence innovation, prompted the improvement of the frameworks of health are monitored. The research article shows in the improvement of a novel cloud-based framework of a human service where the Wireless Body Area Networks (WBAN) gives out the total the information to an individual server. This server includes Fuzzy Brain Storm Optimization (FBSO) and Fuzzy Inference System (FIS) which is a distributed real-time computation system. This is used for processing large volumes of high-velocity data. It also is constant calculation framework and the Fuzzy induction framework. The proposed framework is said to be the Improved framework using Fuzzy Brain Storm Optimization (FBSO) and Fuzzy Inference System (FIS) for healthcare (IFFFH) are facilitated on a private cloud, along these lines security and versatility are guarantee. The stream examination is performed on the physiological information, where removal of non-basic information is done and the basic information are put away and a warning is sent to a doctor or the guardians of the person who is under monitorisation. Subsequently constant investigation along with the help of the cloud to improve the adequacy of the framework of the healthcare system (HcS) and personal satisfaction through the help of medical assistance. This can be used for future reference and by expanding the qualities in the dataset, in this manner expanding the quantity of records for preparing the fuzzy framework, the precision can be expanded.
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References
Adam, E.E.B.: Evaluation of fingerprint liveness detection by machine learning approach-a systematic view. J. ISMAC 3(01), 16–30 (2021)
Belle A, Thiagarajan R, Soroushmehr SM, Navidi F, Beard DA, Najarian K (2015). Big data analytics in healthcare. BioMed. Res. Int. 2015
Bruser, C., Diesel, J., Zink, M.D., Winter, S., Schauerte, P., Leonhardt, S.: Automatic detection of atrial fibrillation in cardiac vibration signals. IEEE J Biomed. Health Inform. 17(1), 162–171 (2012)
De Carvalho, FDA., Irpino, A., & Verde, R. (2015). Fuzzy clustering of distribution-valued data using an adaptive L 2 Wasserstein distance. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE.
Chawla, N.V., Davis, D.A.: Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Internal. Med 28(3), 660–665 (2013)
Chen, J.-Z., Lai, K.-L.: Deep convolution neural network model for credit-card fraud detection and alert. J. Artif. Intell. 3(02), 101–112 (2021)
Chen, J.I.Z., Zong, J.I.: Automatic vehicle license plate detection using k-means clustering algorithm and CNN. J. Electric Eng. Autom 3(1), 15–23 (2021)
Cheng, C.W., Chanani, N., Venugopalan, J., Maher, K., Wang, M.D.: icuARM-An ICU clinical decision support system using association rule mining. IEEE J. Trans Eng. Health. Med. 1, 4400110–4400110 (2013)
Denny, J.C.: Mining electronic health records in the genomics era. PLoS Comput. Biol 8(12), e1002823 (2012)
Forkan, A., Khalil, I., Tari, Z.: CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Generation Comput. Syst. 35, 114–127 (2014)
Gong, Y., Fang, Y., Guo, Y.: Private data analytics on biomedical sensing data via distributed computation. IEEE/ACM Trans Comput Biol Bioinform 13(3), 431–444 (2016)
Havens, T.C., Bezdek, J.C., Leckie, C., Hall, L.O., Palaniswami, M.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst 20(6), 1130–1146 (2012)
Jee, K., Kim, G.H.: Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthc. Inform. Res. 19(2), 79–85 (2013)
Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nature Rev. Genetics 13(6), 395–405 (2012)
Lopez, D., & Gunasekaran, M. (2015). Assessment of vaccination strategies using fuzzy multi-criteria decision making. In: Proceedings of the fifth international conference on fuzzy and neuro computing (FANCCO-2015), pp. 195–208. Springer, Cham.
McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D.: Big data: the management revolution. Harvard. Business. Rev. 90(10), 60–68 (2012)
Smys, S., Bashar, A., Haoxiang, W.: Taxonomy classification and comparison of routing protocol based on energy efficient rate. J. ISMAC 3(02), 96–110 (2021)
Stojanovic, J., Gligorijevic, D., Radosavljevic, V., Djuric, N., Grbovic, M., Obradovic, Z.: Modeling healthcare quality via compact representations of electronic health records. IEEE/ACM Trans Comput Biol. Bioinform 14(3), 545–554 (2016)
Sufi, F., Khalil, I.: Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining-based approach. IEEE Trans Inform Technol Biomed 15(1), 33–39 (2010)
Suma, V.: Community based network reconstruction for an evolutionary algorithm framework. J. Artif. Intell. 3(01), 53–61 (2021)
Sungheetha, A., Sharma, R.: Fuzzy chaos whale optimization and BAT integrated algorithm for parameter estimation in sewage treatment. J. Soft Comput. Paradigm (JSCP) 3(01), 10–18 (2021a)
Sungheetha, A., Sharma, R.: Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. J. Trends Comput. Sci. Smart Technol (TCSST) 3(02), 81–94 (2021b)
Viceconti, M., Hunter, P., Hose, R.: Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health. Inform. 19(4), 1209–1215 (2015)
Vijayakumar, T., Vinothkanna, M.R., Duraipandian, M.: Fusion based feature extraction analysis of ECG signal interpretation–a systematic approach. J Artif. Intell. 3(01), 1–16 (2021)
Wu, D., Mendel, J. M., & Joo, J. (2010). Linguistic summarization using if-then rules. In: International Conference on Fuzzy Systems, pp 1–8. IEEE.
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Purandhar, N., Ayyasamy, S. & Saravana Kumar, N.M. Strategic real time framework for healthcare using fuzzy C-means systems. Autom Softw Eng 29, 17 (2022). https://doi.org/10.1007/s10515-021-00302-0
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DOI: https://doi.org/10.1007/s10515-021-00302-0