A NOVEL ANALYSIS OF MACHINE LEARNING WITH HEALTHCARE TECHNOLOGIES USING IOT

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The Internet of Things (IoT) is a modern-day technology that combines a variety of computing technologies, objects, animals, and humans. The items in the IoT framework communicate with one another and are given unique numbers to identify them. The identifying system establishes network connectivity and functions without the use of a centralized system. Sensor network advancements have enabled automation in a variety of fields, and the integration of soft computing technologies into the IoT system has enabled effective decision-making. The items in the IoT system act intelligently and carry out intelligent actions. Through connected devices, IoT-based technology improves people's daily lives and makes living things context-aware. The data collected from sensors will be processed using computational methods, resulting in accurate forecasts. Recent applications and soft computing algorithms are discussed in this article. Recent applications and soft computing algorithms are reviewed.

References

[1]. Saha, H. N., Mandal, A., & Sinha, A. (2017, January). Recent trends in the Internet of Things. In 2017 IEEE 7th annual computing and communication workshop and conference (CCWC) (pp. 1-4). IEEE.
[2]. Saha, H. N., Saha, N., Ghosh, R., & Roychoudhury, S. (2016, October). Recent trends in implementation of Internet of Things—A review. In 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 1-6). IEEE.
[3]. Ning, H., & Hu, S. (2012). Technology classification, industry, and education for Future Internet of Things. International journal of communication systems, 25(9), 1230-1241.
[4]. Li, S., Da Xu, L., & Zhao, S. (2018). 5G Internet of Things: A survey. Journal of Industrial Information Integration, 10, 1-9.
[5]. Mendez Mena, D., Papapanagiotou, I., & Yang, B. (2018). Internet of things: Survey on security. Information Security Journal: A Global Perspective, 27(3), 162-182.
[6]. Kodali, R. K., Swamy, G., & Lakshmi, B. (2015, December). An implementation of IoT for healthcare.In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 411-416). IEEE.
[7]. Kodali, R. K., Swamy, G., & Lakshmi, B. (2015, December). An implementation of IoT for healthcare.In 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 411-416). IEEE.
[8]. Niitsu, K., Kobayashi, A., Nishio, Y., Hayashi, K., Ikeda, K., Ando, T., ... & Nakazato, K. (2018). A self- powered supply-sensing biosensor platform using bio fuel cell and low-voltage, low-cost CMOS supply- controlled ring oscillator with inductive-coupling transmitter for healthcare IoT. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(9), 2784-2796.
[9]. Yang, G., Xie, L., Mäntysalo, M., Zhou, X., Pang, Z., Da Xu, L., ... & Zheng, L. R. (2014). A health-IoT platform based on the integration of intelligentpackaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE transactions on industrial informatics, 10(4), 2180-2191.
[10]. Prakash, R., & Ganesh, A. B. (2019). Internet of Things (IoT) enabled wireless sensor network for physiological data acquisition. In International Conference on Intelligent Computing and Applications (pp. 163-170). Springer, Singapore.
[11]. Haghi, M., Neubert, S., Geissler, A., Fleischer, H., Stoll, N., Stoll, R., & Thurow, K. (2020). A Flexible and Pervasive IoT-Based Healthcare Platform for Physiological and Environmental Parameters Monitoring. IEEE Internet of Things Journal, 7(6), 5628-5647.
[12]. Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine learning in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials, 22(3), 1686-1721.
[13]. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.
[14]. Hamidouche, R., Aliouat, Z., Ari, A. A. A., & Gueroui, M. (2019). An efficient clustering strategy avoiding buffer overflow in IoT sensors: a bio-inspired based approach. IEEE Access, 7, 156733-156751.
[15]. Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.
[16]. Malima, A., Siavoshi, S., Musacchio, T., Upponi, J., Yilmaz, C., Somu, S., ... & Busnaina, A. (2012). Highly sensitive microscale in vivo sensor enabled by electrophoretic assembly of nanoparticles for multiple biomarker detection. Lab on a Chip, 12(22), 4748-4754.
[17].Yang, G., Deng, J., Pang, G., Zhang, H., Li, J., Deng, B., ... & Yang, H. (2018). An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE journal of translational engineering in health and medicine, 6, 1-10.
[18].Postolache, O., Hemanth, D. J., Alexandre, R., Gupta, D., Geman, O., & Khanna, A. (2020). Remote monitoring of physical rehabilitation of stroke patients using IoT and virtual reality. IEEE Journal on Selected Areas in Communications, 39(2), 562-573.
[19].Agyeman, M. O., & Al-Mahmood, A. (2019, June). Design and implementation of a wearable device for motivating patients with upper and/or lower limb disability via gaming and home rehabilitation. In 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 247-252). IEEE.
[20].Ghorbel, A., Bouguerra, S., Amor, N. B., & Jallouli, M. (2018, June). Cloud based mobile application for remote control of intelligent wheelchair. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 1249-1254). IEEE.

[21]. Majumder, A. K. M., ElSaadany, Y. A., Young, R., & Ucci, D. R. (2019). An energy efficient wearable smart IoT system to predict cardiac arrest. Advances in Human-Computer Interaction, 2019.
[22]. Jayanth, S., Poorvi, M. B., Shreyas, R., Padmaja, B., & Sunil, M. P. (2017, January). Wearable device to measure heart beat using IoT. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-5). IEEE.
[23]. Milici, S., Lorenzo, J., Lazaro, A., Villarino, R., & Girbau, D. (2016). Wireless breathing sensor based on wearable modulated frequency selective surface. IEEE Sensors Journal, 17(5), 1285-1292.
[24]. Brezulianu, A., Geman, O., Zbancioc, M. D., Hagan, M., Aghion, C., Hemanth, D. J., & Son, L. H. (2019). IoT based heart activity monitoring using inductive sensors. Sensors, 19(15), 3284.
[25]. Naranjo-Hernández, D., Talaminos-Barroso, A., Reina-Tosina, J., Roa, L. M., Barbarov-Rostan, G., Cejudo-Ramos, P., ... & Ortega-Ruiz, F. (2018). Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing. Sensors, 18(7), 2144.
[26]. Mahbub, I., Pullano, S. A., Wang, H., Islam, S. K., Fiorillo, A. S., To, G., & Mahfouz, M. R. (2017). A low-power wireless piezoelectric sensor-based respiration monitoring system realized in CMOS process. IEEE Sensors Journal, 17(6), 1858-1864.
[27]. Yoshida, S., Miyaguchi, H., & Nakamura, T. (2018). Development of tablet-shaped ingestible core-body thermometer powered by gastric acid battery. IEEE Sensors Journal, 18(23), 9755-9762.
[28]. Wan, J., Al-awlaqi, M. A., Li, M., O’Grady, M., Gu, X., Wang, J., & Cao, N. (2018). Wearable IoT enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1-10.
[29]. Murali, D., Rao, D. R., Rao, S. R., & Ananda, M. (2018, September). Pulse oximetry and IOT based cardiac monitoring integrated alert system. In 2018 international conference on advances in computing, communications and informatics (ICACCI) (pp. 2237-2243). IEEE.
[30]. Lamonaca, F., Balestrieri, E., Tudosa, I., Picariello, F., Carnì, D. L., Scuro, C., ... & Colaprico, A. (2019, June). An overview on Internet of medical things in blood pressure monitoring. In 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE.
[31]. Yang, S., Gao, B., Jiang, L., Jin, J., Gao, Z., Ma, X., & Woo, W. L. (2018). IoT structured long-term wearable social sensing for mental wellbeing. IEEE Internet of Things Journal, 6(2), 3652-3662.
[32]. Pradhan, B., Bhattacharyya, S., & Pal, K. (2021). IoT-Based Applications in Healthcare Devices. Journal of healthcare engineering, 2021.
[33]. Kafi, M. A., Challal, Y., Djenouri, D., Doudou, M., Bouabdallah, A., & Badache, N. (2013). A study of wireless sensor networks for urban traffic monitoring: applications and architectures. Procedia computer science, 19, 617-626.

[34]. Qin, Y., Sheng, Q. Z., Falkner, N. J., Dustdar, S., Wang, H., & Vasilakos, A. V. (2016). When things matter: A survey on data-centric internet of things. Journal of Network and Computer Applications, 64, 137- 153.
[35]. Jakkula, V., & Cook, D. (2010, July). Outlier detection in smart environment structured power datasets.
In 2010 Sixth International Conference on Intelligent Environments (pp. 29-33). IEEE.
[36]. Toshniwal, D. (2013, February). Clustering techniques for streaming data-a survey. In 2013 3rd IEEE international advance computing conference (IACC) (pp. 951-956). IEEE.
[37]. Derguech, W., Bruke, E., & Curry, E. (2014, December). An autonomic approach to real-time predictive analytics using open data and internet of things. In 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (pp. 204-211). IEEE.
[38]. Hu, S. (2015, July). Research on data fusion of the internet of things. In 2015 International Conference on Logistics, Informatics and Service Sciences (LISS) (pp. 1-5). IEEE.
[39]. Ni, P., Zhang, C., & Ji, Y. (2014, August). A hybrid method for short-term sensor data forecasting in Internet of Things. In 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 369-373). IEEE.
[40]. Khan, M. A., Khan, A., Khan, M. N., & Anwar, S. (2014, November). A novel learning method to classify data streams in the internet of things. In 2014 National Software Engineering Conference (pp. 61- 66). IEEE.
[41].Ma, X., Wu, Y. J., Wang, Y., Chen, F., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies, 36, 1-12.
[42].Han, W., Gu, Y., Zhang, Y., & Zheng, L. (2014, October). Data driven quantitative trust model for the internet of agricultural things. In 2014 International Conference on the Internet of Things (IOT) (pp. 31-36). IEEE.
[43].Kotenko, I., Saenko, I., Skorik, F., & Bushuev, S. (2015, May). Neural network approach to forecast the state of the internet of things elements. In 2015 XVIII international conference on soft computing and measurements (SCM) (pp. 133-135). IEEE.
 

Keywords

Deep learning, Machine learning, IoT, Intelligent of object, healthcare, and automation, soft cyborg Techniques.