Abstract
Deep learning is an innovative set of machine learning algorithms and requires human engineering effort in data collection. It can find the optimum set of parameters for network layers by means of a back-propagation algorithm and thereby model complex data distribution structures. In addition, the deep learning architecture has led to enormous achievements in the most recent challenges of machine learning involving sequential information such as text and series data. Big data technology is an asset in this context for modern companies. Smart automation is useful if it is used. The big data consists of large datasets which can, for example, be analysed by machine learning to find comprehensive models and trends. Thanks to new machine learning and big data techniques, businesses are far more effective than ever before in creating long-term market value. Big data’s potential real-life applications are not confined to medical, retail, financial and automotive industries. This makes a great impact of the profound learning on the analysis of patient data generated by wireless body area network (WBANs). WBAN is the emerging healthcare technology to help monitor essential signs of the use of biomedical sensors for patients. The tracked data is forwarded to the doctor for an optimum processing under life risks. We need to develop an intelligent model for the classification of large data using deep learning on wireless body networks.
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Bedi, P., Goyal, S.B., Sharma, R., Yadav, D.K., Sharma, M. (2021). Smart Model for Big Data Classification Using Deep Learning in Wireless Body Area Networks. In: Sharma, D.K., Son, L.H., Sharma, R., Cengiz, K. (eds) Micro-Electronics and Telecommunication Engineering. Lecture Notes in Networks and Systems, vol 179. Springer, Singapore. https://doi.org/10.1007/978-981-33-4687-1_21
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