Abstract
Deep learning is an innovative set of algorithms in machine learning and requires minimum efforts of human engineering in extraction of features from data. It has the ability to find the optimum set of parameters for the network layers using a back-propagation algorithm, thereby modeling intricate structures in the data distribution. Further, deep learning architectures have resulted in tremendous performance on most recent machine learning challenges included working with sequential data such as text and time series data. In this connection, big data technology is an asset for modern businesses and is useful if powered by intelligent automation. Big data involves huge datasets that can be analyzed by machine learning such as deep learning algorithms to find insightful patterns and trends. With modern-day machine learning and big data technology, organizations can drive its long-term business value far more successful than ever before. Potential real-world applications of big data are not limited to healthcare, retail, financial services, and the automotive industry. In this way, the deep learning can have a great impact on analyzing the patient’s data generated from wireless body area networks (WBANs). WBAN is the emerging technology in healthcare to assist in monitoring of vital signs of patients using biomedical sensors. The monitored data is transmitted to the medical doctor for an optimal treatment in a life-threatening situation. At the end of this book, open research issues in WBAN and big data have discussed.
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Abbreviations
- BMS:
-
Biomedical sensor
- CAP:
-
Contention-access period
- CNN:
-
Convolutional neural networks
- CEP:
-
Complex event processing
- CGOC:
-
Compliance, Governance and Oversight Council
- CFP:
-
Contention-free period
- CS:
-
Conventional server
- CSMA/CA:
-
Carrier-sense multiple access with collision avoidance
- DNN:
-
Deep neural network
- EAP:
-
Exclusive access phase
- ECG:
-
Electrocardiogram
- EEG:
-
Electroencephalogram
- EMG:
-
Electromyography
- IEEE:
-
Institute of Electrical and Electronics Engineers
- IP:
-
Inactive period
- GPU:
-
Graphics processing unit
- GSM:
-
Global system for mobile
- GST:
-
Guaranteed time slot
- HDFS:
-
Hadoop Distributed File Systems
- LOS:
-
Line-of-sight
- LSTM:
-
Long short-term memory
- MLP:
-
Multilayer perceptron
- MAC:
-
Medium access control
- NLOS:
-
Non-line-of-sight
- PHY:
-
Physical layer
- QoS:
-
Quality of service
- RAP:
-
Random-access phase
- RNN:
-
Recurrent neural network
- SPO2:
-
Peripheral capillary oxygen saturation
- TDMA:
-
Time-division medium access
- VC:
-
Virtualized cloudlet
- WBAN:
-
Wireless body area networks
- WHO:
-
World Health Organization
- WSN:
-
Wireless sensor network
- TG6:
-
Task Group 6
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Ullah, F., Islam, I.U., Abdullah, A.H., Khan, A. (2019). Future of Big Data and Deep Learning for Wireless Body Area Networks. In: Deep Learning: Convergence to Big Data Analytics. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-3459-7_5
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