Smart Model for Big Data Classification Using Deep Learning in Wireless Body Area Networks

2021 ◽  
pp. 215-224
Author(s):  
Pradeep Bedi ◽  
S. B. Goyal ◽  
Rohit Sharma ◽  
Dileep Kumar Yadav ◽  
Monika Sharma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Classification ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Body Area ◽  
Big Data Classification

scholarly journals The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

2019 ◽  
Vol 8 (11) ◽  
pp. 2908-2914
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Review Paper ◽  
Data Classification ◽  
Apache Spark ◽  
The Novel ◽  
Second Stage ◽  
Big Data Applications ◽  
Big Data Classification ◽  
Two Stages

At present the Big Data applications, for example, informal communication, therapeutic human services, horticulture, banking, financial exchange, instruction, Facebook and so forth are producing the information with extremely rapid. Volume and Velocity of the Big information assumes a significant job in the presentation of Big information applications. Execution of the Big information application can be influenced by different parameters. Expediently search, proficiency and precision are the a portion of the overwhelming parameters which influence the general execution of any Big information applications. Due the immediate and aberrant inclusion of the qualities of 7Vs of Big information, each Big Data administrations anticipate the elite. Elite is the greatest test in the present evolving situation. In this paper we propose the Big Data characterization way to deal with speedup the Big Data applications. This paper is the review paper, we allude different Big information advancements and the related work in the field of Big Data Classification. In the wake of learning and understanding the writing we discover the holes in existing work and techniques. Finally we propose the novel methodology of Big Data characterization. Our methodology relies on the Deep Learning and Apache Spark engineering. In the proposed work two stages are appeared; first stage is include choice and second stage is Big Data Classification. Apache Spark is the most reasonable and predominant innovation to execute this proposed work. Apache Spark is having two hubs; introductory hubs and last hubs. The element choice will be occur in introductory hubs and Big Data Classification will happen in definite hubs of Apache Spark

scholarly journals Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4229 ◽  
Author(s):  
Krzysztof K. Cwalina ◽  
Piotr Rajchowski ◽  
Olga Blaszkiewicz ◽  
Alicja Olejniczak ◽  
Jaroslaw Sadowski
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Impulse Response ◽  
Indoor Environment ◽  
Measurement Data ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Feedforward Neural Network ◽  
Ultra Wideband ◽  
Body Area

In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification.

Literature review and analysis on big data stream classification techniques

2020 ◽  
Vol 24 (3) ◽  
pp. 205-215
Author(s):  
B. Srivani ◽  
N. Sandhya ◽  
B. Padmaja Rani
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Classification ◽  
Support Vector ◽  
Complex Data ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Data Stream Classification ◽  
Big Data Classification

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.

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