Impact of Big Data Analysis on Nanosensors for Applied Sciences Using Neural Networks

In the current-generation wireless systems, there is a huge requirement on integrating big data which can able to predict the market trends of all application systems. Therefore, the proposed method emphasizes on the integration of nanosensors with big data analysis which will be used in healthcare applications. Also, safety precautions are considered when this nanosensor is integrated where depth and reflection of signals are also observed using different time samples. In addition, to analyze the effect of nanosensors, six fundamental scenarios that provide good impact on real-time applications are also deliberated. Moreover, for proving the adeptness of the proposed method, the results are equipped in both online and offline analyses for investigating error measurement, sensitivity, and permeability parameters. Since nanosensors are introduced, the efficiency of the projected technique is increased by implementing media access control (MAC) protocol with recurrent neural network (RNN). Further, after observing the simulation results, it is proved that the proposed method is more effective for an average percentile of 67% when compared to the existing methods.

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Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

IEEE Access ◽

10.1109/access.2016.2613278 ◽

2016 ◽

Vol 4 ◽

pp. 6171-6180 ◽

Cited By ~ 95

Author(s):

Lo'ai A. Tawalbeh ◽

Rashid Mehmood ◽

Elhadj Benkhlifa ◽

Houbing Song

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Analysis ◽

Mobile Cloud Computing ◽

Big Data Analysis ◽

Mobile Cloud ◽

Healthcare Applications ◽

Computing Model

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Big Data Analysis and Implementation in Different Areas Using IoT

International Journal of Hyperconnectivity and the Internet of Things ◽

10.4018/ijhiot.2017070102 ◽

2017 ◽

Vol 1 (2) ◽

pp. 12-25

Author(s):

Aqeel ur Rehman ◽

Muhammad Fahad ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Healthcare Applications ◽

New Techniques ◽

Standard Data ◽

Huge Data ◽

Data Rates ◽

Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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Smart Home based Big Data Analysis for Healthcare Applications

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1406.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 275-282

Keyword(s):

Health Care ◽

Big Data ◽

Data Analysis ◽

Smart Home ◽

Health Care Services ◽

Activity Patterns ◽

Big Data Analysis ◽

City Centre ◽

Data Types ◽

Healthcare Applications

At present, there is a constant migration of people is encountered in urban regions. Health care services are considered as a confronting challenging factors, there is an extremely influenced by huge arrival of people to city centre. Subsequently, places all around the world are spending in digital evolution in an attempt to offer healthy eco-system for huge people. With this transformation, enormous homes are equipped with smarter devices (for example, sensors, smart sensors and so on) which produce huge amount of indexical data and fine-grained that is examined to assist smart city services. In this work, a model has been anticipated to utilize smart home big data analysis as a discovering and learning human activity patterns for huge health care applications. This work describes and highlights the experimentation with the analysis of vigorous data analysis process that assists healthcare analytics. This procedure comprises of subsequent stages: understanding, collection, cleaning, validation, enrichment, integration and storage. It has been resourcefully utilized to processing of data types variety comprising clinical data from EHR.

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