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An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

Rojalina Priyadarshini, Rabindra K. Barik, Chhabi Panigrahi, Harishchandra Dubey, Brojo Kishore Mishra
Copyright: © 2018 |Volume: 10 |Issue: 3 |Pages: 13
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781522543381|DOI: 10.4018/IJGHPC.2018070101
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MLA

Priyadarshini, Rojalina, et al. "An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care." IJGHPC vol.10, no.3 2018: pp.1-13. http://doi.org/10.4018/IJGHPC.2018070101

APA

Priyadarshini, R., Barik, R. K., Panigrahi, C., Dubey, H., & Mishra, B. K. (2018). An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care. International Journal of Grid and High Performance Computing (IJGHPC), 10(3), 1-13. http://doi.org/10.4018/IJGHPC.2018070101

Chicago

Priyadarshini, Rojalina, et al. "An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care," International Journal of Grid and High Performance Computing (IJGHPC) 10, no.3: 1-13. http://doi.org/10.4018/IJGHPC.2018070101

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Abstract

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

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