Research paper on big data analytics in healthcare

Big data security is a centralized approach to protect big data analytical tools and applications that are quite vulnerable to cyberattacks. It comprises entire strategy and technology stack required to protect large amount of data and analytical tools from potential threats, attacks, and other malicious activities. Over the years, big data security gained considerable significance and is becoming a common part of applications that are associated with computers, smartphone, and smart devices. Owing to the features offered, several industries, including BFSI, manufacturing, healthcare, and service providers, are deploying this technology worldwide on a considerable scale. Besides, rapid incorporation of IoT and other intelligence devices and rapid growth in the volume of the business data are driving the growth of the big data security market.
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Big data in healthcare: management, analysis and future prospects

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A review of data mining using big data in health informatics | Journal of Big Data | Full Text

We use cookies essential for this site to function well. Please click "Accept" to help us improve its usefulness with additional cookies. Learn about our use of cookies, and collaboration with select social media and trusted analytics partners here Learn more about cookies, Opens in new tab. In the past 20 years or so, manufacturers have been able to reduce waste and variability in their production processes and dramatically improve product quality and yield the amount of output per unit of input by implementing lean and Six Sigma programs. However, in certain processing environments—pharmaceuticals, chemicals, and mining, for instance—extreme swings in variability are a fact of life, sometimes even after lean techniques have been applied. Given the sheer number and complexity of production activities that influence yield in these and other industries, manufacturers need a more granular approach to diagnosing and correcting process flaws. Advanced analytics provides just such an approach.
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A review of data mining using big data in health informatics

Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data , as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered.
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A new data-driven programming model is defined by the deep learning DL that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the dat Authors: Kiran Jammalamadaka and Nikhat Parveen. Citation: Journal of Big Data 8 Content type: Research.
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