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Currently computers are not only used in offices but also used at home. As the usage of computers is increasing day by day, the energy consumption is growing rapidly which in turns increase the carbon content in atmosphere. Green computing concept is to improve environmental condition. The main aim of green computing is to reduce toxic materials. During recent years, attention in „Green Computing‟ has moved the research into energy saving techniques for home computers to enterprise systems' Client and Server machines. Saving energy or reduction of carbon footprints is one of the aspects of Green Computing. This study provides a brief account of Green Computing. The emphasis of this study is on current trends in Green Computing; challenges in the field of Green Computing and the future trends of Green Computing.

Keywords

Green Computing, Energy, Computer and IT.

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References

BiswajitSaha,” Green Computing”in International Journal of ComputerTrends and Technology (IJCTT),volume 14 ,Aug 2014,pp.46-50.

PushtikantMalviya, Shailendra Singh, “A Study about Green Computing” in International Journal of Advanced Research in Computer Science and Software Engineering,

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Mrs .SharmilaShinde , Mrs. SimantiniNalawade, Mr .Ajay Nalawade, “Green Computing: Go Green and Save Energy” in International Journal of Advanced Research in Computer Science and Software Engineering,Volume 3, Issue 7, July 2013,pp. 10331037.

A Review on Machine Learning:Trends and Future Prospects

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Authors

Manish Kumar Aery ¹, Chet Ram ²

Affiliations
1 Dept. of MCA,IET Bhaddal, Ropar, Punjab, IN
2 Dept. of CSE,IET Bhaddal, Ropar, Punjab, IN

Source

Research Cell: An International Journal of Engineering Sciences, Vol 25 (2017), Pagination: 89-96

Abstract

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

Keywords

Machine, Computer Science, Statics, Technology.

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References

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A Visual Introduction to Machine Learning (2015). Courtesy of Stephanie yee and Tony Chu

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