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Aery, Manish Kumar
- Green Computing:A Study on Future Computing and Energy Saving Technology
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1 Dept.of MCA, IET Bhaddal, IN
2 IET Bhaddal, Ropar, IN
1 Dept.of MCA, IET Bhaddal, IN
2 IET Bhaddal, Ropar, IN
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Research Cell: An International Journal of Engineering Sciences, Vol 25 (2017), Pagination: 82-88Abstract
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.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,
- Volume 3, Issue 6, June 2013, pp. 790-794.
- GauravJindal,Manisha Gupta, “Green Computing, Future of Computers “in International Journal of Emerging Research in Management &Technology, December 2012, pp. 14-18
- Tariq Rahim Soomro and Muhammad Sarwar, “Green Computing: From Current to Future Trends” in International Scholarly and Scientific Research & Innovation, Vol:6, March 2012,pp. 455-458.
- 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
Abstract Views :162 |
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Authors
Affiliations
1 Dept. of MCA,IET Bhaddal, Ropar, Punjab, IN
2 Dept. of CSE,IET Bhaddal, Ropar, Punjab, IN
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: 89-96Abstract
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.References
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- A Few Useful Things to Know about Machine Learning Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA .S.A.pedrod@cs.washington.edu.
- Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.-R. Muller.Engineering Sup-port Vector Machine Kernels That Recognize Translation Initiation Sites.BioInformatics,16(9):799–807, September 2000.
- Yom-Tov. An introduction to pattern classification. In U. von Luxburg, O. Bousquet, and G. Ratsch, editors, Advanced Lectures on Machine Learning, volume 3176 of LNAI , pages 1–23. Springer, 2004.
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- Trends in extreme learning machines: a review, by Huang, G., Huang, G., Song, S., & You, K. (2015)
- Machine Learning: A study of algorithms that learn from data and experience.
- A Visual Introduction to Machine Learning (2015). Courtesy of Stephanie yee and Tony Chu