CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Applications of Artificial Intelligence in Machine Learning: Review and Prospect

by Sumit Das, Aritra Dey, Akash Pal, Nabamita Roy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 9
Year of Publication: 2015
Authors: Sumit Das, Aritra Dey, Akash Pal, Nabamita Roy
10.5120/20182-2402

Sumit Das, Aritra Dey, Akash Pal, Nabamita Roy . Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications. 115, 9 ( April 2015), 31-41. DOI=10.5120/20182-2402

@article{ 10.5120/20182-2402,
author = { Sumit Das, Aritra Dey, Akash Pal, Nabamita Roy },
title = { Applications of Artificial Intelligence in Machine Learning: Review and Prospect },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 9 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number9/20182-2402/ },
doi = { 10.5120/20182-2402 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:23.699948+05:30
%A Sumit Das
%A Aritra Dey
%A Akash Pal
%A Nabamita Roy
%T Applications of Artificial Intelligence in Machine Learning: Review and Prospect
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 9
%P 31-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that's we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that's also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.

References
  1. Tzanis, George, et al. "Modern Applications of Machine Learning. " Proceedings of the 1st Annual SEERC Doctoral Student Conference–DSC. 2006.
  2. Horvitz, Eric. "Machine learning, reasoning, and intelligence in daily life: Directions and challenges. " Proceedings of. Vol. 360. 2006.
  3. Mitchell, Tom Michael. The discipline of machine learning. Carnegie Mellon University, School of Computer Science, Machine Learning Department, 2006.
  4. Ball, Gregory R. , and Sargur N. Srihari. "Semi-supervised learning for handwriting recognition. " Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on. IEEE, 2009.
  5. Valenti, Roberto, et al. "Machine learning techniques for face analysis. " Machine Learning Techniques for Multimedia. Springer Berlin Heidelberg, 2008. 159-187.
  6. Al-Hmouz, Ahmed. "An adaptive framework to provide personalisation for mobile learners. " (2012).
  7. Al-Hmouz, Ahmed, Jun Shen, and Jun Yan. "A machine learning based framework for adaptive mobile learning. " Advances in Web Based Learning–ICWL 2009. Springer Berlin Heidelberg, 2009. 34-43.
  8. Thore Graepel "Playing Machines: Machine Learning Applications in Computer Games", ICML 2008 Tutorial - 5 July 2008, Helsinki, Finland.
  9. Broder, Andrei, and Vanja Josifovski. "Introduction to computational advertising. " (2010).
  10. Cunningham, Sally Jo, James Littin, and Ian H. Witten. "Applications of machine learning in information retrieval. " (1997).
  11. Kaur, Harjinder, Gurpreet Singh, and Jaspreet Minhas. "A Review of Machine Learning based Anomaly Detection Techniques. " arXiv preprint arXiv:1307. 7286 (2013).
  12. Wiese, Bénard, and Christian Omlin. Credit card transactions, fraud detection, and machine learning: Modelling time with LSTM recurrent neural networks. Springer Berlin Heidelberg, 2009.
  13. Kumar, Vinod, and Dr Om Prakash Sangwan. "Signature Based Intrusion Detection System Using SNORT. " International Journal of Computer Applications & Information Technology 1 (2012).
  14. Shen, Shunrong, Haomiao Jiang, and Tongda Zhang. "Stock market forecasting using machine learning algorithms. " (2012).
  15. Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques. " Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002.
  16. Liao, Shih-wei, et al. "Machine learning-based prefetch optimization for data center applications. " Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. ACM, 2009.
  17. Haider, Peter, Luca Chiarandini, and Ulf Brefeld. "Discriminative clustering for market segmentation. " Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.
  18. Haider, Peter, Luca Chiarandini, and Ulf Brefeld. "Discriminative clustering for market segmentation. " Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.
  19. Haykin, Simon, and Zhe Chen. "The cocktail party problem. " Neural computation 17. 9 (2005): 1875-1902.
  20. Clarke, Bertrand, Ernest Fokoue, and Hao Helen Zhang. Principles and theory for data mining and machine learning. Springer Science & Business Media, 2009.
  21. Kononenko, Igor. "Machine learning for medical diagnosis: history, state of the art and perspective. " Artificial Intelligence in medicine 23. 1 (2001): 89-109.
  22. Caragea, Cornelia, and Vasant Honavar. "Machine Learning in Computational Biology. " Encyclopedia of Database Systems (2009): 1663-1667.
  23. Cho, Sung-Bae, and Hong-Hee Won. "Machine learning in DNA microarray analysis for cancer classification. " Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003-Volume 19. Australian Computer Society, Inc. , 2003.
  24. Wagstaff, Kiri. "Machine learning that matters. " arXiv preprint arXiv:1206. 4656 (2012).
  25. Shoeb, Ali H. , and John V. Guttag. "Application of machine learning to epileptic seizure detection. " Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010.
  26. Gao, Jim, and Ratnesh Jamidar. "Machine Learning Applications for Data Center Optimization. " Google White Paper (2014).
  27. Haider, Peter, Ulf Brefeld, and Tobias Scheffer. "Supervised clustering of streaming data for email batch detection. " Proceedings of the 24th international conference on Machine learning. ACM, 2007.
  28. Sebastiani, Fabrizio. "Machine learning in automated text categorization. " ACM computing surveys (CSUR) 34. 1 (2002): 1-47.
  29. Bratko, Andrej, et al. "Spam filtering using statistical data compression models. " The Journal of Machine Learning Research 7 (2006): 2673-2698.
  30. Xiong, Liang, et al. "Anomaly detection for astronomical data. " (2010).
  31. Guyon, Isabelle, and André Elisseeff. "An introduction to variable and feature selection. " The Journal of Machine Learning Research 3 (2003): 1157-1182.
  32. Hou, Shujie, et al. "SVM and Dimensionality Reduction in Cognitive Radio with Experimental Validation. " arXiv preprint arXiv:1106. 2325 (2011).
  33. Hwang, Kyu-Baek, et al. "Applying machine learning techniques to analysis of gene expression data: cancer diagnosis. " Methods of Microarray Data Analysis. Springer US, 2002. 167-182.
  34. Luca Silvestrin, "Machine Learning in Biology", Universita degli studi di Padova.
  35. Magoulas, George D. , and Andriana Prentza. "Machine learning in medical applications. " Machine Learning and its applications. Springer Berlin Heidelberg, 2001. 300-307.
  36. Bruegge, Bernd, et al. "Classification of Software Engineering Artifacts Using Machine Learning. "
  37. Shhab, Areej, Gongde Guo, and Daniel Neagu. "A Study on Applications of Machine Learning Techniques in Data Mining. " Proc. of the 22nd BNCOD workshop on Data Mining and Knowledge Discovery in Databases, Sunderland, UK. 2005.
  38. Boyarshinov, Victor. Machine learning in computational finance. Diss. Rensselaer Polytechnic Institute, 2005.
  39. Shen, Xipeng, et al. "Multilabel machine learning and its application to semantic scene classification. " Electronic Imaging 2004. International Society for Optics and Photonics, 2003.
  40. Zararsiz, Gokmen, Ferhan Elmali, and Ahmet Ozturk. "Bagging Support Vector Machines for Leukemia Classification. " development 1 (2012): 2.
  41. Tsagkaris, Kostas, Apostolos Katidiotis, and Panagiotis Demestichas. "Neural network-based learning schemes for cognitive radio systems. " Computer Communications 31. 14 (2008): 3394-3404.
  42. Tabakovi?, Željko. "A Survey of Cognitive Radio Systems. " Post and Electronic Communications Agency, Juriši?eva 13.
  43. Hosey, Neil, et al. "Q-Learning for Cognitive Radios. " Proceedings of the China-Ireland Information and Communications Technology Conference (CIICT 2009). ISBN 9780901519672. National University of Ireland Maynooth, 2009.
  44. Pawar, Prashant. Machine Learning applications in financial markets. Diss. Indian Institute of Technology, Bombay Mumbai.
  45. Tarca, Adi L. , et al. "Machine learning and its applications to biology. " PLoS computational biology 3. 6 (2007): e116.
  46. Prof. St´ephan Cl´emen ,"A Machine-Learning View of Quantitative Finance"¸ con - Institut Mines Telecom LTCI UMR Telecom Paris Tech.
  47. Shen, Shunrong, Haomiao Jiang, and Tongda Zhang. "Stock market forecasting using machine learning algorithms. " (2012).
  48. Wang, Yu, et al. "Gene selection from microarray data for cancer classification—a machine learning approach. " Computational biology and chemistry 29. 1 (2005): 37-46.
  49. Prof. Pier Luca Lanzi, Laurea in Ingegneria Informatica, Politecnico di Milano, Polo di Milano Leonardo,"Machine Learning, Data Mining, and Knowledge Discovery: An Introduction"
  50. Sajda, Paul. "Machine learning for detection and diagnosis of disease. " Annu. Rev. Biomed. Eng. 8 (2006): 537-565.
  51. Øland, Anders. "Machine Learning and its Applications to Music. " (2011).
  52. Makeig, S. ; Kothe, C. ; Mullen, T. ; Shamlo, N. B. ; Zhang, Z. & Kreutz-Delgado, K. (2012), 'Evolving Signal Processing for Brain-Computer Interfaces. ', Proceedings of the IEEE 100 (Centennial-Issue) , 1567-1584 .
  53. Seyed Omid Sadjadi and John H. L. Hansen, "Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux", IEEE signal processing letters, March 2013.
  54. Hafiz Malik, "Acoustic Environment Identification and Its Applications to Audio Forensics". IEEE Transactions on Information Forensics and Security, Vol. 8, No. 11, November 2013.
  55. "Acoustic Factor Analysis For Robust Speaker Verification, Fellow, IEEE. IEEE Transactions On Audio, Speech, And Language Processing, Vol. 21, No. 4, April 2013.
  56. Mark J. Weal, Danius T. Michaelides, Kevin Page, David C. De Roure, Fellow, IEEE, Eloise Monger, and Mary Gobbi. , "Semantic Annotation of Ubiquitous Learning", Environments IEEE Transactions On Learning Technologies, Vol. 5, No. 2, April-June 2012.
  57. EJ Horvitz, J Apacible, R Sarin, L Liao - arXiv preprint arXiv:1207. 1352, 2012 - arxiv. org
  58. https://www. coursera. org/learn/recommender-systems
Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Machine learning Supervised learning Unsupervised learning Reinforcement learning Applications.

Powered by PhDFocusTM