A Literature Survey on Machine Learning Algorithms and Applications



Machine Learning (ML) is a subset of the Artificial Intelligence, a field of computer science that provides system to automatically learn and improve from past experience without being explicitly programmed. Machine Learning (ML) is a multidisciplinary field, a combination of statistics and computer science algorithms which is widely used in prediction and classification. The second section of the paper focuses on the basic machine learning methods and algorithms. This paper will highlight the various machine learning tools needed to run the machine learning programs. The main concern of concerned paper is to study the main approaches and case studies of using machine learning for forecasting in different areas such stock price forecasting, weather forecasting, tourism demand forecasting, solar irradiation forecasting, supply chain demand and consideration of neural network in machine learning methods.

How to cite this article:
Dash PK. A Literature Survey on Machine Learning Algorithms and Applications. J Engr Desg Anal 2020; 3(2): 88-91.


Awad M, Khanna R. Efficient learning machines: Concepts and Applications. Aspress Publications, 2015.

Xiuyi T, Yuxial G. Research on Application of machine learning in data mining. IOP Conf. Series: Materials Science and Engineering. 2018.

Praveen M, Jaignesh V. Litrature review on Supervised Machine Learning Algorithms and Boosting Process. International Journal of Computer Applications.

Das K, Narayan RB. A survey on Machine learning: concept, algorithm and applications. International Journal of Innovative Research in Computer and Communication Engineering 2017; 5.

Kotsiants SB. Supervised Machine Learning: A Review of classification tyechniques. Informatica 2007; 249-268.

Law R. Room occupancy rate forecasting: a neural network approach. International Journal of Contemporary Hospitality Management 1998; 10(6): 234-239.

Hua Z, Bin Z. A hybrid support vector machines and logistic regression approach for forecasting intermitten demand of spare parts. Applied Mathematics and Computation 2006; 181: 1035-1048.

Carbonneu R, Kevin L, Vahidov R. Application of Machine learning techniques for supply chain demand forecasting. European Journal of Operational Research 2008; 184: 1140-1154.

Kuan-Yu C, Cheng HW. Support vector regression with genetic algorithms in forecasting tourism demand Tourism Management 2007; 28: 215-226.

Wei CH, Dong Y, Chen LY et al. SVR with hybrid chaotic genetic algorithm for tourism demand forecasting. Applied soft Computing 2011; 11: 1881-1890.