Studies on Recent Machine Learning Approaches to Explore Performance, Security Issues and New Dimensions to Deal with the Challenges | Original Article

ABSTRACT:

In the era of Computer technology, Machine learning is centre of attraction for the researchers of data mining. Organizational and individuals information in the Computers and we are going through serious issues of threats and intrusions. Malicious activities are increased in the computer and web usage. With rapid advancement in computer technology and networking services, huge amount of data has been generating, which is difficult to handle by traditional data processing applications. Datasets in the web are composed of structure and unstructured set of data. To deal with the unstructured set of data is a prime area of attention for the researchers. There is need of advancements in the data mining and data processing techniques, to deal with this massive amount of structure and unstructured datasets. Challenges are to improve accuracy and performance of data classification, regression and clustering, to analyze and update data storage, to maintain security and information privacy. Today machine-learning techniques, which are getting key attention, are Feature reduction, Decision tree techniques, Ensemble techniques, Neural Networks, Statistical techniques, Genetic algorithm, Fuzzy logic and big data analytics. In this paper, we are trying to gain attention on some of recent work done in these fields to explore data processing, data analysis and security challenges issues.