Study on the Socio-Psychological Background of Entrepreneur - Managers of Small Scale Industrial Units in Ernakulam District, Kerala

The understanding of the theory of entrepreneurship depends upon one set of definitions which provide the base for analytical study. The main objective of the study was to understand the distribution of entrepreneurship in the manufacturing sector among different categories of people in kerala and to differentiate the socio - psychological background of successful entrepreneur- managers from unsuccessful entrepreneur-managers. The purpose of the study, a sample of 150 entrepreneur-managers of SS1 units spread over Ernakulam district was surveyed through a specially designed questionnaire.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children