Stress Of Women Entrepreneurs a Comparative With Women In Selected Groups In The District Of Ernakulam

The focus of this study is the stress of women entrepreneurs.As stress is associated with constraints and demands, and as a set of emerging conditions seem to affect the quality of life of women, it is more than just an occasional need to enquire in to the possibilities of promoting entrepreneurship by empowering women.As women entrepreneurs are increasingly involved in inherently complicated activities of improving their enterprise functioning ,it would be appropriate for women entrepreneurs to focus on transformational coping interventions.The study is limited to women entrepreneurs in the tiny sector.Women entrepreneurs registered in the Distric Industries ( DIC) and in the Kerala State Women’s Industries Association (KSWIA) are only selected for the study.It gaves a detailed description about empowerment of women.The social , economic ,political,ecological,and psychological importance of the study are detailed.It explains the family related stress, and the contextual system.This study is suggested on beliefs and values of women about their self-perception influencing gender bias, which contribute to stress and coping.This study is also needed about women’s believes and expectations about the probable effectiveness of various course of action and their ability to perform those actions.It is also neede for appraising coping potential of women and enhancing their stress base.It is important to research on stress and self-concept

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Women Entrepreneurship In the Industrial Manufacturing Sector of Kerala

This study was on women's industries programme in Kerala, to assess the involvement of manpower in this field and to analyse the difficulties and problems faced by the women entrepreneurs which impede the growth and smooth functioning of units. It was supported by the views of 275 women entrepreneurs of Kerala. Census method was adopted and only 58 per cent of units responded by supplying necessary details. Details were collected from these: units through mailed questionnaires designed for the purpose. The study highlights the profile of workers in the women's industrial units, but the profile of the entrepreneurs is neglected. Problems faced by women entrepreneurs are analysed under the following major heads viz., capital, raw materials, marketing, competition from other units and availability of power. But the conclusions drawn from the survey are not on proper empirical support. It also includes suggestions of entrepreneurs. The major findings of the study are as follows : Nearly 82 per cent of the women's industrial units are functioning throughout the year. Proprietory concerns and co—operative societies are the popular ones. Majority of the units are running on profit. Women's units are still in their infancy and so the problems faced by them are many. The characteristics of having other business or sister concerns is lacking among women entrepreneurs. Nearly 94 per cent of the employees are permanent. About four-fifth (81%) of the workers are full time employees. Only a very small proportion of the employees (1%) get a reasonable income that is above Rs.50O per month. The workers are very young and 63 per cent workers have no experience at all.