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.

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

Social Structural Determinants of Education among Tribes in Kerala

The study is focused on education of tribes particularly the problem of high dropout rate existing among the tribal students at school level. Scheduled Tribe is one of the marginalized communities experiencing high level of educational deprivation. The analysis of the study shows that the extent of deprivation existing among STs of Kerala is much higher compared to that of other communities. The present study covered tribes of three tribal predominant districts of Kerala such as Idukki, Palakkad and Wayanad. Out of the 35 tribal communities in the State, 17 of them are concentrated in these districts. Tribes concentrated in Idukki include Muthuvans, Malai Arayan, Uraly, Mannan and Hill Pulaya. The present study analyzed dropouts situation in tribal areas of Kerala by conducting Field Survey among dropout and non-dropout students at school level. High dropouts among STs persist due to many problems which are of structural in nature. Important problems faced by the tribal students that have been analyzed, this can be classified as economic, social, cultural and institutional. It is found that there exists high correlation between Income and expenditure of the family with the well-being of individuals. Significant economic factors are poverty and financial indebtedness of the family. Some of the common cultural factors of tribes are Nature of Habitation, Difference in Dialect and Medium of Instruction etc. Social factors analyzed in the study are illiteracy of parents, migration of family, family environment, motivation by parents, activities engaged in for helping the family and students’ lack of interest in studies. The analysis showed that all these factors except migration of the family, are affecting the education of tribal students. Apart from social, economic and cultural factors, there are a few institutional factors which will also influence the education of tribal students. Institutional factors analyzed in the study include students’ absenteeism, irregularity of teachers, attitude of non-tribal teachers and non-tribal students, infrastructure facilities and accessibility to school. The study found irregularity of students and accessibility to school as significant factors which determine the dropout of the students.