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.

Investigations on the structural, optical and magnetic properties of nanostructured cerium oxide in pure and doped forms and its polymer nanocomposites

In recent years, nanoscience and nanotechnology has emerged as one of the most important and exciting frontier areas of research interest in almost all fields of science and technology. This technology provides the path of many breakthrough changes in the near future in many areas of advanced technological applications. Nanotechnology is an interdisciplinary area of research and development. The advent of nanotechnology in the modern times and the beginning of its systematic study can be thought of to have begun with a lecture by the famous physicist Richard Feynman. In 1960 he presented a visionary and prophetic lecture at the meeting of the American Physical Society entitled “there is plenty of room at the bottom” where he speculated on the possibility and potential of nanosized materials. Synthesis of nanomaterials and nanostructures are the essential aspects of nanotechnology. Studies on new physical properties and applications of nanomaterials are possible only when materials are made available with desired size, morphology, crystal structure and chemical composition. Cerium oxide (ceria) is one of the important functional materials with high mechanical strength, thermal stability, excellent optical properties, appreciable oxygen ion conductivity and oxygen storage capacity. Ceria finds a variety of applications in mechanical polishing of microelectronic devices, as catalysts for three-way automatic exhaust systems and as additives in ceramics and phosphors. The doped ceria usually has enhanced catalytic and electrical properties, which depend on a series of factors such as the particle size, the structural characteristics, morphology etc. Ceria based solid solutions have been widely identified as promising electrolytes for intermediate temperature solid oxide fuel cells (SOFC). The success of many promising device technologies depends on the suitable powder synthesis techniques. The challenge for introducing new nanopowder synthesis techniques is to preserve high material quality while attaining the desired composition. The method adopted should give reproducible powder properties, high yield and must be time and energy effective. The use of a variety of new materials in many technological applications has been realized through the use of thin films of these materials. Thus the development of any new material will have good application potential if it can be deposited in thin film form with the same properties. The advantageous properties of thin films include the possibility of tailoring the properties according to film thickness, small mass of the materials involved and high surface to volume ratio. The synthesis of polymer nanocomposites is an integral aspect of polymer nanotechnology. By inserting the nanometric inorganic compounds, the properties of polymers can be improved and this has a lot of applications depending upon the inorganic filler material present in the polymer.