BIOCHEMICAL GENETIC STUDIES ON THE OIL SARDINE, Sardinella longiceps (CUVIER AND VALENCIENNES, 1847) FROM SELECTED CENTRES OF THE WEST COAST OF INDIA

The main objective of the present investigation was to study the biochemical genetic variability within the species and genetic structure of its regional populations from west coast. Realising the recent report of occurrence of oil sardine fishery in east coast of India, population samples from Mandapam and Madras were also included in the present investigation. The original data gathered on the population genetics of the species have helped to interpret and evaluate the results objectively. The important conclusions drawn from a detailed discussions on the subject would throw some light on the probable process of problematic fluctuations in the abundance of oil sardine fishery of India. The academic and applied values of present discoveries need not be emphasised. The data used for the doctoral thesis were generated during the ICAR ad-hoc project on the "Population genetic studies on oil sardine, sardinella longiceps to identity distinct genetic stocks", carried out at CMFRI, Cochin during the years, 1988-1991

Transition metal complexes of quinoxaline based Schiff base ligands: Synthesis,characterization and catalytic activity study

Schiff base complexes of transition metal ions have played a significant role in coordination chemistry.The convenient route of synthesis and thermal stability of Schiff base complexes have contributed significantly for their possible applications in catalysis,biology,medicine and photonics.Significant variations in cataltytic activity with structure and type are observed for these complexes.The thesis deals with synthsis and characterization of transition metal complexes of quinoxaline based Schiff base ligands and their catalytic activity study.The Schiff bases synthesized in the present study are quinoxaline-2-carboxalidine-2-amino-5-methylphenol,3-hydroxyquinoxaline-2-carboxalidine-2-amino-5-methylphenol,quinoxaline-2-aminothiophenol.They provide great structural diversity during complexation.To the best of our knowledge, the transition metal complexes of quinoxaline based Schiff bases are poorly utilised in academic and industrial research.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.