Consumption Expenditure Pattern of Scheduled Caste Households of Kerala: a Study of Idukki District

The present study made an attempt to analyze the socio-economic background and the consumption pattern of scheduled caste households in Idukki district. The objectives of the study are to examine consumption pattern among the scheduled cast population, differences in the average consumption expenditure of different decile groups, consumption expenditure elasticity of items, variations in expenditure of SC households on food, non-food and total expenditure and to examine the association between consumption expenditure and variables such as income, education, occupation and area of residence. The study reveals that the Monthly Per Capita Expenditure of scheduled castes population in rural Kerala is lower than that of the general population. Average household size is higher in rural sector for Scheduled Caste in Kerala as well as all-India. The per capita expenditure of Scheduled Castes of rural Kerala is found to be much lower than that of general population. The study has found that the levels of livings of the Scheduled Castes are far the below the expectations. Large percentage of the Scheduled Caste belongs to the lower income groups. This is due their very low economic status and the consequent employment prospects in low paying occupations. The consumption standards of the majority of Scheduled Castes are found much below that of General population. Effective implementation of the Schemes for their economic upliftment is needed for improving their consumption standards, Minimum Wage Act in the case of agricultural labourers etc. are some of recommendations on the basis of this study.

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.

Settlement Of Reinforced Sand In Foundations

The technique of reinforcing soil for foundation improvement is well established. This paper addresses the aspect of settlement of reinforced sand foundations, where the major part of the existing work deals with the aspect of bearing capacity. A detailed analysis is made paying individual attention to soil, reinforcement, and the interface between the two. A three-dimensional, nonlinear finite-element analysis is presented that uses a three-dimensional, nonlinear soil-reinforcement interface friction element, along with other threedimensional elements to model the system. The results of the analysis are compared with those from tests conducted in the laboratory and are found to be in good agreement. The studies lead to a better understanding of the behavior of the system at different stages of loading