Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

Economic Impacts Of Air Pollution on Human Health and Property Values

The objectives of the present study are to provide a systematic descriptive documentation of the nature of air pollution of the Cochin industrial agglomeration, estimate the willingness to pay for morbidity reduction due to air pollution in observed and hypothetical markets and to estimate the value of welfare loss in the purchase of property due to reduced air quality. This study is an attempt to examine economic impacts of air pollution on the human health and property values in the industrial capital of Kerala. The process of industrialization in Kerala and the increase in air pollution created damages to human, natural and economic resources in the state. The study documents the extent of air pollution and applied econometric approaches to estimate economic impacts of air pollution on human health and property values. The Important sources of air pollution identified in Cochin are emissions from industries and automobiles.

INVESTIGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY

The thesis entitled INVESTIDGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY embodies the results of the investigations carried out on the solvent extraction separation of iron (III) vanadium(V) and titanium (IV) chlorides from the waste chloride liquors of titanium minerals processing industry by employing tributylphosphate (TBT) as an extractant. The objective of this study is to generate the knowledge base to achieve the recovery of iron, vanadium and titanium cvalues from multi- metal waste chloride liquors originating from ilmenite mineral beneficiation industries through selective separation and value added material development

THE CHANGING CONCEPT OF POLITICAL VALUES IN THE HINDI NOVELS OF POST-SIXTIES

Hindi