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

A Study on the Water Retention Characteristics of Soils and their Improvements

Soil moisture plays a cardinal role in sustaining eclological balance and agricultural development – virtually the very existence of life on earth. Because of the growing shortage of water resources, we have to use the available water most efficiently by proper management. Better utilization of rainfall or irrigation management depends largely on the water retention characteristics of the soil.Soil water retention is essential to life and it provides an ongoing supply of water to plants between periods of irrigation so as to allow their continued growth and survival.It is essential to maintain readily available water in the soil if crops are to sustain satisfactory growth. The plant growth may be retarded if the soil moisture is either deficient or excessive. The optimum moisture content is that moisture which leads to optimum growth of plant. When watering is done, the amount of water supplied should be such that the water content is equal to the field capacity that is the water remained in the saturated soil after gravitational drainage. Water will gradually be utilized consumptively by plants after the water application, and the soil moisture will start falling. When the water content in the soil reaches the value known as permanent wilting point (when the plant starts wilting) fresh dose of irrigation may be done so that water content is again raised to the field capacity of soil.Soil differ themselves in some or all the properties depending on the difference in the geotechnical and environmental factors. Soils serve as a reservoir of the nutrients and water required for crops.Study of soil and its water holding capacity is essential for the efficient utilization of irrigation water. Hence the identification of the geotechnical parameters which influence the water retention capacity, chemical properties which influence the nutrients and the method to improve these properties have vital importance in irrigation / agricultural engineering. An attempt in this direction has been made in this study by conducting the required tests on different types of soil samples collected from various locations in Trivandrum district Kerala, with and without admixtures like coir pith, coir pith compost and vermi compost. Evaluation of the results are presented and a design procedure has been proposed for a better irrigation scheduling and management.

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.