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

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

STANDARDIZATION OF PROCESS PARAMETERS FOR READY TO EAT FISH PRODUCTS IN INDIGENOUS POLYMER COATED TIN FREE STEEL CANS

In the present work, Indigenous polymer coated Tin Free Steel cans were analyzed fortheir suitability for thermal processing and storage of fish and fish products following standard methods. The raw materials used for the development of ready to eat thermally processed fish products were found to be of fresh condition. The values for various biochemical and microbiological parameters of the raw materials were well within the limits. Based on the analysis of commercial sterility, instrumental colour, texture, WB-shear force and sensory parameters, squid masala processed to F0 value of 8 min with a total process time of 38.5 min and cook value of 92 min was chosen as the optimum for squid masala in tin free steel cans while shrimp curry processed to F0 7 min with total process time of 44.0 min and cook value of 91.1 min was found to be ideal and was selected for storage study. Squid masala and shrimp curry thermally processed in indigenous polymer coated TFS cans were found to be acceptable even after one year of storage at room temperaturebased on the analysis of various sensory and biochemical parameters. Analysis of the Commission Internationale d’ Eclirage L*, a* and b* color values showed that the duration of exposure to heat treatment influenced the color parameters: the lightness (L*) and yellowness (b*)decreased, and the redness (a*) significantly increased with the increase in processing time or reduction in processing temperature.Instrumental analysis of texture showed that hardness-1 & 2 decreased with reduction in retort temperature while cohesiveness value did not show any appreciable change with decrease in temperature of processing. Other texture profile parameters like gumminess, springiness and chewiness decreased significantly with increase of processing time. W-B shear force values of mackerel meat processed at 130 °C were significantly higher than those processed at 121.1 and 115 °C. HTST processing of mackerel in brine helped in reducing the process time and improving the quality.The study also indicated that indigenous polymer coated TFS cans with easy openends can be a viable alternative to the conventional tin and aluminium cans. The industry can utilize these cans for processing ready to eat fish and shell fish products for both domestic and export markets. This will help in reviving the canning industry in India.