Inventory management in public sector electrical industry in Kerala

The thesis entitled Inventory Management In Public Sector Electrical Industry In Kerala. Investigations were carried out on inventory management in public sector electrical industry in Kerala and suggest methods to improve their efficiency. Various aspects of inventory management, its scope and need in industry are detailed. The objectives of the present study concentrates to get an overall view of the system of inventory management, assess the positions and levels of inventory. It analyzes the inventory management policies and practices, the organizational set-up for materials by the electrical undertakings. The study examines the liquidity of the electrical undertakings as well as techniques of inventory management in the electrical industry in Kerala. Hypotheses state that the existing organizational systems and practices are inadequate to ensure efficient management of inventories in electrical industry. Introduction of scientific inventory techniques has a favourable effect on the workings of inventory departments. The financial performance of the public sector electrical undertakings is not at all satisfactory on account of the high raw material costs, heavy borrowings and huge interest burdens. The scope of this study is limited to the assessment of savings, in inventories of electrical products due to inventory management. The methodology of the study is to project the cost reduction of the inventory department on the basis of data collected and to validate this projection with the aid of analysis and survey. The limitations of the study is that the data obtained relate to the period 1989-90 and earlier and the current position is not available and uniform norms cannot be applied to evaluate different inventory management organisation.

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