Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

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

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Learning From Cross Country Experiences In Housing: A Micro Finance Approach For Inclusive Housing In India

Housing is one of the primary human needs. It is second only to the need for food and clothing. From a macro perspective, housing is an industry that can prove itself to be a growth engine for a nation, particularly a developing nation like India. Housing has been one of the top priorities for the various governments in India since the seventies. The need for housing has been increasing at a phenomenal pace in India and so also the need for housing finance. Since the growth in supply of housing could not keep pace with the growth in its demand, housing shortage has been on the rise over the years. Housing finance industry which was relatively dormant till the early nineties underwent sweeping changes ever since the initiation of financial sector deregulation measures. Financial deregulation measures brought about several changes in this industry, the first and foremost being the fast growth rate in the industry coupled with cutthroat competition among the industry players. This trend has been quite prominent since the entry of commercial banks into this arena. Accordingly, there has been a surge in the growth of retail (personal) loans segment, particularly in respect of housing loans. This is evident from the fact that housing loans disbursed by banks as a percentage of their total loans has increased from just 2.79% as of end-March 1997 to as high as 12.52% as of end-March 2007. Thus, there has been an unprecedented growth rate in the disbursement of housing loans by banks, and as of 31 March 2007 the outstanding balance of housing loans by all banks in India stands at Rs.230689 Crore, as against just Rs.7946 Crore as of 31 March 1997, the growth rate being 35.82 %CAGR (for the eleven years’ period, FY 1997-‘2007). However, in spite of the impressive growth in housing finance over the years, there are growing apprehensions regarding its inclusiveness, i.e. accessibility to the common man, the underprivileged sections of the society to housing finance etc. Of late, it is widely recognized that formal housing finance system, particularly the commercial banks (CBs) – most dominant among the players – is fast becoming exclusive in operations, with nearly 90% of the total housing credit going to the rich and upper middle income group, primarily the salaried class. The case of housing finance companies (HFCs) is quite similar in this regard. The poor and other marginalized sections are often deprived of adequate credit facilities for housing purpose. Studies have revealed that urban housing poverty is much more acute than the rural probably because of the very fast process of urbanization coupled with constant rural to urban migration