Abstract-Weather prediction is a challenging task for researchers and has drawn a lot of research interest in the recent years. Literature studies have shown that machine learning techniques achieved better performance than traditional statistical methods. This paper presents an application of Support Vector Machines (SVMs) for weather prediction. Time series data of daily maximum temperature at a location is analyzed to predict the maximum temperature of the next day at that location based on the daily maximum temperatures for a span of previous n days referred to as order of the input. Performance of the system is observed over various spans of 2 to 10 days by using optimal values of the kernel function. Non linear regression method is found to be suitable to train the SVM for this application. The results are compared with Multi Layer Perceptron (MLP) trained with back-propagation algorithm and the performance of SVM is found to be consistently better.

The bankruptcy prediction model (BPM) is eminently essential for financial institutions to verify the creditworthiness of companies or management. The inability to accurately predict the bankruptcy can destroy the effects of socio-economics. Hence, it is significant to offer financial decision makers with efficient bankruptcy prediction to forestall these loss states. This paper presents a comprehensive review based on various statistical and machine learning techniques to address the issue of bankruptcy prediction. The statistical techniques include, linear discriminant analysis (LDA), multivariate discriminant analysis (MDA) and logistic regression (LR), and machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and decision trees. Traditional statistical techniques were used to handle large data sets without affecting the prediction performance. Furthermore, the machine learning techniques provide greater prediction accuracy than the traditional statistical techniques for smaller data sets. Besides, optimization techniques, such as genetic algorithm (GA) and particle swarm optimization (PSO), were integrated with machine learning techniques to further improve the prediction accuracy for large data sets. This paper conducts a comparative analysis of the various techniques used based on their corresponding benefits and limitations. In our future work, the prediction of bankruptcy may be improved by integrating other heuristic evolutionary algorithms with the machine learning techniques using the Apache Mahout tool. Index Terms-Artificial neural networks (ANN), bankruptcy prediction model (BPM), optimization techniques and support vector machines (SVM).

No abstract

No abstract

Abstract-TextSummarization is a process that converts the original text into summarized form without changing the meaning of its contents. It finds its usefulness in many areas when the time to go through a large content is limited. This paper presents a comparative evaluation of statistical methods in extractive text summarization. Top score method is taken to be the bench mark for evaluation. Modified weighing method and modified sentence symmetric feature method are implemented with additional characteristic features to achieve a better performance than the benchmark method. Thematic weight and emphasize weights are added to conventional weighing method and the process of weight updation in sentence symmetric method is also modified in this paper. After evaluating these three methods using the standard measures, modified weighing method is identified as the best method with 80% efficiency.