The medical diagnosis process can be interpreted as a decision making process, during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and from his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and fast way. This paper presents a decision support system for heart disease classification based on support vector machine (SVM) and Artificial Neural Network (ANN). A multilayer perceptron neural network (MLPNN) with three layers is employed to develop a decision support system for the diagnosis of heart disease. The multilayer perceptron neural network is trained by back-propagation algorithm which is computationally efficient method. Results obtained show that a MLPNN with back-propagation can be successfully used for diagnosing heart disease than support vector machine.

In context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it. Traffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India. Initial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p < 0.01) and vehicular speed (p < 0.05) have substantial impact on traffic noise apart from traffic volume and type of road. The study suggests that honking must also be a component in traffic noise assessment and to identify and monitor “No Honking” zones in urban agglomerations.

Data streams are viewed as a sequence of relational tuples (e.g., sensor readings,call records, web page visits) that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques and approaches. Classification techniques fail to successfully process data streams because of two factors: their overwhelming volume and their distinctive feature known as concept drift. Concept drift is a term used to describe changes in the learned structure that occur over time. The occurance of concept drift leads to a drastic drop in classification accuracy. The recognition of concept drift in data streams has led to sliding-window approaches also different approaches to mining data streams with concept drift include instance selection methods, drift detection, ensemble classifiers, option trees and using Hoeffding boundaries to estimate classifier performance. This paper describes the various types of concept drifts that affect the data examples and discusses various approaches in order to handle concept drift scenarios. The aim of this paper is to review and compare single classifier and ensemble approaches to data stream mining respectively.

The existence of the World Wide Web has caused an information explosion. Readers are overloaded with lengthy text documents where a shorter version would suffice. All computer users, be it professionals or novice users, are particularly affected by this predicament. There exists an urgent need for the discovery of knowledge embedded in digital documents. This paper intends to investigate techniques and methods used by researchers for automatic text summarization. Special attention is paid to Bio-inspired methods for text summarization.

Fine Classification & Recognition of Hand Written Devnagari Characters with Regular Expressions & Minimum Edit Distance Method