INTRODUCTION:In the year 1895 the X-ray images were discovered. Since then the medical imaging has got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the evolution of Computer aided Diagnosis(CAD). OBJECTIVES: To develop a Computer Aided Diagnosis (CAD) system to detect the bone fracture which helps the radiologists (or) the Orthopaedics by interpreting the medical images in short duration. METHODS: In this paper, an effective automated bone fracture detection is proposed using enhanced Haar Wavelet Transform, Scale-Invariant Feature Transform (SIFT) and back propagation neural network. The former two techniques are used for feature extraction and the latter one is used for classification of fracture images. Simultaneously, the usage of enhanced Haar Wavelet Transforms and SIFT are phenomenally improves the quality of the X-ray image. Further in this work, k-means clustering based 'Bag of Words' methods are used to extract enhanced features extracted from SIFT. The classification phase of this proposed technique uses the classical back propagation neural network that contains 1024 neurons in 3-layers. RESULTS: The experimental validation of this proposed scheme performed using nearly 300 different bone fractures x-ray images confirmed a better classification rate of 93.4%. CONCLUSIONS: The experimental results of the proposed computer aided technique are proven to be better than the detection technique facilitated with the traditional SIFT technique.

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For many data mining and machine learning applications predicting minority class samples from skewed unbalanced data sets is a crucial problem. To address this problem, we propose a majority filter-based minority prediction (MFMP) approach for unbalanced datasets. The MFMP adopts an unsupervised learning technique for selecting samples for supervised learning. The approach is based on two steps. In the first-step, minority samples are clustered and majority class samples that are out of minority classification regions are identified. This improves minority prediction rate. In the second step majority samples are randomly selected in individual clusters and this enhances majority prediction rate. Experimentally we studied the behavior of MFMP approach and compared with the traditional random under-sampling approach on a synthetic data set and three UCI repository datasets using the following classifiers: decision tree, k-nearest neighbor, Naive Bayes and Radial basis function network. Precision, Recall and F-Measure are used for evaluating performance of classifiers. The experimental evidence suggests that MFMP approach exhibits good prediction rates over minority and majority classes on all classifiers. Furthermore, the proposed approach outperforms the traditional random under-sampling approach. MFMP applied on the decision tree gave better prediction as compared to other classifiers studied.

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Let A 1 , A 2 ,....An be the given sequence of n matrices, generally matrix chain multiplication algorithm is used to obtain its-product with minimum cost(lowest cost). However the matrix chain multiplication is a dynamic programming paradigm and takes O(n 3 ) computational complexity. Here we present improved algorithm for matrix chain multiplication with minimum space and time complexities. The viability of this new algorithm is demonstrated using few examples and the performance is computationally verified. This algorithm does not take O(n 3 ) if any two of the S values are not same and O(n 3 ) when the two values of S are same in the worst case.

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In recent years, advanced information systems have enabled collection of increasingly large amounts of data that are sequential in nature. To analyze huge amounts of sequential data, the interdisciplinary field of Knowledge Discovery in Databases (KDD) is very useful. The most important step within the process of KDD is data mining, which is concerned with the extraction of the valid patterns. Recent research focus in data mining includes stream data mining, sequence data mining, web mining, text mining, visual mining, multimedia mining and multi-relational data mining. Sequence data may be discrete or continuous in nature. Most of the research on discrete sequence data concentrated on the discovery of frequently occurring patterns. However, comparatively less amount of work has been carried out in the area of discrete sequence data classification. In this chapter, data taxonomy is introduced with a review of the state of art for sequence data classification. The usefulness of embedding partial subsequence information extracted using sliding window technique into traditional classifier like kNN has been demonstrated. kNN has been tested with various vector based distance/similarity metrics. Further, with the use of S3M similarity metric, the full subsequence information embedded in the data sequences is extracted. The experimental data taken is DARPA’98 IDS benchmark dataset collected from UCIML dataset repository. The chapter closes by pointing out various application areas of sequence data and also the open issues in sequence data classification problem.