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Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA

In medical image processing, segmentation is an important and challenging task. It is classically used to identify object contours and extract the object from the image. Tumor Classification is an significant in medical image analysis since it provides information related to anatomical structures as well as possible anomalous tissues necessary to treatment planning and patient follow-up. In this study, a new approach for automatic classification of brain tumor in enhanced MRI images is developed. Our proposed method consists of Five steps: i) Preprocessing ii) Tumor Region Segmentation iii) Feature Extraction using Wavelet and Level set method iv) Feature Selection and v) Feature Classification using Ada-Boost classifier. The experimental results are validated using the evaluation metrics such as sensitivity, specificity and accuracy. Our proposed system experimental results are compared to other neural network based classifier such as Feed Forward Neural Network (FFNN) and Radial Basics Function (RBF). The classification accuracy of proposed method produces better results compared to other leading tumor classification methods.

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The panorama of the paper is content based image representation and retrieval .The major key flavor is texture content by a set of features having a perceptual meaning. We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, busyness and periodicity (textons). The proposed computational measures can be based upon the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on fundus image data set, the well-known Drive and Stare database. The invisible Comparison is given with statistical and structural methods with probing analysis.