Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods. INDEX TERMSNeural network (NN), deep neural network (DNN), autoencoder (AE), image classification.

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The electroencephalogram (EEG) is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT). To classify the EEG signal, we used a radial basis function neural network (RBFNN). As shown herein, the network can be trained to optimize the mean square error (MSE) by using a modified particle swarm optimization (PSO) algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques.

Glioblastoma (GBM) is a stage IV aggressive malignant brain tumor, which is generally found in the cerebral hemispheres of the brain [1]. The treatment of GBM tumor is very difficult and cure is not possible in most of the cases. The treatment can only slow down the progress of the cancer and may reduce the symptoms and discomfort. The diagnosis of GBM contains neurological exams, imaging tests i.e. MRI and biopsy. The GBM tumor may occur at any age; however, the high majority of the patients are adults. The symptoms of GBM may include worse headache, nausea, vomiting and seizures (Fig. 1). Medical science is equipped with advanced devices and technology. MRI machines are able to capture high contrast images of the brain and other parts of the body. These MRI scans are very useful to diagnose and detect tumors and other defected cells. However, sufficient knowledge and experience is desirable in order to read and understand these MRI scans. Sometimes, unavailability of such trained people may delay the diagnosis process. Therefore, in order to automate the process, a classification model can be developed using machine learning methods. Artificial neural

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Search engine technology plays an important role in web information retrieval. However, with Internet information explosion, traditional searching techniques cannot provide satisfactory result due to problems such as huge number of result Web pages, unintuitive ranking etc. Therefore, the reorganization and post-processing of Web search results have been extensively studied to help user effectively obtain useful information. This paper has basically three parts. First part is the review study on how the keyword is expanded through truncation or wildcards (which is a little known feature but one of the most powerful one) by using various symbols like * or! .The primary goal in designing this is to restrict ourselves by just mentioning the keyword using the truncation or wildcard symbols rather than expanding the keyword into sentential form. Second part consists of the review on subdivision based on wildcards. It is based on the observation that documents are often found to contain terms with high information content which summarize their subject matter. The third part consists of a proposed algorithm based on the above two. The main goal of this paper is to develop a very efficient search technique by which the information retrieval will be very fast, reducing the amount of extra labor needed on expanding the query.

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