Epilepsy is a neurological disorder resulting from unusual electrochemical discharge of nerve cells in the brain, and EEG (Electroencephalography) signals are commonly used today to diagnose the disorder that occurs in these signals. In this study, it was aimed to use EEG signals to automatically detect pre-epileptic seizure with machine learning techniques. EEG data from two epileptic patients were used in the study. EEG data is passed through the preprocessing stage and then subjected to feature extraction in time and frequency domain. In the feature extraction step 26 features are obtain to determine the seizure time. When the feature vector is analyzed, it is observed that the characteristics of the pre-seizure and non-seizure period are unevenly distributed. A systematic sampling method has been applied for this imbalance. For the balanced data, two test sets with and without Eta correlation are established. Finally, the classification process is performed using the k-Nearest Neighbor classification method. The obtained data are evaluated in terms of Eta-correlated and uncorrelated accuracy, error rate, precision, sensitivity and F-criterion for each channel. 

Medical imaging appliances play a pivotal role in preventive medicine as the industry combat to low patient expense and acquire early disease estimation using nonintrusive methods. There are proprietary software packages which provide fast development for designing image processing algorithms. Another trend is to use open source softwares. With the advancement of VLSI (Very Large Scale Integration) technology, hardware implementation has also become an alternative. Proprietary hardwares provide flexibility, efficient power and timing constraints whereas open source hardwares provide optimum quality and cost constraints. The Present study is useful for image architects, researchers, biologists to learn various proprietary and open sources softwares as well as hardwares utilized for distinct applications of the healthcare industry. 

Image provides complete detailed information for thing or object. It is considered as an important aspect of analyzing the details of various objects or environments of real life applications. From analyzing or studying images, various techniques come into existence. These include zooming, watermarking, hazing, and compression. Each has its own advantages and disadvantages with respect to various implicit functions defined for the techniques. The research paper focuses on watermarking techniques. The techniques of watermarking have their advantages and outperforms better when combined with wavelets transformations (DWT) followed by interpolations. The wavelets and interpolations provide a good quality enhanced and zoomed watermarked images at the time of its encoding and decoding processes. The images are embedded with sample images considered as hidden information. After the extraction process image interpolation method is applied to the image to get a quality image. The process is suggested in order to view the changed pixels of images after encoding of two images. The combination of DWT watermarking and interpolation provides 52% better results when compared to existing techniques. 

The field of fraud identification is reaching a very high proportion in the society, thus leading to an increase in the need for fingerprint-based identification. This paper presents ASIC implementation of fingerprint recognition based on Overlap-add method and Integer Wavelet Transforms. In overlap-add method, the present output overlaps the next output and in the integer to integer wavelet, low component at 2nd level decomposition is taken as approximate integer value. The implementation presents an analysis for speed, area and power dissipation between the two algorithms and other methods.

Human gait recognition is an emerging research topic in the biometrics research field. It has recently gained a wider interest from machine vision research community because of its rich amount of merits. In this paper, a robust energy blocks based approach is proposed. For each silhouette sequence, gait energy image (GEI) is generated. Then it is split into three blocks, namely lower legs, upper-half and head. Further, Radon transform is applied to three energy blocks separately. Then, standard deviation is used to capture the variation in radial axis angle. Finally, support vector machine classifier (SVM) is effectively used for the classification procedures. The more prominent gait covariates such as multi views, backpack, carrying, least number of frames, clothing and different walking speed conditions are effectively addressed in this work by choosing sequential, even, odd and multiple’s of three numbering frames for each sequence. Extensive experiments are conducted on four considerably large, publicly available standard datasets and the promising results are obtained.

Designing an efficient watermarking scheme that can achieve better robustness with limited visual quality distortion is the most challenging problem. In this paper, robust digital image watermarking scheme based on edge detection and singular value decomposition (SVD) is proposed. Two sub-images, which are used as a point of reference for both watermark embedding and extracting, are formed from blocks that are selected based on the number of edges they have. Block based SVD is performed on sub-images to embed a binary watermark by modifying the singular value (S). A population-based stochastic optimization technique is employed to achieve enhanced performance by searching embedding parameters which can maintain a better trade-off between robustness and imperceptibility. The experimental results show that the proposed method achieves improved robustness against different image processing and geometric attacks for selected quality threshold. The performance of the proposed scheme is compared with the existing schemes and significant improvement is observed.

Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported [1]. Nowadays early detection of breast cancer became very important. Mammogram - a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations. 
The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.