Side-channel analysis (SCA) exploits the data leaked through outputs to reveal the secret key of cytological modules. the important threat of SCA lies within the ability to mount attacks over tiny components of the key and to mixture data over completely different encryptions. The threat of SCA are often dynamic the secret key at each run. Indeed, several contributions within the domain of outflow resilient cryptography tried to attain this goal. However, the projected solutions were computationally intensive and were designed to resolve the matter of the present cytological schemes. The generic framework of light-weight key change which will shield the present cytological standards and valuate the minimum necessities for heuristic SCA-security. Propose a whole resolution to shield the implementation of any standard mode of advanced cryptography customary. The answer maintains identical level of SCA-security (and typically better) because the state of the art, at a negligible space overhead whereas doubling the turnout.,

Keywords: side channel analysis, Advanced cryptography customary, key updation.

Hepatic cancer is the fifth most common cancer in the world and the majority of patients with liver cancer as a result will die within one year. Computer Aided Diagnosis (CAD) systems help a great deal in aiding the detection and diagnosis of cancer. They serve as an effective second opinion. Computed Tomography (CT) is one of the commonly used imaging modalities in the cancer domain since it gives the intrinsic details of the tissues. Recent developments in Computed tomography have been very helpful to avoid the effect of radiation during the scanning process. The four major steps in the computer aided diagnosis are the pre-processing of the images, segmentation of the regions of interest, feature extraction and classification. This paper gives a brief summary of the recent developments in the segmentation of the region of interest and the classification of liver diseases using the Computed Tomography images and the results of the existing methodologies.

Keywords: CAD, Segmentation, Classification, Liver Tumor, CT

ELM-based hierarchical learning framework for multilayer perception in which it has self-taught feature extraction followed by supervised feature classification and they are bridged by random initialized hidden weights is built in a multilayer manner. H-ELM training is divided into two separate phases: 1) unsupervised hierarchical feature representation and 2) supervised feature classification. A new H-ELM-based auto-encoder is developed to extract multilayer sparse features of the input data. The original ELM-based regression is performed for final decision making. H-ELM-based feature extraction and detection algorithms are developed for practical computer vision applications, such as object detection, recognition, and tracking. Since the supervised training is implemented by the original ELM, the unsupervised building blocks of the H-ELM architecture. The hidden layers of the framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. The advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed. To overcome this, in the proposed system, these issues are overcome by implementing with image and text using Back-Propagation–Extreme Learning Machine algorithm (BP-ELM) and then classified both text and image by using Support Vector Machine algorithm. Hence the proposed system detects the image and text in the form accuracy and efficiency of an image..

Keywords: Feature Extraction; Image Detection; Image Retrieval; BackPropagation-ELM; Hierarchical Extreme Learning Machine (HELM); Image and Text.)

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as location based services. At present, users always make use of location-based services via smart phones, and can obtain information anywhere and anytime. Therefore, we proposed an approach that focuses on location-based information and location-based service to address above issues. Using the proposed approach, location-based information and location-based service will be delivered according to the user context, and the information and service results will be shown on the smart phone. Location-based services are growing in popularity due to the ubiquity of smart phone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. We implemented our system as a location-based search engine that uses helium earth orbit (HEO) about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search).

Keywords: The rapid development of Internet of things (IoT), location-based information, location-based service, helium earth orbit (HEO), page-rank (Google Local Search)

Identifying Diabetes from the tongue obtained before and after food are acquired using high resolution cameras from smart phone. Images are then individually processed (filtered, segmented) and then texture & color analysis are done. The differences between two images (before and after food) are then comparatively analyzed to predict the presence of diabetes..

Keywords: Color Analysis, Image Filtration, Image Segmentation, Texture Analysis