The brain tumor is an abnormal cell growth in the human body. To know which type of brain tumor it is and where is the exact location of it. We are using the MR image is a tomographic imaging technique. MRI is based on Nuclear M a g n e t i c R e s o n a n c e signals. A brain tumor is of two types 1. Benignant 2. malignant. Benignant belongs to I and II grade; t h i s type of tumor is not active cells and have a low-grade tumor. It has a uniform structure. Malignant belongs to III and IV grades, this type of tumor are active cells and have a high grade. It has a non-uniformity structure. The initial phase I n p u t MR image is transformed into a binary image by the Otsu threshold technique. The second step k-means segmentation process is used on binary images. Third step D i s c r e t e Wavelet Transform is used on segmented image for extracting the image and it reduces the large dimensionality by using PCA. It identifies the tumor by using S u p p o r t Vector Machine classification it gives the final output of a brain tumor that normal or abnormal. The proposed paper experimented on the detection of brain tumors using classification algorithms dataset about B r a T S dataset and compared with existing methodologies, and it is then proved that superior to existed.

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Cloud computing is a general term for anything that involves delivering hosted services over the Internet, One of the primary issues in cloud computing is implementation of a novel load balancing approach. The demanding thirst for optimal performance of the system is creating research interest in this area. Many Load Balancing algorithms that aim to enhance the overall system performance have been proposed. In this paper, we survey a special group of Load balancing algorithms that have taken inspiration from nature. We provide an overview of the current trends in the field by discussing and comparing these algorithms.

Image Matching technique is regularly on one of the main errands in numerous Photogrammetry and Remote Sensing applications. Based on multi-discipline, the approach of multiple sensor image matching is a novel one established which has vital application in military, civil, medicinal, and certain other domains. However, image matching approach faces numerous challenges, specifically in multi-sensor images where the images are gathered from the different sensor with different intensities, scales, and moments. Thus, a novel image matching approach is introduced in this paper using affinity tensor and HyperGraph Matching (HGM) technique that attempts to overcome certain drawbacks in matching and increases performance accuracy. Hypergraph matching techniques are employed using affinity tensors and consider supersymmetric property during construction. Graphs are constructed using graph theory for both sources, and target image and matching is done using third-order tensors. The experimental outcomes displayed that the proposed technique has good recall, precision, and positive accuracy values compared to the existing two descriptors based and tensor-based matching algorithms.