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Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Spatial data mining is the process of discovering interesting characteristics and patterns that may implicitly exist in spatial database. A huge amount of spatial data and newly emerging concept of Spatial Data Mining which includes the spatial distance made it an arduous task. Knowledge discovery in spatial databases is the extraction of implicit knowledge, spatial relations and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. Co-location pattern discovery is the process of finding the subsets of features that are frequently located together in the same region. Spatial co-location patterns associate the co-existence of non-spatial features in a spatial neighborhood. The Previous methods of mining co-location patterns, converts neighborhoods of feature instances to item sets and applies mining techniques for transactional data to discover the patterns, combines the discovery of spatial neighborhoods with the mining process. It is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. Previous works on discovering co-location patterns is based on participation index and participation ratio. In this paper we address the problem of mining co-location patterns with a novel method called Mediod participation index Our technique is an extension of maximal participation ratio and deploys the idea of K-mediods from clustering algorithms.. As demonstrated by experimentation, our method yields significant performance improvements compared to previous approaches.

Security requirement is an important aspect of system's development. There are numerous security requirements methodologies, which have been developed till date. Research is still going on to improve or create new methodologies that will make a system as secure as possible. Asset management, risk assessment, validation of functional and non-functional security requirements and security requirements elicitation are some of the important part of a security requirements methodology. However most of the security requirements methodologies in use today such as SQUARE, UMLSec, Secure Tropos and CORAS fail to perform one or more of these functions. Additionally, very few methodologies focus on critical infrastructure industrial systems like SCADA. This paper introduces a methodology (MAR(S)2) that incorporates all the important functions, which will produce a strong methodology that produces a profound and well-defined security requirements for SCADA systems.

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This paper proposed a novel Block based Mean Shift Image Segmentation Algorithm to significantly reduce the computation and improve the segmentation accuracy for high resolution Medical Image. One of the challenging tasks in the image analysis and computer vision area is to correctly classify the pixels as there are no crisp borders among entities in an image. In this proposed methodology, it is observed that the computational complexity of the procedure is diminished by combining the pixels of an image of size MXN into non overlapping image blocks of size 3x3 by eliminating the iterative way of the mean shift procedure. This proposed algorithm shrinks the size of the image by one third of its original image for the computational purpose and then equalizes the number of computations for each new image pixel by constructing links between pixels using their first mean-shift vectors without any iteration process. The accurateness and effectiveness of the proposed methodology is matched with the existing Iterative Mean Shift Algorithm by accomplishing the empherical experiments on the Medical Images (Pathologies Buccales and Eye Retina) composed along with the similarity measures.

In this paper, we propose an approach using multilevel and multiple approaches for Feature Reweighting for CBIR system to reduce semantic gap using Relevance feedback. The first step of this approach does analysis on the positive and negative images, Second step calculates normalized feature component sets of images, Third step calculates overall distances between given query image and database images, and the next step calculates Relevance score along with confidence of the image, it is used for Feature Reweighting. All the above methods are performed individually in the previous systems, where as in our propose system we perform all these together. The assumption for the previous relevance feedback systems are that, all the above methods are performed against to the user given feedback. This increases the number of iterations for the retrieval systems. The propose system can do analysis of images, overall distance calculation, automatically calculates the weight of features for an image based on the confidence and score of the relevance before user feedback. And these results are carried forward to the next iteration for further calculations after the user feedback.

The occurrence of imbalanced datasets in medical imaging has proven to be a challenge for the development of models to analyze and evaluate the underlying condition. In this paper, the bias of the chest CT scan dataset is handled by taking discrete splits and employing ResNets to detect COVID-19 in each split. The scraped images were pre-processed using CLAHE histogram for comparison with low contrast images. Multiple ResNets were extended to form an ensemble neural network model using ANNs which handles the class imbalance. The system has an overall accuracy of 87.23% and the performance is assessed for each class. The image features identified are visualized using the GradCAM algorithm and some of the commonly found clinical features in the CT scan images of the patients suffering from this disease are summarized for better understanding the working of the model.

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