Wednesday 24 June 2015

Analysis of Privacy Challenges and Security Concerns in Cloud Computing

Abstract— Cloud computing is a method to enhance the capacity dynamically without investing in new infrastructure, training new personnel, or licensing new software.  It can be viewed as a cost effective solution to various security threats.  It extends the existing capabilities of Electronics and Communication world and its ongoing capabilities.  In the last few years, cloud computing has grown from being a promising business concept to one of the fast growing segments of the Communication industry at large. But as more and more information on individuals and companies are added in the cloud, concerns are beginning to grow about just “how safe” an environment it is. Despite of all the hype surrounding the cloud, enterprise customers are still hesitating to deploy their business in the cloud. Security is one of the major concern which curbs the growth of cloud computing and complications with data privacy/security and data protection continue to plague the market. In this paper, a survey of the different security risks that pose a concern to the cloud is presented. This paper is a survey more specific to the different security issues that has emanated due to the nature of the service delivery models of a cloud computing system.

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A Review on Health Insurance Claim Fraud Detection

Abstract— The anomaly or outlier detection is one of the applications of data mining. The major use of anomaly or outlier detection is fraud detection. Health care fraud leads to substantial losses of money each year in many countries. Effective fraud detection is important for reducing the cost of Health care system. This paper reviews the various approaches used for detecting the fraudulent activities in Health insurance claim data. The approaches reviewed in this paper are Hierarchical Hidden Markov Models and Non Negative Matrix Factorization. The data mining goals achieved and functions performed in these approaches have given in this paper. 

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Design a Planar Antenna of Integrated Microwave Imaging Radar for Detecting Earlier Breast Cancer

Abstract In this paper, a compact design and construction of micro strip feed planar antenna comprising both coupling and decoupling structure is presented. Each antenna element working under wider frequency range of about 2-16 GHz. To achieve a better imaging radar tool a dedicated radar transceiver is proposed. Segmentation is applied to the decoupled image from transceiver. After, a novel hybrid artifact removal algorithm for microwave breast imaging applications is presented, which combines the best attributes of two existing algorithms to effectively remove the early-stage artifact while preserving the tumor dynamic range of bandwidth is proposed. . The main concern of this project is to diagnose the benign tissue at the earliest as most cancer cells are completely curable at the earlier stage. The tool is best suited for diagnosis of earliest breast cancer causing tumor cells having resolution of about 3 mm. 

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Fuzzy Hv-submodules in Γ-Hv-modules

Abstract In this paper, we introduced the concept of Γ-Hv-modules, which is a generalization of Γ -modules and Hv-modules. The notion of -fuzzy Hv-submodules of a Γ-Hv-module is provided and some related properties are investigated.

Tetrolets-based System for Automatic Skeletal Bone Age Assessment

        This paper presents the design and implementation of the tetrolets based system for automatic skeletal Bone Age Assessment (BAA). The system works according to the renowned Tanner and Whitehouse (TW2) method, based on the carpal and phalangeal Region of Interest (ROI). The system ensures accurate and robust BAA for the age range 0-10 years for both girls and boys. Given a left hand-wrist radiograph as input, the system estimates the bone age by deploying novel techniques for segmentation, feature extraction, feature selection and classification. Tetrolets are used in combination with Particle Swarm Optimization (PSO) for segmentation. From the segmented wrist bones, the carpal and phalangeal ROI are identified and are used in morphological feature extraction. PCA is employed as a feature selection tool to reduce the size of the feature vector. The selected features are fed in to an ID3 decision tree classifier, which outputs the class to which the radiograph is categorized, which is mapped onto the final bone age. The system was evaluated on a set of 100 radiographs (50 for girls and 50 for boys), and the results are discussed. The performance of system was evaluated with the help of radiologist expert diagnoses. The system is very reliable with minimum human intervention, yielding excellent results.

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Engineering Journal September 2021

Effect of Processing Method of Kidney Beans (Phaseolus Vulgaris) on Carcass Quality, Organ Weight and Organoleptic Properties of Broiler A...