Efficient Web Service Discovery and Selection Model

Selection of an optimal web service is a challenging task due to the uncertainty of Quality of Service, which is the deciding factor to identify the accurate web service. Several discovery mechanisms have proposed but most of the research work does not consider the non-functional characteristics called Quality of service. The proposed model for web service selection combines two techniques. First, with Skyline method reduce the search space by filtering the redundant service and secondly to calculate the Relevancy function to normalize the skyline services. The experimental results show that the proposed technique outperforms the existing method

Clustering of datasets using PSO-K-Means and PCA-K-means

Abstract Cluster analysis plays indispensable role in obtaining knowledge from data, being the first step in data mining and knowledge discovery. The purpose of data clustering is to reveal the data patterns and gain some initial insights regarding data distribution. K-means is one of the widely used partitional clustering algorithms and it is more sensitive to outliers and do not work well with high dimensional data. In this paper, K-means has been integrated with other approaches to overcome the shortcomings hereby improving the accuracy of clustering. In this paper, basic k-means and the combination of k-means with PCA and PSO are applied on various datasets from UCI repository. The experimental results of this paper show that PSO-K-means and PCA-KMeans improves the performance of basic K-means in terms of accuracy and computational time

Medical Data Analytics for Secure Multi-party-primarily based Cloud Computing utilizing Homomorphic Encryption

Cloud computing has emerged as a vibrant part of today's modern world, providing computer services such as data storage, managing and processing via the internet. For the most part, cloud applications emphasize a multi-tenant structure to provide support for several customers in a single instance. A multi-tenancy situation involving the allocation of resources in cloud storage and the risks associated with it, in which confidentiality or integrity may be compromised. Homomorphic encryption is one such technique which guarantees to franchise in safeguarding information under cryptographic domain. The proposed modified Algebra Homomorphic Encryption scheme based on updated ElGamal (AHEE) encryption scheme is designed in such a way that the cloud administrators do not obtain any information about the medical data. This scheme is quantitatively evaluated using metrics such as encryption time and decryption time. The experimental results using UCI Machine Learning Repository ECG data set show that the proposed scheme achieved shorter encryption time of 6.61 ms and decryption time of 5.94 ms and also analyze this secured datum using big data analytics

Medical Data Analytics for Secure Multi-party-primarily based Cloud Computing utilizing Homomorphic Encryption

692-698Cloud computing has emerged as a vibrant part of today's modern world, providing computer services such as data storage, managing and processing via the internet. For the most part, cloud applications emphasize a multi-tenant structure to provide support for several customers in a single instance. A multi-tenancy situation involving the allocation of resources in cloud storage and the risks associated with it, in which confidentiality or integrity may be compromised. Homomorphic encryption is one such technique which guarantees to franchise in safeguarding information under cryptographic domain. The proposed modified Algebra Homomorphic Encryption scheme based on updated ElGamal (AHEE) encryption scheme is designed in such a way that the cloud administrators do not obtain any information about the medical data. This scheme is quantitatively evaluated using metrics such as encryption time and decryption time. The experimental results using UCI Machine Learning Repository ECG data set show that the proposed scheme achieved shorter encryption time of 6.61 ms and decryption time of 5.94 ms and also analyze this secured datum using big data analytics

Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time

Fusion of MRI and CT Images with Double Density Dual Tree Discrete Wavelet Transform

Abstract — Data fusion technique is a powerful tool for extracting higher quality information from large amount of remote sensing images or various types of medical images and eliminating redundancy among these images. Traditional multi-resolution analysis image fusion methods always decompose multitemporal images into low and high frequency parts, then fuse the low frequency part of each image into one low frequency part and do not deal with fusion of high frequency parts which represent image' details, such as edges, corners and ridges. In this paper, the fusion of the CT image which gives the information about boundary of the affected area and MR image which gives the information about the tissues affected by diseases is considered and the resultant image provides all boundary and internal details for diagnostic purpose. Entropy based analysis is done on fused CT and MR images using lifting wavelet and double density dual tree DW

Banking Sector; Financial Loss.

different aspects of human life and has made our lives simpler. It has been applied in different industries and has made business processes simpler by sorting, summarizing, coding, and customizing the processes. However, ICT has brought unintended consequences in form of different cybercrimes. Cybercrimes have affected different industries and banking sector is one of them which has witnessed different forms of cybercrimes like ATM frauds, Phishing, identity theft, Denial of Service. The paper discusses the problem of cybercrime in the banking sector and its impact on the bank s finances. It assesses the cybercrime scenario and identifies the actors involved in the scenario. It also examines the different types of cybercrimes which plague the banking sector and the motives of the cyber criminals behind such acts. The financial loss in the banking sector is huge across the globe both in terms of combating the cyber-attacks and on development of systems, so that such attacks need to be prevented in the future. Introduction to Cybercrime in Bankin