No abstract

Abstract-Cloud computing exhibits a remarkable potential to offer cost-effective and more flexible services on-demand to the customers over the network. It dynamically increases the capabilities of the organization without training new people, investment in new infrastructure or licensing new software. Cloud computing has grown dramatically in the last few years due to the scalability of resources and appear as a fast-growing segment of the IT industry. The dynamic and scalable nature of cloud computing creates security challenges in their management by examining policy failure or malicious activity. In this paper, we examine the detailed design of cloud computing architecture in which deployment models, service models, cloud components, and cloud security are explored. Furthermore, this study identifies the security challenges in cloud computing during the transfer of data into the cloud and provides a viable solution to address the potential threats. The task of Trusted Third Party (TTP) is introducing that ensure the sufficient security characteristics in the cloud computing. The security solution using the cryptography is specifically as the Public Key Infrastructure (PKI) that operates with Single-Sign-On (SSO) and Lightweight Directory Access Protocol (LDAP) which ensure the integrity, confidentiality, availability, and authenticity involved in communications and data.

No abstract

No abstract

The current study examines the special class of a generalized reaction-advection-diffusion dynamical model that is called the system of coupled Burger’s equations. This system plays a vital role in the essential areas of physics, including fluid dynamics and acoustics. Moreover, two promising analytical integration schemes are employed for the study; in addition to the deployment of an efficient variant of the eminent Adomian decomposition method. Three sets of analytical wave solutions are revealed, including exponential, periodic, and dark-singular wave solutions; while an amazed rapidly convergent approximate solution is acquired on the other hand. At the end, certain graphical illustrations and tables are provided to support the reported analytical and numerical results. No doubt, the present study is set to bridge the existing gap between the analytical and numerical approaches with regard to the solution validity of various models of mathematical physics.

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means Clustering and Spectral Clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with Kmeans clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

No abstract