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Tema, Ghana

Ding W.,Weifang University | Ghansah B.,Data Link Institute | Wu Y.,Jiangsu University
International Journal of Engineering Research in Africa | Year: 2016

Virtualization and Cloud computing are two popular research directions in recent times. Today, Virtualization is being used by a growing number of organizations to reduce power consumption, Server Consolidation, Testing and Development, Dynamic Load Balancing and Disaster Recovery, Virtual Desktops and Improved System Reliability and Security. Virtualization also provides high availability for critical applications, and streamlines application deployment and migrations. Through cloud computing, Information Technology resources can be delivered as services over the Internet to the end user. Virtualization is one of such important core technologies of cloud computing. In this paper, we present a detailed review on virtualization. Furthermore, three technologies for x86 CPU virtualization and the architecture of Xen are introduced. Specifically, we propose an architecture of the cloud computing platform based on virtualization. Finally, we discuss the performance evaluation of server virtualization in saving cost, time and energy consumption. © 2016 Trans Tech Publications, Switzerland. Source


Ghansah B.,Jiangsu University | Ghansah B.,Data Link Institute | Wu S.,Jiangsu University
International Journal of Engineering Research in Africa | Year: 2015

Opposed to centralized search where Websites are crawled and indexed, Distributed Information Retrieval (DIR), also known as Federated Search, is a powerful way to comprehensively search multiple databases in real-time simultaneously. DIR is preferred to centralized search environments in a number of ways, characteristically among them are: 1. the diversity of resources that are made available; 2. improving scalability and reducing server load and network traffic; 3. the leverage of accessing the hidden or deep Web. There are three major phases/tasks of a DIR (i) resource description or collection representation (ii) resource selection and (iii) result merging. This paper aims at providing a comprehensive review on the various phases of DIR and also some current strategies being recommended in enhancing and improving the smooth implementation of a DIR system. © (2015) Trans Tech Publications, Switzerland. Source


Ghansah B.,Jiangsu University | Ghansah B.,Data Link Institute | Wu S.,Jiangsu University
International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems | Year: 2016

Resource Selection is an important step in a federated search environment. The goal of this work was to improve the collection selection process by selecting collections in terms of relevance and diversity, to best answer a user's query. Sampled documents from the Central Sample Database are first ranked by Indri retrieval algorithm and later re-ranked by a Mean-Standard deviation method that reduces uncertainty and improves diversity of collection sources. A comparative evaluation with the R-based diversification metrics shows that the proposed method significantly outperforms the baseline diversification methods; ReDDE+MMR, ReDDE+MAP-IA and state-of-the-art resource selection methods (ReDDE and CORI) in all metrics. © 2016 World Scientific Publishing Company. Source


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Benuwa B.-B.,Data Link Institute | Ghansah B.,Data Link Institute | Wornyo D.K.,Data Link Institute | Adabunu S.A.,Koforidua Polytechnic
International Journal of Engineering Research in Africa | Year: 2016

Particle swarm optimization (PSO) is a heuristic global optimization method. PSO was motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and the ability to adapt various dynamic environments, makes PSO one of the most important swarm intelligence algorithms and ostensibly the most commonly used optimization technique. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation. We hope that this survey would be beneficial to researchers studying PSO algorithms and would also serve as the substratum for future research in the study area, particularly those pursuing their career in artificial intelligence. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed. © 2016 Trans Tech Publications, Switzerland. Source

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