A.A. Ojugo

Work place: Department of Mathematics/Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

E-mail: arnoldojugo@gmail.com

Website: https://scholar.google.com/citations?user=aEDkRagAAAAJ&hl=en

Research Interests: Soft Computing, Cyber Security, Cloud Computing, Robotics, Machine Learning, Computer Vision


Ojugo, Arnold Adimabua received BSc from Imo State Univ. Owerri in 2000, MSc from NnamdiAzikiwe Univ. Awka in 2005 and PhD from Ebonyi State Univ. Abakiliki in 2012 (Computer Sci.). Currently with Dept. of Math/Comp Sci, Federal Univ. of Petroleum Resources Effurun, Delta State, Nigeria. His research interests: Soft Intelligent Computing, Machine-Learning, Robotics Vision, Data Security/Forensics and Cloud Computing. Editor with Progress for Intelligent Computation and Application and Advancement for Scientific and Engineering Research.Member of: Nigerian Computer Society, Computer Professionals of Nigeria and International Association of Engineers.

Author Articles
Mitigating Technical Challenges via Redesigning Campus Network for Greater Efficiency, Scalability and Robustness: A Logical View

By A.A. Ojugo A.O. Eboka

DOI: https://doi.org/10.5815/ijmecs.2020.06.03, Pub. Date: 8 Dec. 2020

Data transfer over the Internet comes with its range of challenges and associated prospects as a major milestone in the convergence of information and communication technology (ICT). Campus network implemented on IP-telephony defines a range of convergence technologies and applications that refers to a multi-service network that allows integration of data, audio, voice, and video solutions onto a converged infrastructure so that data can be transported via the use of open-source applications, protocols, hardware, and software. The study adopts the Federal College of Education Technical Asaba. It is observed that some issues in its implementation include packet loss, jitters, and latency. Jitters and packet loss can be curbed via an increased bandwidth allocation; while latency is minimized via constant upgrade in network infrastructure to increase speed. Overall, the proposed network seeks to provide its users with mobility, resilience, economy, flexibility, and productivity. Its results recommends that organizations wishing to harness its potentials should join forums and user-groups that will constantly update their knowledge in a bid to help them improve the efficiency and effectiveness of their infrastructure implementation..

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Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning Frameworks

By A.A. Ojugo E. Ben-Iwhiwhu O. Kekeje M.O. Yerokun I.J.B. Iyawa

DOI: https://doi.org/10.5815/ijmecs.2014.08.04, Pub. Date: 8 Aug. 2014

Significant research into the logarithmic analysis of complex networks yields solution to help minimize virus spread and propagation over networks. This task of virus propagation is been a recurring subject, and design of complex models will yield modeling solutions used in a number of events not limited to and include propagation, dataflow, network immunization, resource management, service distribution, adoption of viral marketing etc. Stochastic models are successfully used to predict the virus propagation processes and its effects on networks. The study employs SI-models for independent cascade and the dynamic models with Enron dataset (of e-mail addresses) and presents comparative result using varied machine models. Study samples 25,000 emails of Enron dataset with Entropy and Information Gain computed to address issues of blocking targeting and extent of virus spread on graphs. Study addressed the problem of the expected spread immunization and the expected epidemic spread minimization; but not the epidemic threshold (for space constraint).

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