Evans Baidoo

Work place: Department of Computer Science, KNUST, Ghana

E-mail: evandodidi@yahoo.com


Research Interests: Software Engineering, Software Organization and Properties, Computer Architecture and Organization, Data Structures and Algorithms


Evans Baidoo received his M.Phil degree in Information Technology from the Department of Computer Science, Kwame Nkrumah University of Science and Technology , Ghana in 2015. He has an extensive career of over 7 years in ICT educational training and programs in public sector as an Instructor and as a Project researcher in the private sector. His research interest include: verification, Software technologies in education, Algorithm and Optimization

Author Articles
Implementation of Gray Level Image Transformation Techniques

By Evans Baidoo Alex kwesi Kontoh

DOI: https://doi.org/10.5815/ijmecs.2018.05.06, Pub. Date: 8 May 2018

Gray level transformation is a significant part of image enhancement techniques which deal with images composed of pixels. The outcomes of this process can be either images or a set of representative characteristics. It effects is simple but complicated in its implementation. Recently much work is completed in the field of images enhancement with varying observable techniques. This paper describes how to enhance an image using different gray level techniques and a demonstration of its implementation. PPI Analyzer, a kind of software created to implement the various techniques is based on explosive phenomenon of MATLAB. The implemented program with interactive interface to allow for relaxed modification, presented encouraging results.

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Optimizing Memory using Knapsack Algorithm

By Dominic Asamoah Evans Baidoo Stephen Opoku Oppong

DOI: https://doi.org/10.5815/ijmecs.2017.05.05, Pub. Date: 8 May 2017

Knapsack problem model is a general resource distribution model in which a solitary resource is allocated to various choices with the aim of amplifying the aggregate return. Knapsack problem has been broadly concentrated on in software engineering for a considerable length of time. There exist a few variations of the problem. The study was about how to select contending data/processes to be stacked to memory to enhance maximization of memory utilization and efficiency. The occurrence is demonstrated as 0 – 1 single knapsack problem. In this paper a Dynamic Programming (DP) algorithm is proposed for the 0/1 one dimensional knapsack problem. Problem-specific knowledge is integrated in the algorithm description and assessment of parameters, with a specific end goal to investigate the execution of finite-time implementation of Dynamic Programming.

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