Computer Science

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Browsing Computer Science by Author "Prof. M. O. Abass"

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    A Decision Support System for Counselling: An Analytic Hierarchy Process (AHP) Aproach
    (Bells University of Technology, 2015-09-30) OLADIBOYE, Olasunkanmi Esther; Prof. M. O. Abass
    Decision support systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem put differently. DSS are built to facilitate the decision-making process by people in institution when the need arises. Studies have shown that there is high rate of abuse of drug among students in tertiary institution in Nigeria. This research is therefore an attempt to develop a decision support system (DSS) that can be useful for counselling students who have drug problem using the Analytic Hierarchical Process (AHP) model. Using the DSS developed in this study, were able to compute Eigen vectors of the matrix of pair-wise comparison in generalized ascending order of magnitude. The highest Eigen vector was selected as the most effective method of counselling students with drug problems. The system was implemented using PHP and MySQL databased. This study shows that the counselling methods such as interview, behavioral modification and counselling which refers the student to an expert, all have the same Eigen vector. That is to say that they are equal importance with the criteria used in counselling the student. The study also revealed that 'Rewards' as an approach to counselling with respect to behavioral modification has the highest priority value which is 0.6687. 'Denial' and 'Punishment' have the priority values of 0.2431 and 0.0882 respectively. In conclusion, from our findings the best choice of counselling student with respect to behavioral modification is the 'Reward Approach'.
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    A Mobile Application for Potholes Detection and Analysis System
    (Bells University of Technology, 2020-03-08) KUDETI, Waziri Omolade; Prof. M. O. Abass
    The presence of potholes on roads has over the years been a great source of concern to everyone.it has been identified as one of the major causes of road accidents as well as the wear and tear of vehicle tyres. The mobile sensing method for pothole detection is considered as the most suitable method to detect pothole for mobile devices. This is because the other methods require huge computation power for image recognition. Three different algorithms were implemented: the Z-Diff algorithm, STDEV(z)algorithm which are existing algorithms and a new improved algorithm. The data used for the experiments were obtained by driving through a selected test track which is 18.9km (11.7miles) long .it includes major single lane streets as well as minor multi-lane from Sango Ota, Ogun state to Agege, Lagos state, and is characterized by a range of degrees in road surface smoothness. This research shows that the proposed improved algorithm outperforms both the Z-DIFF algorithm and STDEV(Z) metrics. The performance metrics gives different results because each metric covers a part and considers some parameters while leaving some others. Therefore, a combination of metrics is recommended to be used to measure the performance of each algorithm against others.
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    Comparative Analysis of Certain Data Analytic Languages Using Code Based Metrics
    (Bells University of Technology, 2019-10-17) ADIGUN, Maria Funmilayo; Prof. M. O. Abass
    Software complexity metrics is developed and used by the various software organizations for evaluating and assuring software code quality, operation, and maintenance. Software metrics measure various types of software complexity like size metrics, control flow metrics and data flow metrics. These software complexities must be continuously calculated, followed, and controlled because high complexity may result in more errors and difficulties in maintenance, understandability, modification and testing effort. The revival of data science due to the presence of large amount of data has resulted in the need for a good programming language on which many data science applications can be developed. Hence the need to apply programming complexity metrics to existing data analytic languages so as to guide programmers in choosing tools for building data science applications. For application, binary search and quicksort algorithms are considered. The programs are written in three different data analytic languages: Python, R and Scala. Software complexity for each is found using LOC, McCabe and Halstead models. The results are compared and Scala is realized to be the most complex for all the metrics while Python and R programs are averagely at par.