Computer Science

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Browsing Computer Science by Subject "Information"

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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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    Neuro-Fuzzy Approach to River Sediment Yield Prediction
    (Bells University of Technology, 2016-07-28) BALOGUN, Oluwatobi; Prof. E. R. Adagunodo
    The advancement of the information and communication technology has created an unlimited space for exploration in different areas in order to make meaningful observations and predict at an optimum accuracy. All around the world, data are being generated every moment in large quantities, but these data are of no use until they can be converted to useful in formation. The limitation of existing models used for predicting River sediment yield in order to forestall against natural and economic disasters forms the basis of the motivation of this research. By taking advantage of the opportunities presented by the advancement of technology, this research focuses on predicting the sediment yield from Oyan gauging station of Ogun-Osun River Basin and comparing the output with result with other existing result. Some conventional method available for sediment load/yield estimation are largely empirical. In this research, an ANFIS (Adaptive Neuro-Fuzzy inference system) is used to predict the sediment yield of Oyan gauging station of Ogun-Osun River Basin. The ANFIS is a feed forward five layered architecture and uses the back propagation algorithm to train the network. For this research, four inputs data are used, the water stage and water stage and water discharge, water temperature, and rainfall.