Author : Ziweritin s., baridam b. b. and okengwu u. a.
Keyword : Neural network, anomaly detection, academic results, object-oriented design, simulation
Subject : Computer science
Article Type : Original article (research)
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Abstract : The performance of students in tertiary institutions within and outside Nigeria is based entirely on end of academic-session examination. The processes involved in carrying out semester examinations are complex and crucial in tertiary institutions and confidentiality must be ensured. Anomaly detection in results is an important and increasing problem that has been well-studied within diverse research areas and application domains. The admission of students into different departments of tertiary institutions in Nigeria is increasing at a very high rate and has now reached a position where it is becoming difficult for the available manpower and the existing system to cope with the magnitude of irregularities between the CA and exam scores to detect anomalies, within the given time span leading to delay in approving students semester results for decision making. In this paper, an efficient neural network model is developed to systemically detect anomalies in students' results as an effective measure which can enhance the efficiency and accuracy of the system. The study and the preliminary designs were carried out using the Object Oriented Analysis and Design Methodology (OOADM), simulated using MATLAB in the design, training and testing aimed at detecting anomalies from the dataset. The system was successfully tested according to the design specification with 96% accuracy level in comparison with existing methods.
Article by : stanley ziweritin
Article add date : 2022-03-25
How to cite : Ziweritin s., baridam b. b. and okengwu u. a.. (2022-March-25). Neural network model for detection of result anomalies in higher education. retrieved from https://www.openacessjournal.com/abstract/1050