Browsing by Author "Isiaka, Mope Isiaka"

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    An Enhanced Web-Based Examination System using Automated Proctoring and Background Activity Detection
    (Journal of Institutional Research, Big Data Analytics and Innovation (JIRBDAI.), 2025-08-20) Olanrewaju, Olaitan Toyyib; Isiaka, Mope Isiaka; Babatunde, Seyi Ronke; Jimoh, Muhammed Kamaldeen; Babatunde, Akinbowale Nathaniel
    The rise of online education demands sturdy systems to uphold academic integrity during remote assessments. This paper presents the design and implementation of a web-based examination platform integrated with automated, multi-modal proctoring tools to detect potential cheating. The system enables educators to create and manage diverse assessments while students’ complete exams in a monitored virtual environment. Key security measures include user authentication with facial recognition using the DeepFace library and continuous webcam analysis to detect mobile phone usage, unauthorized individuals, and unusual head or eye movements. Additional features include browser focus tracking to monitor navigation away from the exam window, and background audio analysis using Voice Activity Detection (VAD) and Root Mean Square (RMS) energy to flag suspicious sounds or conversations. Detected anomalies are logged in real-time for review. The prototype effectively combines visual, auditory, and behavioral monitoring into a cohesive framework, offering a comprehensive solution to reduce academic misconduct in remote settings. Functional testing showed 72% overall system accuracy and over 90%+ accuracy in specific detection modules. Future work should intend to enhance the system's AI capabilities, improve performance on models, and ensure fairness for all users, contributing to credible and secure digital learning environments across disciplines.
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    Use of White Shark Optimization for Improving the Performance of Convolution Neural Network in Classification of Infected Citrus
    (University of Ibadan Journal of Science and Logics in ICT Research, 2023-06-09) Babatunde, Ronke Seyi; Isiaka, Mope Isiaka; Ajao, Jumoke Falilat; Abdulsalam, Olaniyi Sulaiman; Balogun, Bukola Fatimah
    Citrus plant diseases are major causes of reduction in the production of citrus fruits and their usage. Early detection of the onset of the diseases is very important to curb and reduce its spread. A number of researches have been done on the detection and classification of the diseases, most of which have identified poor labelling of symptoms which result into improper classification. Some researchers have also experimented on the effectiveness of convolution neural network and other deep learning techniques, most of which results into a faster convergence but suffers from low accuracy, computational overhead and overfitting as fundamental issues. To reduce the effects of overfitting, this research developed a White Shark Optimization-Convolution Neural Network (WSO-CNN) technique to address the aforementioned problem by introducing a regularization strategy via feature selection which selects more useful and distinguishing features for classification. As a result, the developed technique was able to detect and classify various types of citrus fruit diseases and label them accordingly with low false positive rate, high sensitivity, specificity, increased accuracy and reduced recognition time, based on all the experiments performed with the dataset used in the research. Hence WSOCNN performed better than CNN in classifying citrus plant disease having a reduced FPR of 3.57%, 8.34%, 3.89% and 9.00% for black spot, greasy spot, canker and healthy/non healthy dataset respectively.