http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/54 Information and Media Technology Wed, 17 Jun 2026 03:14:55 GMT 2026-06-17T03:14:55Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31662 Title: NutriMax: an Android Based Personalized Nutrition Management System Authors: Alabi, Isiaq Oludare; Salau, Rasaq Bolakale; Sesugh, Simon Lankwagh Abstract: t Personal nutrition management is very important for the sustenance of good health, there are a lot of health complications that occur in the human body when certain food nutrients are insufficient, this results in nutritional deficiencies which are very life-threatening for health vulnerable individuals such as pregnant women, sick people, children and the aged. This research focused on the development of mobile application software with an algorithm based on machine learning of data mining principle to learn, train and analyze the challenges of malnutrition and how to effectively manage it. The mineral value content of one hundred and twenty (120) assorted commonly available Nigerian food substances was collected and contrasted with standard dietary benchmarks. Factory processed foods were not be considered. Hence, a list of daily requirements of food nutrients by the human body was sourced together with a selected number of nutritional deficiencies to create a mobile application powered by an algorithm that establishes a relationship between nutritional deficiencies and their requirements to suggest daily meals for users. The data obtained was uploaded to a real-time database and integrated with Android Studio to build a working Android application interspersed with Java programming language. The food guide application was named NutriMax. Fri, 01 Jan 2021 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31662 2021-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31661 Title: Smart Attendance System Using Multi-Classifier Face Recognition Authors: ALABI, Isiaq Oludare; ETUK, Stela Abiola; SANI, Yahaya Mohammed; AHMAD, Sulaiman; HASSAN, Abdulazeez T. Abstract: Taking attendance in an Institution of learning or an organization cannot be over emphasized. However, keeping attendance records manually can be tedious and ineffective due to time wastage. Researchers have come up with different automated systems of attendance like the Radio Frequency Identification technology (RFID) and Barcode technology to address the issue of time wastage, ease of use and convenience. However, these systems are susceptible to spoofing. An automated system that has the mechanism to proof a claimed identity and at the same time be convenient to accurately process attendance is crucial. This study is developed through the fusion of two feature extraction algorithms: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), aiming at increasing the recognition accuracy compared to using either PCA or LDA singly. The system design follows the divide and conquer method and was implemented using MATLAB R2015a. The recognition performance rate of the system objectively justifies the fusion of PCA and LDA thereby improving the objects recognition accuracy compared to using either PCA or LDA in face recognition. With the appropriate alarm threshold values the identification rate of 60%, 40% and 60%; false negative identification error rate of 0%, 0% and 0%; and the false alarm rate of 40%, 60%, and 40% were recorded. Mon, 01 Dec 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31661 2025-12-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31660 Title: Systematic literature review and bibliometric analysis of pipeline monitoring and leakage detection techniques Authors: Abubakar, Adamu; Abisoye, Opeyemi Aderiike; Alabi, Isiaq Oludare; Adepoju, Solomon; Oyefolahan, Ishaq Oyebisi Abstract: Liquids such as water, chemicals, oil, and gas are mostly transported via pipelines worldwide. It is considered one of the fastest, cheapest, and most reliable methods of transporting oil and gas. One of the major threats to this form of transportation of liquids is leakage due to vandalism or deterioration causing economic loss, environmental damages, and loss of lives and properties. To mitigate these threats, several techniques have been proposed. This systematic review analysed the various principles and approaches of pipeline leakage detection and monitoring. Furthermore, relevant standard, taxonomy of different methods of monitoring, and leakage detection of oil and gas in pipelines were discussed; their merits, performance and limitations were also discussed. Kitchenham literature search strategy was adopted to search relevant academic databases using various principles of inclusion and exclusion criteria to filter unaligned articles. It was found that integrating two or more techniques enhances the efficiency, effectiveness and accuracy of detecting and monitoring leakages in pipeline. Finally, this review concludes by outlining potential ways for future researchers based on the limitations and findings of the reviewed articles. Wed, 30 Apr 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31660 2025-04-30T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31659 Title: Pipeline Leakage Detection and Monitoring Model using Enhanced Multiple Signal Classification Algorithm and Hybrid Acoustic Emission Techniques Authors: Abisoye, O.A.; Adamu, A.; Adepoju, S.A.; Alabi, I. O.; Oyefolahan, I. O. Abstract: The consequences of pipeline leakages pose great multifaceted hazards, including carcinogenicity and cytotoxicity in humans exposed to leaked toxic substance from pipelines. Pipeline leak also causes environmental contamination of soil resulting to environmental pollution, fire disaster and even loss of life. Therefore, pipeline leakage detection monitoring is a crucial concern in pipeline industry for ensuring the safe and efficient operations. Background noise and detection of single leak are significant limitations of the existing pipeline monitoring and leakage detection techniques. These undesired noises can arise from multiple sources, including environmental, proximity industries, pipe vibration, and electronic interferences. This study therefore optimizes the conventional Multiple Signal Classification (MUSIC) algorithm and Acoustic Emission (AE) technique with the aim to develop a novel technique to address the effect of the background noise. The proposed method combines the advantages of the MUSIC algorithm and AE techniques with real-time monitoring to promptly and accurately detect leakages in pipeline systems. The model achieved Accuracy of 95.5%, Sensitivity of 75%, Mean Detection Time of 1.02 seconds and Response Time of 1.06 seconds. These quantitative results demonstrate the effectiveness of our proposed Enhanced MUSIC algorithm and Hybrid AE technique (Enhanced-MUSICHAE) to detect and monitor pipeline leakage. This has the potential to improve pipeline safety, reduce economic losses, and minimize environmental damages. Mon, 01 Sep 2025 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31659 2025-09-01T00:00:00Z