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All Journal ComEngApp : Computer Engineering and Applications Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Olaniyi, Olayemi Mikail
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Low-cost and Efficient Fault Detection and Protection System for Distribution Transformer Umar, Buhari Ugbede; Ambafi, James Garba; Olaniyi, Olayemi Mikail; Agajo, James; Isah, Omeiza Rabiu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.722 KB) | DOI: 10.22219/kinetik.v5i1.987

Abstract

Distribution transformers are a vital component of electrical power transmission and distribution system. Frequent Monitoring transformers faults before it occurs can help prevent transformer faults which are expensive to repair and result in a loss of energy and services. The present method of the routine manual check of transformer parameters by the electricity board has proven to be less effective. This research aims to develop a low-cost protection system for the distribution transformer making it safer with improved reliability of service to the users. Therefore, this research work investigated transformer fault types and developed a microcontroller-based system for transformer fault detection and protection system using GSM (the Global System of Mobile Communication) technology for fault reporting. The developed prototype system was tested using voltage, current and temperature, which gave a threshold voltage higher than 220 volts to be overvoltage, a load higher than 200 watts to be overload and temperature greater than 39 degrees Celsius to be over temperature was measured. From the results, there was timely detection of transformer faults of the system, the transformer protection circuits were fully functional, and fault reporting was achieved using the GSM device. Overall, 99% accuracy was achieved. The system can thus be recommended for use by the Electricity Distribution Companies to protect distribution transformers for optimal performance, as the developed system makes the transformers more robust, and intelligent. Hence, a real-time distribution transformer fault monitoring and prevention system is achieved and the cost of transformer maintenance is reduced to an extent.
Deep Neural Networks for Intelligent Voice Authentication Systems in Large-Scale Electronic Voting Olaniyi, Olayemi Mikail; Nuhu, Bello Kotangora; Okunade, Oluwasogo Adekunle; Ezeanya, Uchenna Christiana; Eke, Chimdiebube Emmanuel
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1164

Abstract

The authentication of eligible voters is an area of concern that needs further exploration of the prospects of electronic voting systems. The integration of voice authentication in electronic voting systems for varying numbers of disabled and prospective voters should be secure, scalable, and suitable in both federal and state elections. Machine learning (ML) is an evolving field of computing that presents prospects in electronic voting. Applying ML algorithms to electronic voting provides optimal solutions to a wide range of biometric authentication challenges. This paper presents the design of an effective voice classification algorithm from a narrower perspective that can be used in developing prototype electronic voting systems in large-scale voting scenarios, particularly for disabled voters. Applying the knowledge of deep neural networks, a three hidden layer network using a feed-forward architecture is designed for classifying voice data acquired from prospective voters. The proposed design is tested on two different datasets and is adapted to handle small and vast amounts of voters’ voice information. Results indicated average training and average validation accuracies of 92% and 97% respectively for both deep learning models for inclusivity and accountability of disabled voters in secure electronic voting systems.
Voice Recognition Systems for The Disabled Electorate: Critical Review on Architectures and Authentication Strategies Olaniyi, Olayemi Mikail; Bala, Jibril Abdullah; Ganiyu, Shefiu; Abdulsalam, Yunusa Simpa; Eke, Chimdiebube Emmanuel
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 2 (2023)
Publisher : Universitas Sriwijaya

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Abstract

An inevitable factor that makes the concept of electronic voting irresistible is the fact that it offers the possibility of exceeding the manual voting process in terms of convenience, widespread participation, and consideration for People Living with Disabilities. The underlying voting technology and ballot design can determine the credibility of election results, influence how voters felt about their ability to exercise their right to vote, and their willingness to accept the legitimacy of electoral results. However, the adoption of e-voting systems has unveiled a new set of problems such as security threats, trust, and reliability of voting systems and the electoral process itself. This paper presents a critical literature review on concepts, architectures, and existing authentication strategies in voice recognition systems for the e-voting system for the disabled electorate. Consequently, in this paper, an intelligent yet secure scheme for electronic voting systems specifically for people living with disabilities is presented.
Deep Neural Networks for Intelligent Voice Authentication Systems in Large-Scale Electronic Voting Olaniyi, Olayemi Mikail; Bello Kontagora Nuhu; Okunade, Oluwasogo Adekunle; Ezeanya, Uchenna Christiana; Eke, Chimdiebube Emmanuel
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The authentication of eligible voters is an area of concern that needs further exploration of the prospects of electronic voting systems. The integration of voice authentication in electronic voting systems for varying numbers of disabled and prospective voters should be secure, scalable and suitable in both federal and state elections. Machine learning (ML) is an evolving field of computing that presents prospects in electronic voting. Applying ML algorithms to electronic voting provides optimal solutions to a wide range of biometric authentication challenges. This paper presents the design of an effective voice classification algorithm from a narrower perspective that can be used in developing prototype electronic voting systems in large-scale voting scenarios, particularly for disabled voters. Applying the knowledge of deep neural networks, a three hidden layer network using a feed-forward architecture is designed for classifying voice data acquired from prospective voters. The proposed design is tested on two different datasets and is adapted to handle small and vast amounts of voters’ voice information. Results indicated average training and average validation accuracies of 92% and 97% respectively for both deep learning models for inclusivity and accountability of disabled voters in secure electronic voting systems.
Co-Authors Abdulsalam, Yunusa Simpa Agajo, James Ambafi, James Garba Bala, Jibril Abdullah Bello Kontagora Nuhu Eke, Chimdiebube Emmanuel Ezeanya, Uchenna Christiana Ganiyu, Shefiu Isah, Omeiza Rabiu Nuhu, Bello Kotangora Okunade, Oluwasogo Adekunle Umar, Buhari Ugbede