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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Adetiba, Emmanuel
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Development of a blockchain-based electronic voting system utilizing national identification number Bismark, Olabode Idowu-; Oshin, Oluwadamilola; Adetiba, Emmanuel
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i3.pp810-820

Abstract

Traditional voting methods in Nigeria face numerous challenges, including logistic issues, security concerns, and allegations of fraud, which undermine public trust. This work develops a blockchain-based electronic voting system (EVS) that leverages the national identification number (NIN) for biometric verification to address these issues. The research identifies the limitations of current blockchain voting solutions, such as implementation complexity, scalability issues, user adoption resistance, and cybersecurity threats and provide a more secure and user-friendly alternative. The system integrates blockchain technology with biometric verification to create an immutable, transparent, and secure voting process. The methodology involves designing a system architecture that includes a blockchain network, an NIN verification module, and a user interface (UI). Users register using their NIN, authenticate themselves, and cast their votes, which are then encrypted and recorded on the blockchain. The system's functionality was tested using tools like Ganache for local blockchain development, MetaMask for Ethereum wallet integration, and Solidity for writing smart contracts. Results from the implementation indicate significant improvements in security, transparency, and user accessibility compared to traditional voting systems. The user authentication test achieved a 100% valid login success rate and 0% invalid login attempts. Meanwhile, the voting test accuracy was 100%.
Evolutionary trends in automatic speech recognition with artificial intelligence: a systematic literature review Oluwatobi Sobola, Gabriel; Adetiba, Emmanuel; Idowu-Bismark, Olabode; Abayomi, Abdultaofeek; Jules Kala, Raymond; Thakur, Surendra Colin; Moyo, Sibusiso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp20-43

Abstract

Human beings depend greatly on communication and continually seek ways to overcome language barriers. Automatic speech recognition (ASR) has emerged as a vital tool for enhancing human interaction. Early ASR research relied on probabilistic models, particularly the hidden Markov model (HMM) and Gaussian mixture model (GMM), with mel-frequency cepstral coefficients (MFCCs) for feature extraction, leading to the creation of Audrey at Bell Laboratories. Subsequently, artificial intelligence (AI) approaches, especially deep learning, have transformed ASR and produced systems such as Jasper, Whisper, Google Assistant, Microsoft Cortana, Apple Siri, and Amazon Alexa. This paper presents a systematic literature review that examines ASR’s evolution, the AI architectures employed, their features, strengths and weaknesses, and the performance gains achieved since AI was integrated into probabilistic modelling. A snowballing approach was used to identify relevant studies from Google Scholar and Scopus to address five research questions, iterating through backward and forward searches until no new information was found. Findings reveal that ASR dates back to the 1920s with the Radio Rex toy and has since advanced through architectures including deep learning, recurrent neural networks (RNN), support vector machines (SVM), and transformers, all contributing to improved performance measured by reduced word error rates (WER).
Ethylene gas-based fruit expiry predictor: a sustainable solution to fruit wastage Idowu-Bismark, Olabode; Eberchukwu, Anyiam Chinemere; Oshin, Oluwadamilola; Emmanuel, Ibiang; Mary, Tonwe; Adetiba, Emmanuel
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9809

Abstract

Globally, environmental and economic issues are very apparent regarding food wastage. Fruits play a significant role since they can be damaged easily. Ethylene gas is one of the significant gases produced by fruits when they begin to ripen; it tests whether the fruit is ripe or else spoilable. This work presents a sustainable solution for fruit wastage: an ethylene gas-based fruit expiry predictor (FEP). The system is designed and developed around advanced sensing and artificial intelligence (AI) models for real-time monitoring of ethylene levels and temperature in forecasting the spoilage of fruits. The system consists of non-invasive sensors for detecting ethylene and temperature, a data processing microcontroller, and an AI model trained on a large dataset to make accurate predictions regarding the expiry of fruit. The AI model processes the information collected by the sensors and then displays the grade (level of ripeness of the fruit) on a liquid crystal display (LCD) screen. This solution improves fruit management with reduced wastage in line with international sustainability targets. These would enable real-time and highly accurate predictions of fruit spoilage, allowing end-users informed choices that will eventually lead to reducing the carbon footprint from food waste and increasing food security.
Co-Authors Abayomi, Abdultaofeek Bismark, Olabode Idowu- Eberchukwu, Anyiam Chinemere Emmanuel, Ibiang Idowu-Bismark, Olabode Jules Kala, Raymond Mary, Tonwe Moyo, Sibusiso Oluwatobi Sobola, Gabriel Oshin, Oluwadamilola Thakur, Surendra Colin