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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES)
Adetiba, Emmanuel
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Computer Science & IT Economics, Econometrics & Finance 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).
Co-Authors Abayomi, Abdultaofeek Bismark, Olabode Idowu- Idowu-Bismark, Olabode Jules Kala, Raymond Moyo, Sibusiso Oluwatobi Sobola, Gabriel Oshin, Oluwadamilola Thakur, Surendra Colin