International Journal of Electrical and Computer Engineering Research 2024-03-19T11:03:53+03:00 Yunus Uzun Open Journal Systems <p>International Journal of Electrical and Computer Engineering Research (IJECER) is an academic journal that publishes research articles and review articles emerging from theoretical and experimental studies in all fields of electrical and computer engineering. IJECER is an open access, free publication and peer-reviewed journal. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. In addition, there is no APC fee. In order for the articles submitted to the journal to be evaluated, they should not have been published elsewhere before and the similarity rate should be less than 20%. <br />The main aim of IJECER is to publish quality original scientific papers and bring together the latest research and development in various fields of science and technology related electrical and computer enginerring. IJECER is published quarterly a year, in March, June, September and December. Permanent links to published papers are maintained by using the Digital Object Identifier (DOI) system by CrossRef.</p> <p>The topics related to this journal include but are not limited to:</p> <table border="0" width="100%"> <tbody> <tr> <td>Electrical engineering<br />Computer engineering<br />Electronics engineering<br />Biomedical engineering<br />Mechatronics engineering<br />Electrical energy and power<br />Internet of things emerging technologies<br />Internet technologies, and smart devices<br />Computer science and information technology<br />Artificial intelligence and soft computing<br />Computational science and engineering<br />Big data and cloud computing<br />Signal, image and speech processing<br />Networking and the internet</td> <td>Pattern recognition<br />Renewable energy<br />Algorithms and applications<br />Green technologies in information<br />Circuits and electronics<br />Power electronics and drives<br />Wireless sensor network<br />Computer software engineering<br />Communications and wireless networks<br />Sensors and actuators<br />Computer vision and robotics<br />Embedded systems<br />Radar and sonar systems<br />Robotics</td> </tr> </tbody> </table> Design of a Voice Recognition System Using Artificial Neural Network 2024-03-01T09:34:47+03:00 Daniel Oluwatobi Mayowa Ibukunoluwa Adetutu Olajide <p>Voice recognition systems have gained significant prevalence in our everyday lives, encompassing a wide range of applications, from virtual assistants on smartphones to voice-controlled home automation systems. This research paper presents a comprehensive design and implementation of a voice recognition security system employing artificial neural networks. The system's training involved a dataset consisting of 900 audio samples collected from 10 distinct speakers, enabling the resulting model to accurately classify the speaker of a given audio sample. For the implementation of the voice recognition system, Python serves as the primary programming language. The system leverages the Keras library, which offers a high-level interface for constructing and training neural networks, with efficient computation facilitated by the TensorFlow back-end. Additionally, the Flask framework, a Python-based web framework, was utilized to create a user interface in the form of a web application for the voice recognition system. To effectively train the artificial neural network, the audio data undergoes preprocessing, involving the extraction of relevant features from the audio samples. Subsequently, during the preprocessing phase, the audio data is labelled, and the neural network is trained on this labelled dataset to learn the classification of different speakers. The trained model was rigorously tested on a set of previously unseen audio samples, yielding an impressive classification accuracy exceeding 96%. The finalized model will be integrated into the web application, enabling users to upload audio files and receive accurate predictions regarding the speaker's identity. This paper demonstrates the efficacy of artificial neural networks in the context of voice recognition systems, while also providing a practical framework for constructing such systems using readily available tools and libraries.</p> 2024-03-15T00:00:00+03:00 Copyright (c) 2024 International Journal of Electrical and Computer Engineering Research Design and Implementation of a Secured, Real-Time Internet-based Voting System 2024-03-13T14:22:14+03:00 Raban Nghidinwa Joshua A. Abolarinwa <p>The Internet of Voting system presented in this paper represents a modern solution to the challenges faced by the traditional electoral processes. In an era characterized by technological advancements, this system harnesses digital innovation to enhance the efficiency, security, and accessibility of elections. Evolving from a rich history of voting systems, this digital platform integrates several influential works and leverages cutting-edge technology to create a user-friendly interface with robust security measures. The system addresses critical issues that have plagued traditional paper and electronic ballots, such as voter confusion and fraud, by implementing stringent encryption techniques, password renewal mechanisms, and real-time prevention of duplicated votes. It enables the elimination of multiple registrations, streamlining the voter registration process. Online voting systems have gained prominence as a means of conducting elections efficiently, reducing logistical challenges, and increasing voter accessibility. The system offers three distinct categories for voting: Regional Elections, Local Elections, and Presidential Elections, each with its own set of independent candidates.</p> 2024-03-15T00:00:00+03:00 Copyright (c) 2024 International Journal of Electrical and Computer Engineering Research Performance Comparison of LMS and RLS Algorithms for Ambient Noise Attenuation 2024-03-13T14:22:36+03:00 Amira Chiheb Hassina Khelladi <p>The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one, a few simulations are achieved. The numerical experiments are performed by using several real recordings of different environment noises tested on the two proposed adaptive algorithms. A comparison is emphasized regarding noise suppression ability and convergence speed, by implementing both adaptive algorithms on the same noise sources. From this numerical study, the RLS algorithm reveals a faster convergence speed and better control performances than the LMS algorithm.</p> 2024-03-15T00:00:00+03:00 Copyright (c) 2024 International Journal of Electrical and Computer Engineering Research