Article Per Year (5 Year)
p-Index From 2021 - 2026
0.961
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Ajagbe, Sunday Adeola
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Comprehensive review of load balancing in cloud computing system Oyediran, Mayowa O.; Ojo, Olufemi S.; Ajagbe, Sunday Adeola; Aiyeniko, Olukayode; Chima Obuzor, Princewill; Adigun, Matthew Olusegun
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3244-3255

Abstract

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.
Enhancing car plate recognition with convolutional neural network and regular expressions correction Awoseyi, Ayomikun Abayomi; Timothy, Timileyin Favour; Ajagbe, Sunday Adeola; Onuiri, Ernest Enyinnaya; Abdulahi, Qudus Opeyemi; Adekunle, Temitope Samson; Adigun, Matthew Olusegun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2073-2080

Abstract

This research paper presents the development and evaluation of an Automatic Number Plate Recognition (ANPR) system using Convolutional Neural Networks (CNN) with Regex correction. The aim is to enhance the accuracy and effectiveness of car verification and security processes at First Technical University, Ibadan. The ANPR system was implemented both without Regex correction and with Regex correction. The evaluation results demonstrate significant improvements in the system's performance when CNN with Regex correction is employed. The CNN-based ANPR system achieves a precision of 1.00, recall of 0.90, and F1-score of 0.95 in accurately identifying number plates. These scores indicate increased accuracy and reduce false positives compared to the system without Regex correction. The integration of CNN and Regex correction effectively handles variations and errors in the number plate data, leading to a reliable and efficient car verification process. Future work can focus on further refining the CNN model and optimizing the Regex correction algorithms to enhance the system's accuracy and robustness. The developed ANPR system, utilizing CNN with Regex correction, shows great potential for enhancing car verification and security in various domains, including law enforcement, parking management, and traffic monitoring
EASESUM: an online abstractive and extractive text summarizer using deep learning technique Adeniyi, Jide Kehinde; Ajagbe, Sunday Adeola; Adeniyi, Abidemi Emmanuel; Aworinde, Halleluyah Oluwatobi; Falola, Peace Busola; Adigun, Matthew Olusegun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1888-1899

Abstract

Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.
Design and implementation of an alcohol detection driver system Alabi, Oluwaseyi Omotayo; Adeaga, Oyetunde Adeoye; Ajagbe, Sunday Adeola; Adekunle, Esther Oluwayemisi; Adigun, Matthew Olusegun
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp278-285

Abstract

A technology called an alcohol detection driver system is used to stop drunk driving by identifying alcohol in a motorist's breath or blood. This technology correctly measures the amount of alcohol a driver has in their system using sensors and algorithms, and it stops the car from starting if the amount is more than the legal limit. The number of fatal accidents and traffic fatalities caused by drinking could be greatly decreased thanks to this technology. The main focus of this project is to carry out the experiment in lowering the number of alcohol-related incidents on the road. Alcohol detection devices come in a variety of forms right now, including ignition interlocks, passive alcohol sensors, and in-car breathalyzers. Although these systems have reduced the number of drunk driving accidents, there remain questions about their efficiency, dependability, and cost. According to the sensor's specs, the output voltage of the MQ-3 sensor reduces by 69% during the sensor's recovery period of 30 seconds at 69% of baseline resistance. To assess the long-term viability and efficiency of these systems in lowering alcohol-related accidents and enhancing traffic safety, more research is required.
Comparative analysis of selected optimization algorithms for mobile agents’ migration pattern Oyediran, Mayowa O.; Ajagbe, Sunday Adeola; Ojo, Olufemi S.; Elegbede, Adedayo Wasiat; Adio, Michael Olumuyiwa; Adeniyi, Abidemi Emmanuel; Adebayo, Isaiah O.; Obuzor, Princewill Chima; Adigun, Matthew Olusegun
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp685-693

Abstract

Mobile agents are agents that can migrate from host-to-host to work in a heterogeneous network environment. A mobile agent can migrate from host-to-host in its plan with the statistics generated on each host through a route known as migration pattern. Migration pattern therefore is the route the agents use to travel within the plan from the first host to the last host. However, there is a need for a comparison between the commonly used optimization algorithms in developing migration patterns for mobile agents with respect to some evaluation metrics. In this paper, the three techniques firefly algorithm (FFA), honeybee optimization (HBO) and particle swarm optimization (PSO) were used for developing migration patterns for mobile agents and their comparison was done based on migration time, time complexity and network load as metrics. PSO is discovered to perform better in terms of network load with an average of 242.3905 bits per second (bps), time complexity with an average of 41.2688 number of nodes (n), and migration/transmission time with an average of 4.203462 seconds (s).
Predictive analytics on crop yield using supervised learning techniques Okesola, Julius Olatunji; Ifeoluwa, Olaniyi; Ajagbe, Sunday Adeola; Okesola, Olubunmi; Abiodun, Adeyinka O.; Osang, Francis Bukie; Solanke, Olakunle O.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1664-1673

Abstract

Agriculture is one of Nigeria’s most important economic activities but with climate change is a threat to crop production and a significant impact on the national economy as unforeseen scenarios can cause a drop in crop yield. Machine learning algorithms are now being considered as decision support tools for crop yields prediction and weather forecasting. Maize is the crop selected in this study, and a stochastic gradient model of five popular regression algorithms was evaluated. The prediction system is written in Python programming language and linked to a web-based interface for ease of use and effectiveness. Using performance metrics, the result shows that stochastic gradient descent (SGD) performed best with lower error rates and better R2_score value of 0.98505036. This crop yield prediction system (CYPS) is able to predict the yield of the crop which will help farmers and analysts in decision-making. It will also help industries that make use of the agricultural product in strategizing the logistics of their business.
Co-Authors Abdulahi, Qudus Opeyemi Abiodun, Adeyinka O. Adeaga, Oyetunde Adeoye Adebayo, Isaiah O. Adekunle, Esther Oluwayemisi Adekunle, Temitope Samson Adeniyi, Abidemi Emmanuel Adeniyi, Jide Kehinde Adigun, Matthew Olusegun Adio, Michael Olumuyiwa Aiyeniko, Olukayode Alabi, Oluwaseyi Omotayo Aworinde, Halleluyah Oluwatobi Awoseyi, Ayomikun Abayomi Chima Obuzor, Princewill Elegbede, Adedayo Wasiat Falola, Peace Busola Ifeoluwa, Olaniyi Obuzor, Princewill Chima Ojo, Olufemi S. Okesola, Julius Olatunji Okesola, Olubunmi Onuiri, Ernest Enyinnaya Osang, Francis Bukie Oyediran, Mayowa O. Solanke, Olakunle O. Timothy, Timileyin Favour