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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Okokpujie, Kennedy
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments Adewale, Adeyinka Ajao; Obiazi, Oghorchukwuyem; Okokpujie, Kennedy; Koto, Omiloli
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4602-4615

Abstract

Load balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories.
Development of a web-based application for real-time eye disease classification system using artificial intelligence Okokpujie, Kennedy; Tolulope, Adekoya; Orimogunje, Abidemi; Mommoh, Joshua Sokowonci; Ijeh, Adaora Princess; Ogundele, Mary Oluwafeyisayo
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp558-574

Abstract

The incorporation of artificial intelligence (AI) into the field of medicine has created new strategies in enhancing the detection of disease, with a focus on the identification of eye diseases such as glaucoma, diabetic retinopathy, and macular degeneration associated with age, which can lead to blindness if not detected and treated early enough. Driven by the need to combat blindness, which affects approximately 39 million people globally, according to the World Health Organization (WHO). This research offers a web-based, real time approach to classifying eye diseases from fundus images due to user friendliness. Three pre-trained convolutional neural network (CNN) models are adopted, namely ResNet-50, Inception-v3, and MobileNetV3. The models were trained on a dataset of 8000 fundus images subdivided into four classes: cataract, glaucoma, diabetic retinopathy, and normal eyes. The performance of the models was evaluated in 3-way (normal eye and two diseases) and 4-way (normal eye and three diseases). ResNet-50 had higher performances, with 98% and 97% accuracy in the respective classifications, compared to InceptionV3 and MobileNetV3. Consequently, ResNet-50 was used in an online application that made real-time diagnoses. This research findings reveal the potential of CNNs in the healthcare industry, particularly in reducing over-reliance on specialists and increasing access to quality diagnostic technologies. Especially in critical areas such as this with limited healthcare resources, where the technology can create significant gaps in disease detection and control.
Classification model for infectious lung diseases using convolutional neural networks on web and mobile applications Okokpujie, Kennedy; Agamah, Alvin K.; Orimogunje, Abidemi; Adaora, Ijeh Princess; Omolara, Olusanya Olamide; Daramola, Samuel Adebayo; Awomoyi, Morayo Emitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp410-424

Abstract

Accurate lung disease diagnosis in infected patients is critical for effective treatment. Tuberculosis, COVID-19, pneumonia, and lung opacity are infectious lung diseases with visually similar chest X-ray presentations. Human expertise can be susceptible to errors due to fatigue or emotional factors. This research proposes a real-time deep learning-based classification system for lung diseases. Three models of convolutional neural networks (CNNs) were deployed to classify lung illnesses from chest X-ray images: MobileNetV3, ResNet-50, and InceptionV3. To evaluate the effect of high interclass similarity, the models were evaluated in 3-class (Tuberculosis, COVID-19, normal), 4-class (lung opacity, tuberculosis, COVID-19, normal), and 5-class (tuberculosis, lung opacity, pneumonia, COVID-19, normal) modes. The best classification accuracy was attained by retraining MobileNetV3, which obtained 94% and 93.5% for 5-class and 4-class, respectively. InceptionV3 had the lowest accuracy (90%, 89%, 93% for 5-, 4-, and 3-class), while ResNet-50 performed best for the 3-class setting. These findings suggest MobileNetV3's potential for accurate lung disease diagnosis from chest X-rays despite the interclass similarity, supporting the adoption of computer-aided detection systems for lung disease classification.
Development of classification model for thoracic diseases with chest X-ray images using deep convolutional neural network Okokpujie, Kennedy; Anointing, Tamunowunari-Tasker; Ijeh, Adaora Princess; Okokpujie, Imhade Princess; Ogundele, Mary Oluwafeyisayo; Oguntuyo, Oluwadamilola
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9300

Abstract

Thoracic disease is a medical condition in the chest wall region. Accurate thoracic disease diagnosis in patients is critical for effective treatment. Atelectasis, mass, pneumonia, and pneumothorax are thoracic diseases that can lead to life-threatening conditions if not detected and treated early enough. When diagnosing these diseases, human expertise can also be susceptible to errors due to fatigue or emotional factors. This research proposes developing a real-time deep learning-based classification model for thoracic diseases. Three deep convolutional neural network (CNN) models - MobileNetV3Large, ResNet-50, and EfficientNetB7 - were evaluated for classifying thoracic diseases from chest X-ray images. The models were tested in 5-class (atelectasis, mass, pneumothorax, pneumonia, and normal), 4-class (atelectasis, pneumothorax, pneumonia, and normal), and 3-class (atelectasis, pneumonia, and normal) modes to assess the impact of high interclass similarity. Retrained MobileNetV3Large achieved the highest classification accuracy: 75.72% next to ResNet-50 (75.2%) and last EfficientNetB7 (73.03%). For the 4-class, EfficientNetB7 (88.08%) led with MobileNetV3Large in the last (87.08%), but MobileNetV3Large led the 3-way with 97.88% with EfficientNetB7 again in the last (96.55%). These results indicate that MobileNetV3 can effectively distinguish and diagnose thoracic diseases from chest X-rays, even with interclass similarity and supports the use of computer-aided detection systems in thoracic disease classification.
A single-user electronic ticketing system using ERC-721 protocol for smart contracts Okokpujie, Kennedy; Owivri, Oghenetega; Olusanya, Olamide; Daramola, Samuel; Awomoyi, Morayo E.
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8806

Abstract

Single-user electronic ticketing systems face significant security challenges, including fraud and counterfeiting. While blockchain has been explored for electronic ticketing, existing solutions often remain centralized or focus solely on event-based scenarios, not single-user tickets such as flight, train, bus, big transport schemes, movie tickets, and vouchers. This paper presents a decentralized single-user ticketing system to address this gap by utilizing Ethereum's ERC-721 standard for smart contracts (SC). Transparency and privacy are ensured through asymmetric encryption. Digital signatures validate ticket authenticity, and an innovative ERC-721-based verification process is applied. Leveraging Ethereum's ERC-721 Protocols, digital signatures, and the interplanetary file system (IPFS) for decentralized metadata storage, this paper addresses centralization, security, traceability, and transparency concerns. The SC is integrated into a web application, and empirical analysis based on blockchain metrics demonstrates its performance. Results indicate that the system exhibits an efficient ticket transaction completion time of 19.64 seconds and a mean ticket verification time of 3.17 seconds. The outcome illustrates the efficiency of the system in mitigating fraud, counterfeiting, and security risks in single-user electronic ticketing systems.
Implementation of a network intrusion detection system for man-in-the-middle attacks Okokpujie, Kennedy; Abdulateef-Adoga, William A.; Owivri, Oghenetega C.; Ijeh, Adaora P.; Okokpujie, Imhade P.; Awomoy, Morayo E.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp3913-3927

Abstract

Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
Co-Authors Abdulateef-Adoga, William A. Adaora, Ijeh Princess Adewale, Adeyinka Ajao Agamah, Alvin K. Anointing, Tamunowunari-Tasker Awomoy, Morayo E. Awomoyi, Morayo E. Awomoyi, Morayo Emitha Daramola, Samuel Daramola, Samuel Adebayo Ijeh, Adaora P. Ijeh, Adaora Princess Koto, Omiloli Mommoh, Joshua Sokowonci Obiazi, Oghorchukwuyem Ogundele, Mary Oluwafeyisayo Oguntuyo, Oluwadamilola Okokpujie, Imhade P. Okokpujie, Imhade Princess Olusanya, Olamide Omolara, Olusanya Olamide Orimogunje, Abidemi Owivri, Oghenetega Owivri, Oghenetega C. Tolulope, Adekoya