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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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Journal : Bulletin of Electrical Engineering and Informatics

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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.
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