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All Journal Bulletin of Electrical Engineering and Informatics
Sepiribo Lucky Braide
Rivers State University
Electrical & Electronics Engineering

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Analysis of a new voltage stability pointer for line contingency ranking in a power network Tayo Uthman Badrudeen; Funso Kehinde Ariyo; Ayodeji Olalekan Salau; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4266

Abstract

Improper management of reactive power in a power network could lead to voltage instability. This paper presents a well-detailed study on voltage instability due to violation of power equilibrium in a power network and introduces a new voltage stability pointer (NVSP). The proposed NVSP is developed from a reduced 2-bus interconnected network to predict the sensistivity of voltage stability to reactive power variation. The simulation results from MATLAB were evaluated on IEEE 14-bus test system. The contingency ranking was achieved by varying the reactive power on the load buses to its maximum loading limit. The maximum reactive power point was taken at each load bus and the critical lines were ranked according to their vulnerability to voltage collapse. The results were compared with other notable voltage stability indices. The results prove that the NVSP is an essential tool in predicting voltage collapse.
CT-based lung cancer detection using spatially localized integral transforms with U-NET classification Abel Belay Wossene; Dawit Assefa; Ayodeji Olalekan Salau; Sepiribo Lucky Braide; Aitizaz Ali; Ting Tin Tin
Bulletin of Electrical Engineering and Informatics 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/eei.v15i1.9161

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the need for early, accurate, and scalable detection methods. Low-dose computed tomography (LDCT) has improved early diagnosis, yet challenges like image noise, low contrast, and subtle nodule features often limit reliable interpretation in large-scale screening. This paper proposes a computationally efficient computer-aided detection (CADe) framework that integrates a rotation-invariant, spatially localized integral transform feature extraction with a U-Net-based classifier to enhance lung nodule detection and segmentation. The approach strengthens spatial feature representation while maintaining low computational and memory demands, enabling real-time use in resource-limited clinical settings. Implemented in MATLAB and evaluated on the Cancer Imaging Archive (TCIA) dataset, the system achieved 99.32% classification accuracy, 88.88% specificity, 84.21% precision, 87.3% intersection over union (IoU), and 92.9% dice similarity coefficient (DSC). These results show clear improvements over conventional methods, particularly in rotational robustness and efficiency—key requirements for scalable screening. Although precision and IoU could be further optimized, the framework demonstrates strong potential for clinical adoption. By providing accurate, fast, and robust nodule analysis, this work advances practical high-performance tools for early lung cancer detection, especially in resource-constrained environments, ultimately contributing to better patient survival rates.
Evaluation of structural failure resistance of glass fiber reinforced concrete beams Yilachew Getachew Chikol; Tsehay Admassu Assegie; Shaimaa Hadi Mohmmad; Ayodeji Olalekan Salau; Liu Yanhui; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6620

Abstract

Glass fiber reinforced concrete (GFRC) is a composite material that is widely used in construction due to its high strength and durability. GFRC is made by adding glass fibers to the concrete mix, which increases the tensile strength of the material. This paper evlautes the shear resistance (SR) of sliced glass fiber (30 mm) GFRC beams. The shear resistance of GFRC beams can be significantly improved by adding glass fibers to the concrete mix. However, further research is needed to fully understand the shear behavior of GFRC and to optimize its design for maximum shear resistance. The result indicates that shear fracture glass fiber is a better alternative for increasing a shear resistance input mechanism.
Machine learning-based detection of fake news in Afan Oromo language Ayodeji Olalekan Salau; Kedir Lemma Arega; Ting Tin Tin; Andrew Quansah; Kwame Sefa-Boateng; Ismatul Jannat Chowdhury; Sepiribo Lucky Braide
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8016

Abstract

This paper presents a machine learning-based (ML) approach for identifying fake news on web-based social media networks. Data was acquired from Facebook to develop the model which was used to identify Afan Oromo's false news. The system architecture uses algorithms, such as support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) to detect and classify fake news. Existing models have limitations in understanding reported news accuracy compared with verified news. This study successfully resolved the challenges in the detection of social media fake news detection for the Afan Oromo language with the use of ML models and natural language processing (NLP) techniques. The results show that the SVM approach achieved a precision, recall, and F1-score, of 0.92, 0.92, and 0.90.
Enhanced air quality index classification: leveraging genetic algorithm and SMOTE for accurate assessments in Indian cities Komal Kumar Napa; Ayodeji Olalekan Salau; Angati Kalyan Kumar; Sepiribo Lucky Braide; Aitizaz Ali; Ting Tin Tin
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.9996

Abstract

The escalating air pollution levels in Indian metropolitan regions necessitate robust predictive systems for air quality assessment. This study presents an advanced air quality index (AQI) forecasting model leveraging the radial basis function (RBF) kernel-based extreme learning machine (ELM) optimized using a genetic algorithm (GA). The proposed model is evaluated on real-time AQI datasets from four major Indian cities: Vishakhapatnam, Delhi, Hyderabad, and Patna. Initial experiments without class balancing yielded prediction accuracies of 83.9%, 88.3%, 87.0%, and 86.9% respectively. To address the class imbalance and enhance predictive performance, the synthetic minority oversampling technique (SMOTE) was applied. Post-balancing, the model achieved significantly improved accuracies of 93.9%, 94.7%, 92.3%, and 96.2% across the respective cities. These results underscore the effectiveness of integrating SMOTE with RBF-ELM for AQI prediction and demonstrate the critical role of data balancing in improving model generalizability. The proposed approach offers a promising solution for urban air quality monitoring and can assist policymakers in formulating timely interventions to mitigate health risks associated with air pollution.
Co-Authors Abel Belay Wossene Aitizaz Ali Aitizaz Ali Andrew Quansah Angati Kalyan Kumar Ayodeji Olalekan Salau Dawit Assefa Funso Kehinde Ariyo Ismatul Jannat Chowdhury Kedir Lemma Arega Komal Kumar Napa Kwame Sefa-Boateng Liu Yanhui Shaimaa Hadi Mohmmad Tayo Uthman Badrudeen Ting Tin Tin Tsehay Admassu Assegie Yilachew Getachew Chikol