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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ayodeji Olalekan Salau
Afe Babalola University
Computer Science & IT Electrical & Electronics Engineering

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A review of various image fusion types and transforms Ayodeji Olalekan Salau; Shruti Jain; Joy Nnenna Eneh
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1515-1522

Abstract

Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.
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.
Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks Ayodeji Olalekan Salau; Tsehay Admassu Assegie; Adedeji Tomide Akindadelo; Joy Nnenna Eneh
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4020

Abstract

Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
Estimation of concrete compression using regression models Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Tayo Uthman Badrudeen
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4210

Abstract

The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).
Multivariate sample similarity measure for feature selection with a resemblance model Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Crescent Onyebuchi Omeje; Sepiribo Lucky Braide
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3359-3366

Abstract

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
Explainable extreme boosting model for breast cancer diagnosis Tamilarasi Suresh; Tsehay Admassu Assegie; Sangeetha Ganesan; Ravulapalli Lakshmi Tulasi; Radha Mothukuri; Ayodeji Olalekan Salau
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5764-5769

Abstract

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.
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 Adedeji Tomide Akindadelo Aitizaz Ali Aitizaz Ali Andrew Quansah Angati Kalyan Kumar Crescent Onyebuchi Omeje Dawit Assefa Funso Kehinde Ariyo Ismatul Jannat Chowdhury Joy Nnenna Eneh Kedir Lemma Arega Komal Kumar Napa Kwame Sefa-Boateng Liu Yanhui Radha Mothukuri Ravulapalli Lakshmi Tulasi Sangeetha Ganesan Sepiribo Lucky Braide Sepiribo Lucky Braide Shaimaa Hadi Mohmmad Shruti Jain Tamilarasi Suresh Tayo Uthman Badrudeen Tayo Uthman Badrudeen Ting Tin Tin Tsehay Admassu Assegie Yilachew Getachew Chikol