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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Salau, Ayodeji Olalekan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Security-based low-density parity check encoder for 5G communication Rajangam, Balamurugan; Alagarsamy, Manjunathan; Radhakrishnan, Chirakkal Rathish; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Quansah, Andrew; Chowdhury, Nur Mohammad; Chowdhury, Ismatul Jannat
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7019

Abstract

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.
Evaluation of structural failure resistance of glass fiber reinforced concrete beams Getachew Chikol, Yilachew; Assegie, Tsehay Admassu; Mohmmad, Shaimaa Hadi; Salau, Ayodeji Olalekan; Yanhui, Liu; Braide, Sepiribo Lucky
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 Salau, Ayodeji Olalekan; Arega, Kedir Lemma; Tin, Ting Tin; Quansah, Andrew; Sefa-Boateng, Kwame; Chowdhury, Ismatul Jannat; Braide, Sepiribo Lucky
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.
Soybean leaf disease detection and classification using deep learning approach Adimas, Ayenew Kassie; Mekonen, Mareye Zeleke; Assegie, Tsehay Admassu; Singh, Hemant Kumar; Mazumdar, Indu; Gupta, Shashi Kant; Salau, Ayodeji Olalekan; Tin, Ting Tin
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.v14i4.8585

Abstract

In Ethiopia, where soybeans are mainly involved, manual observation has traditionally been relied upon for detecting soybean leaf diseases. However, the manual process is susceptible to numerous issues such as labor-intensiveness, inconsistency, and subjectivity. While previous studies have explored automated classification for soybean leaf disease detection, they primarily focused on binary classification, overlooking the complexity and diversity of soybean leaf diseases, which hinders effective management strategies. This study introduces deep learning algorithms and computer vision for automated soybean leaf disease identification and classification in soybean leaves. By comparing pre-trained convolutional neural network (CNN) models (VGG16, VGG19, and ResNet50V2), a dataset of 3078 soybean leaf images was curated, representing various diseases. Image preprocessing techniques augmented the dataset to 6,958 images, enhancing the model's accuracy and generalization performance. VGG16 demonstrated outstanding performance with a test accuracy of 99.35%, highlighting its promising performance and generalization potential.
Co-Authors Adimas, Ayenew Kassie Alagarsamy, Manjunathan Arega, Kedir Lemma Assegie, Tsehay Admassu Ayalew, Amogne Andualem Braide, Sepiribo Lucky Chaojun, Yang Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Eneh, Joy Nnenna Getachew Chikol, Yilachew Gupta, Shashi Kant Kumar, Napa Komal Manivannan, Bommy Markus, Elisha Didam Mazumdar, Indu Mekonen, Mareye Zeleke Mohmmad, Shaimaa Hadi Ozue, ThankGod Izuchukwu Prasad, Battula Krishna Quansah, Andrew Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Sefa-Boateng, Kwame Singh, Hemant Kumar Suresh, Tamilarasi Tegegne, Melaku Lake Tin, Ting Tin Wondimu, Amberbir Yanhui, Liu