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All Journal Bulletin of Electrical Engineering and Informatics
Braide, Sepiribo Lucky
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Electrical & Electronics Engineering

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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.
Co-Authors Arega, Kedir Lemma Assegie, Tsehay Admassu Chowdhury, Ismatul Jannat Getachew Chikol, Yilachew Mohmmad, Shaimaa Hadi Quansah, Andrew Salau, Ayodeji Olalekan Sefa-Boateng, Kwame Tin, Ting Tin Yanhui, Liu