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Quansah, Andrew
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Electrical & Electronics Engineering

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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.
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 Alagarsamy, Manjunathan Arega, Kedir Lemma Assegie, Tsehay Admassu Braide, Sepiribo Lucky Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Salau, Ayodeji Olalekan Sefa-Boateng, Kwame Tin, Ting Tin