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All Journal Scientific Journal of Computer Science
Sani, Nura Muhammad
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Computer Science & IT

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Journal : Scientific Journal of Computer Science

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Hybrid Deep Learning Model for Fake News Detection on Social Media Using CNN-GRU on X formerly known as Twitter Muhammad, Lawan Jibril; Mohammed, Isa Umar; Sani, Nura Muhammad
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.400

Abstract

The spread of fake news on social media platforms has created a dilemma for the world community by spreading false information and eroding public confidence. Fake news spreads quickly and seriously harms society. Predicting and identifying fake news is crucial for preserving the integrity of information ecosystems in the wake of an epidemic of multiple high-profile disinformation efforts. In order to detect fake news, this work suggests a hybrid deep learning algorithm called Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU), which combines the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) learning algorithms in an efficient manner. Models for identifying fake news were developed using deep learning-based methods, such as CNN, GRU, and CNN-GRU deep learning algorithms. Four standard performance metrics—accuracy, precision, recall, and F1-score—were used to evaluate the models. Nevertheless, the CNN-GRU deep learning-based detection model outperformed models created with CNN and GRU, achieving the maximum accuracy of 98.77%, 98.68%, 98.73%, and 98.71% for precision, recall, and F1-score, respectively. With a combined accuracy of 98.77%, precision of 98.68%, recall of 98.73%, and F1-score of 98.71%, the CNN-GRU deep learning-based false news detection model performs better than the two other deep learning-based models.
Deep Learning for Venomous and Non-Venomous Snakes Classification Lidani, Yakubu Abubakar; Yola, Abdullahi Musa; Tasiu, Abu; Sani, Nura Muhammad; Gidado, Sulaiman Muhammad
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.463

Abstract

Snakes are a major health threat in various communities, specifically where human and snake encounters are frequent. When a snake is not identified correctly, healthcare providers often administer the wrong treatment, this can worsen patient recovery outcomes or even prove fatal to the victim. Therefore, a fast, proper and accurate distinction between venomous and nonvenomous snakes is vital for proper antivenom administration. This study proposes a hybrid deep learning system combining a CNN and an LSTM model for snake image classification through feature extraction from visual data. The CNN extracts key spatial features such as colour and scale patterns, texture, and body shape, whereas the LSTM captures sequential dependencies across these features, by helping distinguish visual similarity amongst the species. The model was trained and evaluated on a dataset of 6,798 snake images from diverse sources. The system achieved a performance of 97% accuracy, 97% precision, 96% recall, an F1-score of 97%, and a ROC-AUC of 0.97. These results demonstrate that integrating CNN and LSTM is moderately effective for snake classification. The proposed system has practical applications in the area of emergency healthcare, wildlife management, as well as mobile based identification tool. With 97% accuracy, this model can improve emergency responders first aid, enhance a safer treatment administration and help make safer decisions on the use of antivenom, by reducing treatment delays and improving patient survival prognosis. This model has the potential to save lives and minimize the consequences of snakebite envenoming.
Co-Authors Gidado, Sulaiman Muhammad Lidani, Yakubu Abubakar Mohammed, Isa Umar Muhammad, Lawan Jibril Tasiu, Abu Yola, Abdullahi Musa