ARCHIVES
Fake News Classification Using Machine Learning and Deep Learning Technique
¹ ² Department of Computer Science & Engineering, BIT Mesra, Patna Campus, Bihar, India.
Published Online: March-April 2026
Pages: 326-335
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702040
Abstract
View PDFThis project focuses on detecting fake news using a hybrid approach combining machine learning and deep learning techniques. The dataset includes real and fake news articles that are cleaned and preprocessed to improve quality. For feature extraction, TF-IDF is used to convert text into numerical form. Machine learning models such as LogisticRegression, Support Vector Machine, and Naive Bayes are trained and evaluated. Transformer-based models like BERT and RoBERTa are also implemented. Model performance is measuredusing accuracy, precision, recall, and F1-score, along with confusion matrix and ROC analysis.A majority voting technique is applied to improve final predictions. Results show that deeplearning models provide better accuracy and reliability.
Related Articles
2026
AI-Based Stomach Cancer Detection Using Biomarkers, Medical Images, and Voice Analysis
2026
Hydrogen-Efficient Eco-Driving and Route Planning for Fuel-Cell Electric Vehicles Using Multi-Objective Optimization Under Traffic and Terrain Uncertainty
2026
A Data-Driven Machine Learning Framework for Assessing Patent Commercial Value and Technological Significance
2026
Soft Computing Approaches for Robust Analysis of Imbalanced and Noisy Data
2026
Smart Attendance System Using Face Recognition and Gaze-Based Attention Monitoring
2026
Analyzing Customer Review Sentiments using Machine Learning
2026
Agentic Artificial Intelligence as a Strategic HR Partner: Redefining Decision-Making Authority and Strategic Roles
2026
Solid Waste Management Rules, 2026 (India): Regulatory Design Review and Environmental Benefits for Urban Sustainability
2026
Optimizing Hospital Resource Utilization Using Power BI Analytics
2026
Contribution of Machine and Deep Learning methodologies in the identification of counterfeit currency notes
