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All Journal International Journal of Electrical and Computer Engineering
Gaddam, Venu Gopal
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Computer Science & IT Electrical & Electronics Engineering

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Application of deep learning and machine learning techniques for the detection of misleading health reports Jaladanki, Ravindra Babu; Murthy, Garapati Satyanarayana; Gaddam, Venu Gopal; Nagamani, Chippada; Ramesh, Janjhyam Venkata Naga; Eluri, Ramesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp373-382

Abstract

In the current era of vast information availability, the dissemination of misleading health information poses a considerable obstacle, jeopardizing public health and overall well-being. To tackle this challenge, experts have utilized artificial intelligence methods, especially machine learning (ML) and deep learning (DL), to create automated systems that can identify misleading health-related information. This study thoroughly investigates ML and DL techniques for detecting fraudulent health news. The analysis delves into distinct methodologies, exploring their unique approaches, metrics, and challenges. This study explores various techniques utilized in feature engineering, model architecture, and evaluation metrics within the realms of machine learning and deep learning methodologies. Additionally, we analyze the consequences of our results on enhancing the efficacy of systems designed to detect counterfeit health news and propose possible avenues for future investigation in this vital area.
Co-Authors Eluri, Ramesh Jaladanki, Ravindra Babu Murthy, Garapati Satyanarayana Nagamani, Chippada Ramesh, Janjhyam Venkata Naga