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Transformer-Based Tool for Automated Fact-Checking: A Pilot Study on Online Health Information.
JMIR Infodemiology
December 2024
Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Montreal, CA.
Background: Many people seek health-related information online. The significance of reliable information became particularly evident due to the potential dangers of misinformation. Therefore, discerning true and reliable information from false information has become increasingly challenging.
View Article and Find Full Text PDFHarnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks.
Sci Rep
January 2025
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India.
Article Synopsis
- The increasing use of connected devices in smart homes has heightened security risks, particularly from Man-in-the-Middle (MitM) attacks, which can go undetected.
- Traditional security methods struggle to manage these complex threats, highlighting the need for more advanced intrusion detection systems.
- The AEXB Model, a hybrid deep learning approach combining AutoEncoder for feature extraction and XGBoost for classification, achieves 97.24% accuracy in detecting MitM attacks, while also enabling real-time threat responses and continuous protection.
Impact of sample heterogeneity on the evaluation of uncertainty from sampling and analytical steps in pharmaceutical analysis.
J Pharm Biomed Anal
January 2025
Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, Av. Prof. Lineu Prestes, 580 - Bloco 15, SP, São Paulo CEP 05508-000, Brazil. Electronic address:
Measurement uncertainty is a critical factor in the reliability of pharmaceutical analyses, since it directly affects batch acceptance and regulatory compliance. While analytical uncertainty has been extensively studied, uncertainty arising from sampling remains less explored. This study aims to address this gap by evaluating the contributions of sampling and analytical uncertainties to the overall uncertainty for acetaminophen tablets and oral solution.
View Article and Find Full Text PDFAccurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.
Abdom Radiol (NY)
January 2025
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India.
Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images.
View Article and Find Full Text PDFBlood Culture Contamination and Diagnostic Stewardship: From a Clinical Laboratory Quality Monitor to a National Patient Safety Measure.
J Appl Lab Med
January 2025
Centers for Disease Control and Prevention (CDC), Office of Laboratory Systems and Response (OLSR), Divsion of Laboratory Systems (DLS), Atlanta, GA, United States.
Laboratory analysis of blood cultures is vital to the accurate and timely diagnosis of bloodstream infections. However, the reliability of the test depends on clinical compliance with standard operating procedures that limit the risk of inconclusive or incorrect results. False-negative blood culture results due to inadequate volumes of blood can result in misdiagnosis, delay therapy, and increase patients' risk of developing or dying from bloodstream infections.
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