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| http://dx.doi.org/10.4103/IJO.IJO_2982_23 | DOI Listing |
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Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFDiscovery of anti-SARS-CoV-2 S2 protein antibody CV804 with broad-spectrum reactivity with various beta coronaviruses and analysis of its pharmacological properties in vitro and in vivo.
PLoS One
December 2024
Laboratory of Antibody Design, Center for Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.
The SARS-CoV-2 pandemic alerted the potential for significant harm due to future cross-species transmission of various animal coronaviruses to human. There is a significant need of antibody-based drugs to treat patients infected with previously unseen coronaviruses. In this study, we generated CV804, an antibody that binds to the S2 domain of SARS-CoV-2 spike protein, which is highly conserved across the coronavirus family and less susceptible to mutations.
View Article and Find Full Text PDFAdvanced Lung Disease Detection: CBAM-Augmented, Lightweight EfficientNetB2 with Visual Insights.
Curr Med Imaging
December 2024
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Introduction: This paper presents a multichannel deep-learning method for detecting lung diseases using chest X-ray images. Using EfficientNetB0 through EfficientNetB7 pretrained models, the methodology offers improved performance in classifying COVID-19, viral pneumonia, and normal chest Xrays.
Methods: The EfficientNetB2 model was customized by incorporating Squeeze-and-Excitation (SE) blocks and the Convolutional Block Attention Module (CBAM) to improve the model's attention mechanisms.
Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.
Phys Med
November 2024
Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081 Aviano, Italy.
Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.
Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).
The unseen in COVID-19.
Indian J Ophthalmol
October 2024
Department of Microbiology, Saveetha Medical College, Chennai, Tamil Nadu, India.
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