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Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.
JMIR Form Res
January 2025
Smith School of Business, Queen's University, Kingston, ON, Canada.
Background: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns.
View Article and Find Full Text PDFPerformance analysis of image retrieval system using deep learning techniques.
Network
January 2025
Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, India.
The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.
View Article and Find Full Text PDFEfficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration.
Front Plant Sci
January 2025
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
The precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry's sustainable progression. This research addresses the inherent subjectivity and variability in conventional maturity evaluation techniques reliant on human expertise by introducing an innovative YOLOv10-based method for tobacco leaf maturity detection. This technique facilitates a rapid and non-invasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation.
View Article and Find Full Text PDFEvaluating GPT Models for Automated Literature Screening in Wastewater-Based Epidemiology.
ACS Environ Au
January 2025
Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Methods to quantitatively synthesize findings across multiple studies is an emerging need in wastewater-based epidemiology (WBE), where disease tracking through wastewater analysis is performed at broad geographical locations using various techniques to facilitate public health responses. Meta-analysis provides a rigorous statistical procedure for research synthesis, yet the manual process of screening large volumes of literature remains a hurdle for its application in timely evidence-based public health responses. Here, we evaluated the performance of GPT-3, GPT-3.
View Article and Find Full Text PDFA machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.
Front Neurol
January 2025
Department of Neurosurgery, Tokyo Medical and Dental University, Bunkyo-ku, Japan.
Objective: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to improve patient outcomes. In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.
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