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Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFEnergy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism.
Heliyon
January 2025
Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia.
The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques.
View Article and Find Full Text PDFLiDAR point cloud denoising for individual tree extraction based on the Noise4Denoise.
Front Plant Sci
January 2025
Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing, China.
The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.
View Article and Find Full Text PDFAutomated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.
Gates Open Res
January 2025
University of Virginia, Charlottesville, Virginia, USA.
Background: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single reaction. However, the manual post-run analysis of TAC data is time consuming and subject to interpretation. We sought to automate the post-run analysis of TAC data using machine learning models.
View Article and Find Full Text PDFReinforcement Learning is Impaired in the Sub-acute Post-stroke Period.
Neurorehabil Neural Repair
January 2025
Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA.
Background: In humans, most spontaneous recovery from motor impairment after stroke occurs in the first 3 months. Studies in animal models show higher responsiveness to training over a similar time-period. Both phenomena are often attributed to a milieu of heightened plasticity, which may share some mechanistic overlap with plasticity associated with normal motor learning.
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