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Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment.
Sci Rep
January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
View Article and Find Full Text PDFIndividual differences in working memory and attentional control continue to predict memory performance despite extensive learning.
J Exp Psychol Gen
January 2025
Institute for Mind and Biology, University of Chicago.
Individual differences in working memory predict a wide range of cognitive abilities. However, little research has been done on whether working memory continues to predict task performance after repetitive learning. Here, we tested whether working memory ability continued to predict long-term memory (LTM) performance for picture sequences even after participants showed massive learning.
View Article and Find Full Text PDFQuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.
Anal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
View Article and Find Full Text PDFA Low-Power General Matrix Multiplication Accelerator with Sparse Weight-and-Output Stationary Dataflow.
Micromachines (Basel)
January 2025
Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture.
View Article and Find Full Text PDFFrom Online Learning to Clinical Practice: An Investigation on the Factors Influencing Training Transfer Among Physicians.
Healthcare (Basel)
January 2025
Department of Humanities, University of Naples Federico II, 80133 Naples, Italy.
Massive Open Online Courses (MOOCs) are an agile context for workplace training, which can provide physicians with needed knowledge and skills related to their clinical practice. From an organisational standpoint, their effectiveness can be assessed on physicians' intention to transfer what they learn through them in the workplace. Despite the Theory of Planned Behaviour (TPB) standing among the more solid models in explaining individuals' behavioural intention, its adoption in investigating the training transfer process among physicians is notably underdeveloped, limiting its contribution to enhancing the transfer rates of MOOCs content.
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