Results 201 to 210 of about 6,372,670 (283)

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Exploring unsupervised learning techniques for early detection of myocardial ischemia in type 2 diabetes. [PDF]

Front Endocrinol (Lausanne)
Liu B, Hou YJ, Wu P, Han X, Qi H, Yang XY, Wu ZF, Li SJ.   +7 more
europepmc   +1 more source

An Integrated Fabric‐Pneumatic Soft Sensing System for Multi‐Site Muscle Activity Decoding

Advanced Intelligent Systems, EarlyView.
Fabric‐pneumatic chambers convert muscle deformation into pressure signals without electronics at the body‐contact sensing interface. A unified processing pipeline enables static posture classification, continuous elbow‐angle estimation, forearm gesture recognition, and knee‐movement detection across three anatomical sites.
Qifeng Xun, Jonathan William Ambrose, Quan Xiong, Chen Hua Yeow   +3 more
wiley   +1 more source

Technical classification of professional cycling stages using unsupervised learning: implications for performance variability. [PDF]

Front Sports Act Living
Garcia-Atutxa I, Dudagoitia Barrio E, Villanueva-Flores F.   +2 more
europepmc   +1 more source

ViTAMIn‐O: Democratizing Computer Vision‐Based Machine Learning for Stem Cell Research

Advanced Intelligent Systems, EarlyView.
This study introduces a fully open generalist computer vision model for predicting the differentiation outcomes of stem cell‐based model systems, together with its code‐free deployment platform, ColabViTAMIn‐O. It is benchmarked against various architectures, datasets, and training protocols.
Ferhat Hamurcu, Markus Breunig, Arpad Varga, Bianka Bosch, Jessica Lindenmayer, Anjan T. Kanakappady, Kevin Achberger, Natalia Pashkovskaia, Alexander Kleger, Stefan Liebau, Stefanie Klingenstein, Moritz Klingenstein   +11 more
wiley   +1 more source

Fault Detection in MV Switchgears Through Unsupervised Learning of Temperature Conditions. [PDF]

Sensors (Basel)
Iadarola G, Mingotti A, Negri V, Spinsante S.   +3 more
europepmc   +1 more source

Occupational Vulnerability to AI‐Driven Change: The Role of Skill Composition, Task Structure, and Psychosocial Buffers

American Journal of Industrial Medicine, EarlyView.
ABSTRACT Background Automation risk is often framed as a question of which jobs can be automated. This perspective neglects how the structure of work itself shapes workers' vulnerability to AI‐driven change. In occupational health research, job characteristics such as autonomy, task variety, and social interaction are known to influence worker well ...
Thinuri Welithotage, Behdin Nowrouzi‐Kia   +1 more
wiley   +1 more source

Unsupervised learning of temporal regularities in visual cortical populations. [PDF]

Nat Commun
Pojoga S, Andrei A, Dragoi V.
europepmc   +1 more source

Integrating One Health to Mitigate the Emergence and Spread of Antimicrobial Resistance in Livestock and Aquaculture

Animal Research and One Health, EarlyView.
Antimicrobial resistance (AMR) is an escalating global threat driven by antimicrobial use in aquaculture and livestock. Resistant pathogens and genes can spread across humans, animals, and the environment through interconnected ecosystems. Using a One Health approach, this review emphasizes antimicrobial stewardship, regulatory strengthening, enhanced ...
Mir Mohammad Ali, Delower Hossain, Ridwan Olamilekan Adesola, Anum Hanif, Shamsaldeen Ibrahim Saeed, Muhammad Abdul Mannan, Anoar Jamai Masroure, Kazi Estieque Alam, Md Kamal Hossain, Sabiha Zarin Tasnim Bristi, Md Jisan Ahmed   +10 more
wiley   +1 more source

Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography. [PDF]

Biomed Phys Eng Express
Victor M, Ribeiro A, Matsumoto M, Xin Y, Nova A, Gaulton T, Cereda M.   +6 more
europepmc   +1 more source