Results 241 to 250 of about 143,911 (316)

Multi‐command wearable magnetic human‐machine interface enabled by low‐noise sensing and time‐series deep learning

InfoScience, EarlyView.
This study integrates a wrist‐worn sensing platform based on a low‐noise planar Hall magnetoresistive sensor with time‐series deep learning to enable long‐range, single‐sensor, multi‐command gesture recognition with high accuracy. Further evaluations show reliable generalization across different days, previously unseen users, wearing‐position ...
Guannan Mu, Proloy Taran Das, Jan Schmidtpeter, Andreas Knüpfer, Rui Xu, Denys Makarov   +5 more
wiley   +1 more source

A multi-model prediction of a stage-specific prognosis for colorectal cancer using attention-driven deep ensemble learning on genomic profiling data. [PDF]

Front Artif Intell
Supriya K, Anitha A.
europepmc   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

LSTM-Transformer-Based Mine Pressure Prediction Using Hydraulic-Support Monitoring Data. [PDF]

Sensors (Basel)
Tao R, Lei X, Wan L, Wang Y, Xu N.
europepmc   +1 more source

Remote Sensing for Irrigation Water Management—The State of the Art and Prospects for Near‐Future Operations

Irrigation and Drainage, EarlyView.
ABSTRACT A recent UN report describes many regions as facing ‘water bankruptcy,’ a condition in which available water resources can no longer meet existing demands. In practice, this means irrigation will likely bear the greatest burden of future water‐use reductions, a complicated and fraught decision to make given its critical role in food security ...
Wim G. M. Bastiaanssen, Chris Perry, Royce Dalby, Yasir Mohamed, Xueliang Cai, Poolad Karimi, IJsbrand de Jong, Pasquale Steduto, Marat Beksultanov, Christopher C. M. Neale, David Molden, Waqas Awan, Laarousi Mohamed, Jippe Hoogeveen, Jeff Davids, Nutchanart Sriwongsitanon, P. S. Rao, Roula Bachour, Palitha Bandara, Sophea Chhim, Richard Evans, Tom Lahad, D. Nagesh Kumar, Zakaria Adawy, Devendra Singh Bundela, Kawa Hakzi, Isaya Kisekka, Nicolas Puelma, Alejandro A. Pannunzio, Benjamin Koetz, Mukhiddin Ibragimov   +30 more
wiley   +1 more source

A JSBSim Sensor-Interface Protocol for Selecting Learned Fixed-Wing Flight-Dynamics Surrogates. [PDF]

Sensors (Basel)
Feng Y, Li J, Yang S, Ji Y.
europepmc   +1 more source

The Role of Artificial Intelligence in Modern Allergology: A Review of Applications in Diagnosis, Prediction, and Management

JEADV Clinical Practice, EarlyView.
ABSTRACT Artificial Intelligence is rapidly transforming allergology by enhancing diagnosis, risk prediction, automation, patient communication, education, and therapy development. Machine learning approaches, including convolutional neural networks, recurrent architectures, and transformer‐based models, enable analysis of complex datasets from ...
Sebastian Seurig, Stephan Traidl, Sonja Mathes   +2 more
wiley   +1 more source

Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator. [PDF]

PLoS One
Wang S, Wang P, Wang X, Chen S, Liu M.
europepmc   +1 more source

Artificial Intelligence for Identifying Tumor‐Reactive CD8+ T Cells: Biological Principles, Computational Advances, and Future Directions

Med Research, EarlyView.
This review details a three‐stage paradigm shift for tumor‐reactive CD8+ T‐cell identification: decoding transcriptomic states, deciphering clonal functional efficacy, and molecular‐level therapeutic TCR design. Addressing translational hurdles and generative AI “scientific blind spots”—such as missing catch bonds—we present a visionary roadmap.
Chao Yang, Yingying Sun, Yan Zhang, Yifen Shi, Zhiwen Shi   +4 more
wiley   +1 more source

Lower Limb Motion Classification of Actions in Confined Environments Based on Multi-Source Signal Fusion and Muscle Symmetry Features. [PDF]

Sensors (Basel)
Li D, Guan X, Wang Z, Jiang C, He L, Mao X, Zhou Q.   +6 more
europepmc   +1 more source