Results 171 to 180 of about 8,272,458 (291)

From Traditional Inspection to Quantitative Imaging: Tongue and Facial Color Features for Automated Machine Learning-Driven Depression and Schizophrenia Classification. [PDF]

Behav Sci (Basel)
Gao L, Zhang M, Li Y, Wang L, Qian P, Tong J, Fu H, Sun X, Li F.   +8 more
europepmc   +1 more source

Online feature selection for model-based reinforcement learning

, 2013
30th International Conference on Machine Learning, ICML 2013PART 1498 ...
Li, Zhuoru, Tze-Yun LEONG,, Nguyen, Trung Thanh, Leong, T.-Y., Silander, T., Li, Z., Nguyen, T.T., Silander, Tomi   +7 more
core  

Ethical Precision in Nanoscale Brain Interfacing

Advanced Science, EarlyView.
As brain interfaces approach the nanoscale, precision no longer only measures—it knows, predicts, and potentially reshapes the mind. This work argues that traditional ethics fails under such conditions and proposes a shift toward continuous, operation‐based governance using the recovery–discovery framework to track, constrain, and responsibly steer ...
Guilherme Wood
wiley   +1 more source

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms. [PDF]

iScience
Ao R, Zhan P, Wang G, Liu H, Wang W.
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography. [PDF]

Front Cardiovasc Med
Olivier A, Riou A, d'Humières T, Touzot M, Elgui K, Sauty B, Trichelair P, Ducret V, Telenczuk M, Simon A, Balazard F, Balan MM, Bastien A, Sehnert A, Zannad F, Steg PG, Blizard P, Turvey-Haigh L, Ripoll T, Bradlow W, Charron P.   +20 more
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Development of automated machine learning models in predicting colorectal adenoma in metabolic-associated steatotic liver disease. [PDF]

Front Med (Lausanne)
Liu L, Zhang H, Zhao Z, Xu C, Qu S.
europepmc   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review. [PDF]

JMIR AI
Castonguay A, Hegg-Deloye S, Chatton A, Goyette A.   +3 more
europepmc   +1 more source