Results 91 to 100 of about 15,495,898 (198)

Sensory Processing Profiles and Learning Potential in Autism: A Longitudinal Study of Cognitive Development in Preschoolers. [PDF]

J Intell
Robles-Bello MA, Barba-Colmenero F, Camino-Alarcón JV, Valencia-Naranjo N.   +3 more
europepmc   +1 more source

Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential. [PDF]

Nanomaterials (Basel), 2023
Han J, Zeng Q, Chen K, Yu X, Dai J.
europepmc   +1 more source

Time-varying risk prediction for oral potentially malignant disorders using deep learning

Jing Wen Li, Zi Han Lin, Xiang Zhu, Liu Yang Zhao, Ya Fang Zhou, Hui Feng, Hannah On Lap Yiu, John Adeoye, Jingya Jane Pu, Amy B. W. Chan, Ou Sheng Liu, Peter Thomson, Colman McGrath, Dian Zhang, Li Wu Zheng   +14 more
openalex   +1 more source

Integrated machine learning and AHP-based framework for spatial delineation of rainwater harvesting potential [PDF]

Agradeep Mohanta, Ameet Yadav, Dharmendra Singh, Sultan Singh, Shuchi Mishra, Kanwarpreet Singh, Abhishek Sharma, Amenjor Senagah, Aditya Kumar Tiwary, Chandni Thakur, Mohamed A. Mattar   +10 more
openalex   +1 more source

Evidence for Cognitive Styles in Imaginitive Play

, 1981
Flyer for speaker Dr. Dennie Wolf sponsored by Learning About Learning through the Trinity University Psychology department Colloquia ...
Learning About Learning
core  

Integrative analysis explores potential trigeminal neuralgia-associated biomarkers in major depressive disorder via bioinformatics and machine learning

Mu Li, YiMing Li, QingHua Li, ZiYe Tang, Wenchao Zhang, Hao Cheng   +5 more
openalex   +1 more source

Growing Imaginations

Details on a list of Learning About Learning children's workshops offered at Southwest Craft ...
Learning About Learning
core   +1 more source

Self-single-atomization of active sites in electrochemical ammonia synthesis unveiled by machine learning potential. [PDF]

Natl Sci Rev
Long J, Yang C, Li H, Li H, Xiao J.
europepmc   +1 more source

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. [PDF]

J Chem Inf Model, 2023
Han F, Hao D, He X, Wang L, Niu T, Wang J.   +5 more
europepmc   +1 more source

Programmed cell death-driven molecular subtyping identifies ARPC1B as a potential immune-fibrotic biomarkers in diabetic nephropathy via multi-model machine learning

Fu JueMin, Baoai Wu, Linsheng Meng
openalex   +1 more source