Results 101 to 110 of about 41,144 (258)

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Benchmarking uncertainty quantification for protein engineering.

PLoS Computational Biology
Machine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods
Kevin P Greenman, Ava P Amini, Kevin K Yang   +2 more
doaj   +1 more source

BAYES-GAIT: A Bayesian Deep Neural Network for Continuous Gait Reference Trajectory Generation Using an Enhanced Loss Function

IEEE Access
Background: Adaptive gait trajectory prediction is essential to achieve natural and stable locomotion in prosthetic limbs and legged robots, particularly under varied conditions such as changing inclines and walking speeds.
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar, Naveen Gehlot   +4 more
doaj   +1 more source

Harnessing Large‐Scale Multi‐Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the General Population

Advanced Science, EarlyView.
Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang, Yang Liu, Mingyu Song, Ning Chen, Canqing Yu, Jun Lv, Eric Yuk Fai Wan, Lu Qi, Liming Li, Dianjianyi Sun, Bang Zheng   +10 more
wiley   +1 more source

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Scientific Reports
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it ...
Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh   +6 more
doaj   +1 more source

Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach

CoRR
With recent advancements in the development of artificial intelligence applications using theories and algorithms in machine learning, many accurate models can be created to train and predict on given datasets. With the realization of the importance of imaging interpretation in cancer diagnosis, this article aims to investigate the theory behind Deep ...
Xi, Pei, Lin
openaire   +3 more sources

Multiscale Circuit Architecture Associated With Memory Dysfunction in Temporal Lobe Epilepsy

Advanced Science, EarlyView.
A multiscale precision‐mapping framework reveals that memory impairment in temporal lobe epilepsy arises from the convergence of focal medial temporal pathology, strategic white matter disconnection, and limbic‐centered metabolic network dysfunction.
Jiajie Mo, Qiwen Yuan, Pengfei Zhang, Lin Sang, Zhong Zheng, Baotian Zhao, Xiu Wang, Chao Zhang, Wenhan Hu, Xiaoqiu Shao, Jianguo Zhang, Ruijin Yang, Kai Zhang   +12 more
wiley   +1 more source

Forecasting VIX using Bayesian deep learning

International Journal of Data Science and Analytics
Abstract Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX.
Héctor J. Hortúa, Andrés Mora-Valencia   +1 more
openaire   +3 more sources

Automating Chemical Reasoning in High‐Throughput Phase Identification With a Probabilistic, LLM‐Guided Framework

Advanced Science, EarlyView.
Autonomous laboratories can now synthesize materials faster than experts can interpret the resulting diffraction data. A probabilistic framework combines refinement‐fit metrics with large language model‐derived chemical reasoning to rank competing phase interpretations and flag those unsuitable for autonomous use.
Olympia Dartsi, Lauren N. Walters, Amalie E. Trewartha, Steven B. Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain   +7 more
wiley   +1 more source

Development of hybrid computational model for simulation of heat transfer and temperature prediction in chemical reactors

Scientific Reports
Computational modeling based on heat transfer was developed to model temperature distribution in a liquid-phase chemical reactor. The model is hybrid by combining heat transfer and machine learning in simulation of the process. The simulated process is a
Kamal Y. Thajudeen, Mohammed Muqtader Ahmed, Saad Ali Alshehri   +2 more
doaj   +1 more source