Results 211 to 220 of about 3,278,052 (298)

Cellular Material Network: A General Machine Learning Architecture for Predicting Mechanical Properties of Cellular Materials

Advanced Intelligent Systems, EarlyView.
This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou, Qiang Gao, Liuchao Jin, Guoquan Xie, Kang Zhang, Wei‐Hsin Liao   +5 more
wiley   +1 more source

BrainGraphNet-ViT model: a hybrid model combining vision transformers and graph convolutional networks for brain tumor diagnosis. [PDF]

Sci Rep
Elhamzi W, Mokni R.
europepmc   +1 more source

A Soft Robotic Finger With Deep Learning‐Enhanced Tactile Sensing for Texture and Softness Recognition

Advanced Intelligent Systems, EarlyView.
Inspired by human touch, a tendon‐driven soft robotic finger combines multimodal tactile sensing and deep learning to simultaneously perceive texture and softness on deformable surfaces. A CNN‐LSTM model fuses pressure, accelerometer, and gyroscope signals to accurately classify material properties, achieving up to 95.4% texture and 97.0% softness ...
Gorkem Anil Al, Arya Shabani, Nicos Hadjigeorgiou, James Fant‐Male   +3 more
wiley   +1 more source

BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks. [PDF]

Quant Biol
Sartono A, Trilaksono BR, Damayanti S, Nugroho AS, Firdayani F.   +4 more
europepmc   +1 more source

Behaviorally Adaptive and Inclusive Advanced Driver‐Assistance Systems

Advanced Intelligent Systems, EarlyView.
Advanced driver‐assistance systems (ADASs) are mapped as evolving human‐centered, adaptive technologies linking sensing, driver monitoring, AR/HUD interfaces, patents, regulation, and inclusive design. The review identifies gaps in real‐world evidence, diverse‐driver validation, gaze metrics, and governance, outlining a roadmap for safer, behaviorally ...
Jana Skirnewskaja, Mardavij Roozbehani, Munther A. Dahleh   +2 more
wiley   +1 more source

T-pGNN4DTI: Towards better drug-target interactions prediction using Global Self-attentive Pooled Graph Convolutional Networks and protein pre-training Models. [PDF]

PLoS One
Lin Y, Yang B, Liao J, Du C, Cai H, Wu Y, Peng Y.   +6 more
europepmc   +1 more source

Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers

Advanced Intelligent Systems, EarlyView.
A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley   +1 more source

Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy. [PDF]

NPJ Precis Oncol
Zhang S, Liu Y, Liu Z, Li X, Wang G, Yang Z, Liu Y, Li M, Wang J, Zhang J, Chen B, Liu J, Zhang Y, Song J, Zhou X.   +14 more
europepmc   +1 more source

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

Morphometric similarity network-based graph convolutional networks for schizophrenia classification. [PDF]

Sci Rep
Park HW, Lee WH.
europepmc   +1 more source