Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Advanced Intelligent Discovery, EarlyView.Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs
Advanced Intelligent Discovery, EarlyView.Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal +3 more
wiley +1 more source
Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks. [PDF]
Front Neurosci, 2019 Marzullo A +6 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks. [PDF]
Sci Rep, 2019 Spier N +5 more
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks. [PDF]
BMC Bioinformatics, 2019 Eguchi R +7 more
europepmc +1 more source
BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models
Advanced Intelligent Systems, EarlyView.This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan +7 more
wiley +1 more source
Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging. [PDF]
Uncertain Safe Util Machine Learn Med Imaging Clin Image Based Proced (2019), 2019 Yin S +9 more
europepmc +1 more source