Results 191 to 200 of about 4,069,375 (260)

GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ALZHEIMER'S DISEASE CLASSIFICATION. [PDF]

Proc IEEE Int Symp Biomed Imaging, 2019
Song TA, Roy Chowdhury S, Yang F, Jacobs H, El Fakhri G, Li Q, Johnson K, Dutta J.   +7 more
europepmc   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks. [PDF]

J Transl Med
Nguyen TH, Doan NNT, Tran TH, Huynh LAK, Doan PL, Nguyen THH, Nguyen VTC, Nguyen GTH, Nguyen HN, Giang H, Tran LS, Phan MD.   +11 more
europepmc   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Non-convolutional Graph Neural Networks

Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation.
Cho, Kyunghyun, Wang, Yuanqing
core  

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

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

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks. [PDF]

Front Neurosci, 2019
Marzullo A, Kocevar G, Stamile C, Durand-Dubief F, Terracina G, Calimeri F, Sappey-Marinier D.   +6 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

Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks. [PDF]

Sci Rep, 2019
Spier N, Nekolla S, Rupprecht C, Mustafa M, Navab N, Baust M.   +5 more
europepmc   +1 more source