Results 221 to 230 of about 4,047,749 (292)

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Innovative approaches: Utilizing artificial intelligence for early diagnosis of main neurodegenerative diseases. [PDF]

Caspian J Intern Med
Ebrahimi H, Hejazian E, Eftekhari Z.
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

Eyespots in <i>Automeris</i> Moths Mimic Sympatric Owl Species (Lepidoptera: Saturniidae). [PDF]

Ecol Evol
Skojec C, Godfrey RK, Adams B, Nazrul N, Earl C, McTammany A, Kawahara AY, Shirey V, Umbers KDL.   +8 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

Exploring the impact of artificial intelligence on dental education: Benefits, challenges, and future directions - A systematic review of the literature. [PDF]

J Educ Health Promot
Khoshgoftar Z, Ghaiyoomi S, Alaee A.
europepmc   +1 more source

Designing Wire Mazes for Replicating Natural Echoes to Study Bat Biosonar Function

Advanced Intelligent Systems, EarlyView.
A validated framework combining efficient physical modeling (multiple scattering model) and deep learning is presented to guide wire‐maze design for bat biosonar studies. This approach rapidly generates large datasets to test acoustic distinguishability among wire arrangements.
Chunlin Jia, Gengkai Hu, Boo Cheong Khoo, Rolf Müller   +3 more
wiley   +1 more source

Computational approaches to deep learning-driven EEG analysis for neurological disease diagnosis: a systematic review. [PDF]

Front Neuroinform
H F T, Jacob JE.
europepmc   +1 more source

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

Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors. [PDF]

Sensors (Basel)
Xie D, Yan P, Li Y, Wang Q, Liu J.
europepmc   +1 more source