Results 151 to 160 of about 64,275 (248)

Correction: A new band selection approach integrated with physical reflectance autoencoders and albedo recovery for hyperspectral image classification. [PDF]

Sci Rep
Sangeetha V, Agilandeeswari L.
europepmc   +1 more source

Computationally Driven Advances in Cu‐CNT On‐Chip Interconnect Materials: From First Principles to Machine Learning

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A multiscale framework integrating electronic, mechanical, and thermal analysis with machine learning to optimize carbon nanotube interconnects. As the component dimensions in integrated circuits shrink to extreme scales, the complexity of interconnect systems is increasing significantly, necessitating an urgent and comprehensive upgrade of ...
Changhong Zhang, Jie Liang, Yi Li, Zhenyu Wang, Haoyuan Li, Zhen Yuan, Jiuren Zhou, Rongmei Chen, Wensheng Zhao, Lei Zhang, Hanshen Xin, Jianhua Zhang   +11 more
wiley   +1 more source

A deep ensemble encoder network method for improved polygenic risk score prediction. [PDF]

BioData Min
Ozdemir OB, Chen R, Wu O, Li R.
europepmc   +1 more source

Frontiers in EEG as a tool for the management of pediatric epilepsy: Past, present, and future

Epilepsia Open, EarlyView.
Abstract Electroencephalography (EEG) has evolved into an indispensable tool in pediatric epilepsy, fundamentally transforming the diagnosis, classification, and management of this condition. This review chronicles the historical journey of EEG from its groundbreaking inception to its current pivotal role in delineating distinct pediatric epilepsy ...
Hiroki Nariai
wiley   +1 more source

Autoencoders reveal polyunsaturated fatty acids (PUFA)-Related metabolic signature linked to cancer risk. [PDF]

EBioMedicine
Breeur M, Atkins J, Peruchet-Noray L, Bonheme L, Alcala N, Dossus L, Jenab M, Johansson M, Rinaldi S, Travis RC, Bork C, Dahm CC, Tjønneland A, Olsen A, Naudin S, Park S, Truong T, Katzke V, Le Cornet C, Schulze MB, Prada M, Sacerdote C, Bendinelli B, Agnoli C, Pasanisi F, Gálvez-Navas JM, Guevara M, Heath AK, Yarmolinsky J, Gunter MJ, Ferrari P, Smith-Byrne K, Viallon V.   +32 more
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species. [PDF]

BMC Bioinformatics
Heilmann PG, Grosch E, Frisch M, Herrmann M, Beuch S, Kurra V, Mascher M, Avni R, Oldach K, Röhrs I, Hanemann A, Mehta RR, Reinbrecht C, Serfling A, Stahl A, Stucke M, Abbadi A, Kox T, Zenke-Philippi C.   +18 more
europepmc   +1 more source

Autoencoders lineares e autoencoders não lineares (ReLU)

, 2020
Este trabalho é dedicado ao estudo de autoencoders lineares, onde se destacam as suas ligações com a técnica PCA e com autoencoders não lineares, nomeadamente, usando a função de ativação ReLU. Ao longo desta dissertação, são demonstrados diversos resultados sobre esta temática, através de diversas simplificações e hipóteses adicionais.
openaire   +1 more source

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

MM-WAE: Multimodal Wasserstein Autoencoders for Semi-Supervised Wafer Map Defect Recognition. [PDF]

Micromachines (Basel)
Zhang Y, Sun Q, Liu Z, Zhang DW.
europepmc   +1 more source