Results 251 to 260 of about 119,617 (348)

Use of Automation Technologies and Data Mining in Speech Recognition for Autism

open access: yesBrain and Behavior, Volume 16, Issue 2, February 2026.
Pipeline analyzes clinical and naturalistic speech using LENA, wav2vec 2.0, and foundation‐model ASR (Whisper) to enable scalable ASD detection and severity estimation. Future work integrates benchmarking, privacy‐preserving collaboration (federated learning), and explainable, edge‐ready AI for clinically credible assessment and longitudinal monitoring.
Rongjie Mao, Yuncheng Zhu
wiley   +1 more source

Dimer models and conformal structures

open access: yesCommunications on Pure and Applied Mathematics, Volume 79, Issue 2, Page 340-446, February 2026.
Abstract Dimer models have been the focus of intense research efforts over the last years. Our paper grew out of an effort to develop new methods to study minimizers or the asymptotic height functions of general dimer models and the geometry of their frozen boundaries.
Kari Astala   +3 more
wiley   +1 more source

Epitranscriptomics as a Candidate Universal Modulator of Dormancy Transitions

open access: yesEcology and Evolution, Volume 16, Issue 2, February 2026.
Dormancy is presented as a conserved, reversible survival program in which epitranscriptomic RNA modifications are proposed to provide a rapid, energy‐efficient layer that establishes, maintains, and terminates the state by modulating mRNA stability, translation, and localization.
Ehsan Pashay Ahi
wiley   +1 more source

Artificial Intelligence in Multimedia Content Generation: A Review of Audio and Video Synthesis Techniques

open access: yesJournal of the Society for Information Display, Volume 34, Issue 2, Page 49-67, February 2026.
Modern AI systems can now synthesize coherent multimedia experiences, generating video and audio directly from text prompts. These unified frameworks represent a rapid shift toward controllable and synchronized content creation. From early neural architectures to transformer and diffusion paradigms, this paper contextualizes the ongoing evolution of ...
Charles Ding, Rohan Bhowmik
wiley   +1 more source

Forecasting Local Ionospheric Parameters Using Transformers

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago   +4 more
wiley   +1 more source

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