Results 131 to 140 of about 5,451,620 (296)

Spiral Recurrent Neural Network for Online Learning

, 2008
. Autonomous, self * sensor networks require sensor nodes with a certain degree of “intelligence”. An elementary component of such an “intelligence ” is the ability to learn online predicting sensor values.
Huaien Gao, Rudolf Sollacher, Hans-peter Kriegel   +2 more
core  

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

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

Predicting Cybersickness in Virtual Reality from Head–Torso Kinematics Using a Hybrid Convolutional–Recurrent Network Model

Computers
Motion sickness (MS) is a prevalent condition that can significantly degrade user comfort and immersion, particularly in virtual reality (VR) environments.
Ala Hag, Houshyar Asadi, Mohammad Reza Chalak Qazani, Thuong Hoang, Ambarish Kulkarni, Stefan Greuter, Saeid Nahavandi   +6 more
doaj   +1 more source

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

CoRR, 2016
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained,
openaire   +3 more sources

MUSE-RNN: a multilayer self-evolving recurrent neural network for data stream classification

, 2019
In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network model for real-time classification of streaming data. Unlike the existing approaches, MUSE-RNN offers special treatment towards capturing temporal aspects of data ...
Das, M., Savitri, S., Zhang, J., Pratama, M.   +3 more
core   +1 more source

Citrus Essential Oils in Food Safety and Health: Advances in AI‐Assisted Authentication, Antimicrobial Applications, Sustainable Extraction, and Regulatory Translation

Food Safety and Health, EarlyView.
Citrus essential oils combine sustainable extraction, AI‐assisted authentication, multi‐omics profiling, antimicrobial activity, and regulatory safety to enhance food quality, detect adulteration, and promote consumer health, and support intelligent, sustainable food systems through innovative industrial applications and circular bioeconomy strategies.
Md. Hassan Bin Nabi, Liza Akter, Yusuf Chowdhury, Mohima Akther Any, Nazmul Islam, Erni Sofia Murtini, Nurul Huda, Wahidu Zzaman   +7 more
wiley   +1 more source

Vehicle classification using a recurrent neural network (RNN)

, 2018
A device can receive GPS data or values for a set of metrics at a set of GPS points that form a GPS track of a vehicle. The device can determine additional values for additional metrics using the GPS data or the values for the set of metrics.
Samuele Salti, Francesco Sambo, Alessandro Lori, Luca Bravi, Leonardo Taccari, Matteo Simoncini   +5 more
core   +1 more source

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Ibrain, Volume 11, Issue 1, Page 32-43, Spring 2025.
Electroencephalogram (EEG) can noninvasive, continuous, and real‐time monitor the state of brain electrical activity, and the monitoring of EEG can reflect changes in the depth of anesthesia (DOA). The development of artificial intelligence can enable anesthesiologists to extract, analyze, and quantify DOA from complex EEG data.
Xiaolan He, Tingting Li, Xiao Wang
wiley   +1 more source

Deciphering transcriptome complexity via long‐read sequencing

iMeta, EarlyView.
Long‐read sequencing is transforming transcriptomics from gene‐level quantification to isoform‐resolved interpretation by directly resolving full‐length transcript structures, transcription in repetitive regions, and linked molecular features. This review provides an end‐to‐end roadmap covering sequencing platforms, library preparation strategies ...
Chuwen Xu, Jia Li, Chenxi Yin, Keying Li, Tian Wang, Shiwen Gao, Yue Yu, Cheng Chang, Ye Wang, Guoqing Tong, Liang Gong, Wen Hu, Guoliang Chai, Yilai Han, Qian Qin, Suoqin Jin, Xiufen Zou, Bo Li, Tianyuan Zhang, Ana Conesa, Yunhao Wang, Dingjie Wang   +21 more
wiley   +1 more source