Results 111 to 120 of about 30,985 (264)

Prediction Analysis of Greeting Gestures Based on Recurrent Neural Networks

JOIV: International Journal on Informatics Visualization
Human activity recognition, such as rehabilitation, sports, human behavior, etc., is developing rapidly. A Recurrent Neural Network (RNN) is a practical approach to human activity recognition research and sequential data.
Angga Wibowo, - Kurnianingsih, Eri Sato-Shimokawara   +2 more
doaj   +1 more source

Paradoxical noise preference in RNNs

CoRR
In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning. The expectation is that removing the noise at test time should preserve or improve performance.
Noah Eckstein, Manoj Srinivasan
openaire   +2 more sources

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
wiley   +1 more source

Сучасні методи та засоби роботи з часовими рядами

Nauka ta progres transportu
Мета. Провести структурований аналіз та класифікацію сучасних методів і моделей, що застосовуються для роботи з часовими рядами різної природи. При цьому була приділена увага не тільки типовими ознаками та родом обчислень, а й виділенням предметної ...
A. A. Zhadan, V. I. Shynkarenko
doaj   +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

An Optimized Hybrid Deep Learning Approach for Accurate Fruit Image Classification

JOIV: International Journal on Informatics Visualization
The field of fruit classification in computer and machine vision is growing rapidly. However, numerous deep learning approaches have been introduced for image classification, but they often encounter challenges that must be addressed.
Hasanain H. Razzaq, Laith F. M. H. Al-Rammahi, Asraa Mounaf Almousawy, Muhammad Zulqarnain   +3 more
doaj   +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

Feasibility of Wind‐Powered Green Hydrogen Production via a Hybrid Graph Neural Network‐Transformer Forecasting Model

Energy Science &Engineering, EarlyView.
ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes
Iman Baghaei, Mojtaba Mirhosseini, Alireza Zahedi   +2 more
wiley   +1 more source

Energy Consumption and CO2 Emissions Forecasting of Transport Sector Using Machine Learning

Energy Science &Engineering, EarlyView.
The transport sector accounts for approximately one‐quarter of Iran's final energy consumption. The energy demand in this sector has the least variation, with petroleum products accounting for more than 85% of the demand. Furthermore, the accelerated growth of energy consumption and the sector's reliance on fossil fuels, which are the main cause of ...
Amir Hossein Akbari, Seyyed Mohammad Hashemi, Meisam Farajollahi   +2 more
wiley   +1 more source