Results 121 to 130 of about 176,660 (282)

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 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

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

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

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

Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm

Energy Science &Engineering, EarlyView.
Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson, Sivarani Thankamony Saradha Devi, Kumar Chandrasekaran, Sourav Barua   +3 more
wiley   +1 more source

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

Optimizing Electric Vehicle Charging Scheduling With Deep Q Networks and Long Short‐Term Memory‐Based Electricity and Battery State of Charge Prediction

Energy Science &Engineering, EarlyView.
Schematic diagram showing the proposed approach for EV charging/discharging. ABSTRACT The number of electric vehicles (EVs) on the road is rising as a result of recent advancements in EV technology, and EVs are important to the smart grid economy. Demand response schemes involving electric vehicles have the potential to dramatically reduce the cost of ...
F. Zonuntluanga, Subhasish Deb, Kshetrimayum Robert Singh, Subir Datta, Ahmet Onen, Taha Selim Ustun, Jaesung Jung   +6 more
wiley   +1 more source

PREDICTING COVID-19 TRENDS: A DEEP DIVE INTO TIME- DEPENDENT SIRSD WITH DEEP LEARNING TECHNIQUE

Malaysian Journal of Computing
he COVID-19 pandemic, also known as Coronavirus Disease 2019, has affected over 700 million people globally, resulting in approximately 7 million deaths. Research has proposed multiple mathematical models to institute a disease transmission framework and
Abdul Basit, Jasni Mohamad Zain, Abdul Kadir Jumaat, Nur’Izzati Hamdan, Hafiza Zoya Mojahid   +4 more
doaj   +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