Results 41 to 50 of about 276,034 (269)

Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification

Italian National Conference on Sensors
Brain–computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements.
Mouna Bouchane, Wei Guo, Shuojin Yang
semanticscholar   +1 more source

Spectral Decomposition of Chemical Semantics for Activity Cliffs‐Aware Molecular Property Prediction

Advanced Science, EarlyView.
PrismNet mimics chemical intuition by functioning as a computational prism, refracting molecular graphs into complementary semantic views and spectral frequencies. This dual‐decomposition strategy effectively captures both global topologies and subtle “activity cliff” perturbations.
Chaoyang Xie, Junhu Xu, Guangyi Huang, Shihang Wang, Mutian He, Xinyu Dong, Huiyang Hong, Xiaojun Yao, Qi Wang, Yuquan Li   +9 more
wiley   +1 more source

A Study of Hybrid Renewable Energy Production Scenarios Using a Long Short-Term Memory Method. A Case Study of Göksun

Elektronika ir Elektrotechnika
The global demand for energy has increased exponentially over the years. To reduce the dominance of fossil fuels in energy production, there has been a shift towards energy production models based on renewable sources.
Habibe Karayigit, Aykan Bolukbasi, Kadir Abaci, Ali Akdagli   +3 more
doaj   +1 more source

دراسة إحصائية مقارنة للتنبؤ بإيرادات الموارد النفطية في جمهورية مصر العربية باستخدام نماذج الشبكات العصبية المتكررة [PDF]

Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗ
ملخص البحثيهدف هذا البحث إلى مقارنة كفاءة ودقة التنبؤ بإيرادات الموارد النفطية (ORR) باستخدام نماذج الشبكات العصبية المتكررة (RNN) مثل LSTM وGRU، بالإضافة إلى نماذج السلاسل الزمنية متعددة المتغيرات.
AHMED MOHAMED MOHEY ELDEEN AL HOSAFY
doaj   +1 more source

Diagonal RNNs in Symbolic Music Modeling

, 2017
In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of
Smaragdis, Paris, Subakan, Y. Cem
core   +1 more source

A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

IEEE Access, 2020
Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial Neural Networks (ANNs), Extreme Learning Machine (ELM ...
M. Sajjad, Z. Khan, Amin Ullah, Tanveer Hussain, Waseem Ullah, Mi Young Lee, S. Baik   +6 more
semanticscholar   +1 more source

AI‐Based D‐Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug‐Resistant Bacterial Infection

Advanced Science, EarlyView.
This study constructed the first D‐amino acid antimicrobial peptide dataset and developed an AI model for efficient screening of substitution sites, with 80% of candidate peptides showing enhanced activity. The lead peptide dR2‐1 demonstrated potent antimicrobial activity in vitro and in vivo, high stability, and low toxicity.
Yinuo Zhao, Qingzhou Kong, Haifan Gong, Lixiang Li, Jialu Fu, Boyao Wan, Peizhu Wang, Xiaojuan Li, Yue Wang, Jinghui Zhang, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +14 more
wiley   +1 more source

Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks

, 2019
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their ...
Bahrak, Behnam, Mahini, Hamid, Nejadettehad, Alireza   +2 more
core   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis

Applied Sciences, 2023
This paper proposes a novel hybrid model for sentiment analysis. The model leverages the strengths of both the Transformer model, represented by the Robustly Optimized BERT Pretraining Approach (RoBERTa), and the Recurrent Neural Network, represented by ...
Kian Long Tan, C. Lee, K. Lim
semanticscholar   +1 more source