Results 81 to 90 of about 156,045 (186)

AFAgarap/gru-svm v0.3.11-alpha

, 2017
Code implementation of "A Neural Network Architecture Combining Deep Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection" (https://arxiv.org/abs/1709.03082)
Abien Fred Agarap
core   +1 more source

Predicting Power Consumption Using Deep Learning with Stationary Wavelet

Forecasting
Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and ...
Majdi Frikha, Khaled Taouil, Ahmed Fakhfakh, Faouzi Derbel   +3 more
doaj   +1 more source

State of the art in energy consumption using deep learning models

AIP Advances
In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption.
Shikha Yadav, Nadjem Bailek, Prity Kumari, Alina Cristina Nuţă, Aynur Yonar, Thomas Plocoste, Soumik Ray, Binita Kumari, Mostafa Abotaleb, Amal H. Alharbi, Doaa Sami Khafaga, El-Sayed M. El-Kenawy   +11 more
doaj   +1 more source

Retraction Notice: Deep Recurrent Architectures for EEG-Based Classification of Major Psychiatric Disorders: A Comparative Study of LSTM, BiLSTM, GRU, BiGRU, and Their Hybrids

IEEE Access
Bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ) are serious mental disorders that affect millions of people worldwide. Due to the overlapping clinical symptoms, early and accurate diagnosis of these disorders is difficult ...
Osman Cetin, Hamdi Melih Saraoglu
doaj   +1 more source

DAB-GRU: A Dynamic Attention-Based Bidirectional Network for Anomaly Detection of On-Board Time-Series Data in High-Speed Rail

IEEE Access
Time-series data from the Chinese Train Control System (CTCS) exhibits complex multidimensional correlations. Traditional statistical and machine learning methods are limited by local statistical features, failing to capture the underlying associative ...
Wenbing Zhu, Ming Xu, Zhiyang Yu, Yang Wang, Xinjun Gao, Xiaoming Chen   +5 more
doaj   +1 more source

Fatigue Detection from 3D Motion Capture Data Using a Bidirectional GRU with Attention

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures.
Xueyi Liu, Ziyang Wang, Yikang Wang
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Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU

Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy ...
Aodong Tian, Xintai Yuan, Jens Wickert, Wei Liu, Zihui Lin, Yuan Hu   +5 more
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Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels

Scientific Reports
To tackle the challenge of discrete and complex monitoring data generated during high-speed rail tunnel construction, this study proposes a hybrid deep learning model for deformation forecasting.
Zeping Yang, Zhikai Cheng, Da Wu
doaj   +1 more source

Yet another bidirectional algorithm for shortest paths [PDF]

For finding a shortest path in a network the bidirectional~A* algorithm is a widely known algorithm. An A* instance requires a heuristic estimate, a real-valued function on the set of nodes.
Post, H., Pijls, W.H.L.M.
core  

Image1_An ensemble model for short-term wind power prediction based on EEMD-GRU-MC.TIF

As a kind of clean and renewable energy, wind power is of great significance for alleviating energy crisis and environmental pollution. However, the strong randomness and large volatility of wind power bring great challenges to the dispatching and safe ...
Peilin Wang (4448401), Chengguo Su (14654342), Chaoyu Guo (17829953), Li Li (14993), Wenlin Yuan (46983)   +4 more
core   +1 more source