LSTM: A Search Space Odyssey

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893

Untitled work

(2012) · Cited by 8,009

Practical Bayesian Optimization of Machine Learning Algorithms

Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370

Long-term recurrent convolutional networks for visual recognition and description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228

Untitled work

(2013) · Cited by 3,740

Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Lee, D., & Kim, K. (2019) · Energies · doi:10.3390/en12020215 · Cited by 160

Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China

Li, L., Jiang, P., Xu, H., Lin, G., Guo, D. M., & Wu, H. (2019) · Environmental Science and Pollution Research · doi:10.1007/s11356-019-05116-y · Cited by 163

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Qiu, C., Mou, L., Schmitt, M., & Zhu, X. X. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.05.004 · Cited by 160

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

Zhi, W., Ouyang, W., Shen, C., & Li, L. (2023) · Nature Water · doi:10.1038/s44221-023-00038-z · Cited by 173

Analysis of different RNN autoencoder variants for time series classification and machine prognostics

Yu, W., Kim, I. Y., & Mechefske, C. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107322 · Cited by 166

Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning

Andrew, W., Greatwood, C. M., & Burghardt, T. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.336 · Cited by 161

Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Fekri, M. N., Ghosh, A. M., & Grolinger, K. (2019) · Energies · doi:10.3390/en13010130 · Cited by 166

Prediction of melt pool temperature in directed energy deposition using machine learning

Zhang, Z., Liu, Z., & Wu, D. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101692 · Cited by 163

A Scalable Privacy-Preserving Multi-Agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading

Ye, Y., Tang, Y. P., Wang, H., Zhang, X., & Štrbac, G. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3103917 · Cited by 156

Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157

A deep-learning-based computer vision solution for construction vehicle detection

Arabi, S., Haghighat, A. K., & Sharma, A. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12530 · Cited by 177

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Li, Y., Harfiya, L. N., Purwandari, K., & Lin, Y. (2020) · Sensors · doi:10.3390/s20195606 · Cited by 165

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

Zhang, Q., Barri, K., Babanajad, S. K., & Alavi, A. H. (2020) · Engineering · doi:10.1016/j.eng.2020.07.026 · Cited by 163

Applications of deep learning for phishing detection: a systematic literature review

Catal, C., Giray, G., Teki̇nerdoğan, B., Kumar, S., & Shukla, S. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01672-x · Cited by 192

Neural Survival Recommender

Jing, H., & Smola, A. J. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018719 · Cited by 161

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

Wang, Y., Velswamy, K., & Huang, B. (2017) · Processes · doi:10.3390/pr5030046 · Cited by 157

Recurrent Neural Networks

Caterini, A. L., & Chang, D. E. (2018) · SpringerBriefs in computer science · doi:10.1007/978-3-319-75304-1_5 · Cited by 156

Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations

Karimian, H., Li, Q., Wu, C., Qi, Y., Mo, Y., Chen, G., Zhang, X., & Sachdeva, S. (2019) · Aerosol and Air Quality Research · doi:10.4209/aaqr.2018.12.0450 · Cited by 159

Predicting Alzheimer’s Disease Using LSTM

Hong, X., Lin, R., Yang, C., Zeng, N., Cai, C., Gou, J., & Yang, J. Y. (2019) · IEEE Access · doi:10.1109/access.2019.2919385 · Cited by 160

Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges

Zhou, J., Huang, J. X., Chen, Q., Hu, Q., Wang, T., & He, L. (2019) · IEEE Access · doi:10.1109/access.2019.2920075 · Cited by 157

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Górriz, J. M., Moosaei, H., Khosravi, A., Nahavandi, S., Chofreh, A. G., Goni, F. A., et al. (2021) · Results in Physics · doi:10.1016/j.rinp.2021.104495 · Cited by 152

Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model

Ding, N., Ma, H., Gao, H., Ma, Y., & Tan, G. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106458 · Cited by 150

Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels

Bai, Q., Wang, J., Zhang, Y., & Song, J. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2943455 · Cited by 155

Deep learning reservoir porosity prediction based on multilayer long short-term memory network

Chen, W., Yang, L., Zha, B., Zhang, M., & Chen, Y. (2020) · Geophysics · doi:10.1190/geo2019-0261.1 · Cited by 150

Re$^3$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

Gordon, D., Farhadi, A., & Fox, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2792152 · Cited by 169

Long short-term memory recurrent neural network architectures for Urdu acoustic modeling

Zia, T., & Zahid, U. (2018) · International Journal of Speech Technology · doi:10.1007/s10772-018-09573-7 · Cited by 155

A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

Yu, L., Qu, J., Gao, F., & Tian, Y. (2019) · Shock and Vibration · doi:10.1155/2019/2756284 · Cited by 153

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Wilson, G., Doppa, J. R., & Cook, D. J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403228 · Cited by 149

Speech emotion recognition using recurrent neural networks with directional self-attention

Li, D., Liu, J., Yang, Z., Sun, L., & Wang, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114683 · Cited by 154

Code smell detection by deep direct-learning and transfer-learning

Sharma, T., Efstathiou, V., Λουρίδας, Π., & Spinellis, D. D. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110936 · Cited by 167

MIMO Channel Information Feedback Using Deep Recurrent Network

Lu, C., Xu, W., Shen, H., Zhu, J., & Wang, K. (2018) · IEEE Communications Letters · doi:10.1109/lcomm.2018.2882829 · Cited by 149

A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity

Gao, D., Zhu, Y., Ren, Z., Yan, K. L., & Kang, W. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107413 · Cited by 149

BERT: a sentiment analysis odyssey

Alaparthi, S., & Mishra, M. (2021) · Journal of Marketing Analytics · doi:10.1057/s41270-021-00109-8 · Cited by 313

Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

Wu, X., Wang, S., Huang, W., Dong, Y., Wang, Z., & Huang, W. (2023) · Nature Communications · doi:10.1038/s41467-023-36205-9 · Cited by 147

Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system

Liu, X., Wang, Y., Wang, X., Xu, H., Li, C., & Xin, X. (2021) · Optics Express · doi:10.1364/oe.416672 · Cited by 153

Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning

Xu, R., Cao, J., Feng, G., Luo, J., Feng, Q., Ni, B., & Fang, F. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.132893 · Cited by 146

Change detection using deep learning approach with object-based image analysis

Liu, T., Yang, H. L., & Lunga, D. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112308 · Cited by 153

The LSTM-Based Advantage Actor-Critic Learning for Resource Management in Network Slicing With User Mobility

Li, R., Wang, C., Zhao, Z., Guo, R., & Zhang, H. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.3001227 · Cited by 145

A federated learning-based zero trust intrusion detection system for Internet of Things

Javeed, D., Saeed, M. S., Adil, M., Kumar, P., & Jolfaei, A. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103540 · Cited by 169

Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks

Zazo, R., Lozano-Díez, A., Gónzalez-Domínguez, J., Toledano, D. T., & González-Rodríguez, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146917 · Cited by 144

Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation

Zhang, Z., Qin, H., Liu, Y., Yao, L., Yu, X. J., Lu, J., Jiang, Z., & Feng, Z.-K. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.06.024 · Cited by 145

Automatically learning construction injury precursors from text

Baker, H., Hallowell, M. R., & Tixier, A. J. ‐. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103145 · Cited by 155

Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System

Wen, S., Wang, Y., Tang, Y. K., Xu, Y. H., Li, P., & Zhao, T. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2910416 · Cited by 145

LSTM Pose Machines

Luo, Y. G., Ren, J., Wang, Z., Sun, W., Pan, J., Liu, J., Pang, J., & Lin, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00546 · Cited by 158

Fault Detection of the Harmonic Reducer Based on CNN-LSTM With a Novel Denoising Algorithm

Zhi, Z., Liu, L., Liu, D., & Hu, C. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3137992 · Cited by 145

Hierarchical attention networks for information extraction from cancer pathology reports

Gao, S. B., Young, M. T., Qiu, J. X., Yoon, H., Christian, J. B., Fearn, P., Tourassi, G. D., & Ramanthan, A. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx131 · Cited by 152

Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages

Habler, E., & Shabtai, A. (2018) · Computers & Security · doi:10.1016/j.cose.2018.07.004 · Cited by 148

Social media contents based sentiment analysis and prediction system

Yoo, S., Song, J., & Jeong, O. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.055 · Cited by 156

An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring

Guo, J., Yang, L., Bie, R., Yu, J., Gao, Y., Shen, Y., & Kos, A. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.01.026 · Cited by 147

DWTLSTM for electronic nose signal processing in beef quality monitoring

Wijaya, D. R., Sarno, R., & Zulaika, E. (2020) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2020.128931 · Cited by 149

Hierarchical Neural Network Generative Models for Movie Dialogues.Preprint

Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2015) · arXiv (Cornell University) · Cited by 143

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Ungurean, L., Micea, M. V., & Carstoiu, G. N. (2020) · International Journal of Energy Research · doi:10.1002/er.5413 · Cited by 140

Hybrid deep neural network using transfer learning for EEG motor imagery decoding

Zhang, R., Zong, Q., Dou, L., Zhao, X., Tang, Y., & Li, Z. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102144 · Cited by 144

Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence: An E-Seq2Seq-Based SCUC Expert System

Yang, N., Yang, C., Wu, L., Shen, X., Jia, J., Li, Z., Chen, D., Zhu, B., & Liu, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3107406 · Cited by 140

Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling

Ghazi, M. M., Nielsen, M., Pai, A., Cardoso, M. J., Marc Modat, A., Sebastien Ourselin, A., & Sørensen, L. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.004 · Cited by 141

Modeling Personalized Item Frequency Information for Next-basket Recommendation

Hu, H., He, X., Gao, J., & Zhang, Z.-L. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401066 · Cited by 150

Multi-disease prediction using LSTM recurrent neural networks

Men, L., Ilk, N., Tang, X., & Liu, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114905 · Cited by 140

Multistep Prediction of Dynamic Systems With Recurrent Neural Networks

Mohajerin, N., & Waslander, S. L. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2891257 · Cited by 150

Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks

Wang, T., Qiu, R. G., & Yu, M. Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-27337-w · Cited by 146

Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder

Huang, Y., Chen, C.-H., & Huang, C.-J. (2019) · IEEE Access · doi:10.1109/access.2019.2940769 · Cited by 144

EMG-to-Speech: Direct Generation of Speech From Facial Electromyographic Signals

Janke, M., & Diener, L. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2738568 · Cited by 138

A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting

Fu, W., Wang, K., Tan, J., & Zhang, K. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.112461 · Cited by 140

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

Wu, J., Yılmaz, E., Zhang, M., Li, H., & Tan, K. C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00199 · Cited by 149

Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction

Cui, Z., Ke, R., Pu, Z., Ma, X., & Wang, Y. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102620 · Cited by 149

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2930410 · Cited by 135

The Cloud-edge-based Dynamic Reconfiguration to Service Workflow for Mobile Ecommerce Environments

Gao, H., Huang, W., & Duan, Y. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3391198 · Cited by 136

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

Chen, J., Li, K., Zhang, Z., Li, K., & Yu, P. S. (2021) · ACM Computing Surveys · doi:10.1145/3465398 · Cited by 154

A survey on machine learning models for financial time series forecasting

Tang, Y., Song, Z., Zhu, Y., Yuan, H., Hou, M., Ji, J., Tang, C. C., & Li, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.003 · Cited by 149

Untitled work

Peng (2018) · doi:10.1145/3214277 · Cited by 134

CNN–LSTM–AM: A power prediction model for offshore wind turbines

Sun, Y., Zhou, Q., Sun, L. Q., Sun, L., Kang, J., & Li, H. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117598 · Cited by 144

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Gui, G., Zhou, Z., Wang, J., Liu, F., & Sun, J. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2981959 · Cited by 133

Cryptocurrency malware hunting: A deep Recurrent Neural Network approach

Yazdinejad, A., HaddadPajouh, H., Dehghantanha, A., Parizi, R. M., Srivastava, G., & Chen, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106630 · Cited by 135

Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data

Brahma, B., & Wadhvani, R. (2020) · Symmetry · doi:10.3390/sym12111830 · Cited by 140

Deep Time-Series Clustering: A Review

Alqahtani, A., Ali, M., Xie, X., & Jones, M. W. (2021) · Electronics · doi:10.3390/electronics10233001 · Cited by 133

A paradigm shift fully self-powered long-distance wireless sensing solution enabled by discharge-induced displacement current

Wang, H., Wang, J., Yao, K., Fu, J., Xia, X., Zhang, R., Li, J., Xu, G., Wang, L., Yang, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abi6751 · Cited by 132

Recognizing Surgical Activities with Recurrent Neural Networks

DiPietro, R. S., Lea, C., Malpani, A., Ahmidi, N., Vedula, S. S., Lee, G. I., Lee, M. R., & Hager, G. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_64 · Cited by 137

Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment With LSTM-RNN

Wang, Q., Bu, S., & He, Z. Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2966033 · Cited by 139

Prostate Cancer Detection Using Deep Learning and Traditional Techniques

Iqbal, S., Siddiqui, G. F., Rehman, A. U., Hussain, L., Saba, T., Tariq, U., & Abbasi, A. A. (2021) · IEEE Access · doi:10.1109/access.2021.3057654 · Cited by 131

AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Yang, C., Wu, C.-H., Shao, J.-C., Wang, Y., & Hsieh, C.-M. (2022) · IEEE Access · doi:10.1109/access.2022.3154812 · Cited by 142

Intelligent Asset Allocation via Market Sentiment Views

Xing, F., Cambria, E., & Welsch, R. E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2866727 · Cited by 131

Exploring Trajectory Prediction Through Machine Learning Methods

Wang, C., Ma, L., Li, R., Durrani, T. S., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2929430 · Cited by 130

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms

Guo, Q., Chen, S., Xie, X., Ma, L., Hu, Q., Liu, H., Liu, Y., Zhao, J., & Li, X. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00080 · Cited by 140

Model-Free Quantum Control with Reinforcement Learning

Sivak, V. V., Eickbusch, A., Liu, H., Royer, B., Tsioutsios, I., & Devoret, M. H. (2022) · Physical Review X · doi:10.1103/physrevx.12.011059 · Cited by 130

A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

Karim, F., Armin, M. A., Ahmedt‐Aristizabal, D., Tychsen-Smith, L., & Petersson, L. (2023) · Water · doi:10.3390/w15030566 · Cited by 146

Dual-modality Seq2Seq Network for Audio-visual Event Localization

Lin, Y.-B., Li, Y.-J., & Wang, Y.-C. F. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683226 · Cited by 132

Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network

Zhang, B., Li, J., Quan, L., Chen, Y., & Lü, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.013 · Cited by 128

A Multi-Patient Data-Driven Approach to Blood Glucose Prediction

Aliberti, A., Pupillo, I., Terna, S., Macii, E., Cataldo, S. D., Patti, E., & Acquaviva, A. (2019) · IEEE Access · doi:10.1109/access.2019.2919184 · Cited by 134

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

Heider, Y., Wang, K., & Sun, W. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.112875 · Cited by 131

Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

Marani, M., Zeinali, M., Songméné, V., & Mechefske, C. K. (2021) · Measurement · doi:10.1016/j.measurement.2021.109329 · Cited by 129

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects

Güçlü, U., & van Gerven, M. A. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.036 · Cited by 129

Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs

Abdel‐Nasser, M., Mahmoud, K., & Lehtonen, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2996235 · Cited by 128

A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset

Jony, A. I., & Arnob, A. K. B. (2024) · Journal of Edge Computing · doi:10.55056/jec.648 · Cited by 153

LSTM and GRU type recurrent neural networks in model predictive control: A Review

Ławryńczuk, M., & Zarzycki, K. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129712 · Cited by 146

On Network Design Spaces for Visual Recognition

Radosavovic, I., Johnson, J., Xie, S., Lo, W.-Y., & Dollar, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00197 · Cited by 127

A new financial data forecasting model using genetic algorithm and long short-term memory network

Huang, Y., Gao, Y., Gan, Y., & Ye, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.086 · Cited by 135

CDLSTM: A Novel Model for Climate Change Forecasting

Haq, M. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.023059 · Cited by 127

Dynamic State Estimation for the Advanced Brake System of Electric Vehicles by Using Deep Recurrent Neural Networks

Xing, Y., & Lv, C. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2952807 · Cited by 130

Applications of deep learning in congestion detection, prediction and alleviation: A survey

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A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal

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