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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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Speech recognition with deep recurrent neural networks

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Framewise phoneme classification with bidirectional LSTM and other neural network architectures

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An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China

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Multiple-target deep learning for LSTM-RNN based speech enhancement

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Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model

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Portfolio formation with preselection using deep learning from long-term financial data

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Learning representations for the early detection of sepsis with deep neural networks

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Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles

Hong, J., Wang, Z., Chen, W., Wang, L. Y., & Qu, C. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101459 · Cited by 225

The building blocks of a brain-inspired computer

Kendall, J. D., & Kumar, S. (2020) · Applied Physics Reviews · doi:10.1063/1.5129306 · Cited by 220

A deep learning model for air quality prediction in smart cities

Kök, İ., Şimşek, M. U., & Özdemi̇r, S. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258144 · Cited by 221

Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing

Shao, H., Cheng, J., Hongkai, J., Yang, Y., & Wu, Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105022 · Cited by 216

Understanding Hidden Memories of Recurrent Neural Networks

Ming, Y., Cao, S., Zhang, R., Li, Z., Chen, Y., Song, Y., & Qu, H. (2017) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2017.8585721 · Cited by 229

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

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Improved Prediction of Harmful Algal Blooms in Four Major South Korea’s Rivers Using Deep Learning Models

Lee, S. M., & Lee, D.-H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15071322 · Cited by 215

Deep Adaptive Input Normalization for Time Series Forecasting

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944933 · Cited by 211

CNN and RNN based payload classification methods for attack detection

Liu, H., Lang, B., Liu, M., & Yan, H. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.08.036 · Cited by 211

Recurrent inception convolution neural network for multi short-term load forecasting

Kim, J., Moon, J., Hwang, E., & Kang, P. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.04.034 · Cited by 212

UWB Indoor Localization Using Deep Learning LSTM Networks

Poulose, A., & Han, D. S. (2020) · Applied Sciences · doi:10.3390/app10186290 · Cited by 215

Time2Vec: Learning a Vector Representation of Time

Kazemi, S. M., Goel, R., Eghbali, S., Ramanan, J., Sahota, J., Thakur, S., Wu, S., Smyth, C., Poupart, P., & Brubaker, M. (2019) · arXiv · doi:10.48550/arxiv.1907.05321 · Cited by 206

Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Asanjan, A. A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., & Peng, Q. (2018) · Journal of Geophysical Research Atmospheres · doi:10.1029/2018jd028375 · Cited by 217

Self-Supervised Pretraining of Transformers for Satellite Image Time Series Classification

Yuan, Y., & Lin, L. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3036602 · Cited by 224

Deep learning-based tensile strength prediction in fused deposition modeling

Zhang, J., Wang, P. E., & Gao, R. X. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.011 · Cited by 208

Long Short-Term Memory Recurrent Neural Network for Automatic Speech Recognition

Oruh, J. N., Viriri, S., & Adegun, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3159339 · Cited by 225

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

Zhang, G., Davoodnia, V., Sepas‐Moghaddam, A., Zhang, Y., & Etemad, A. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2956998 · Cited by 203

Audio Word2Vec: Unsupervised Learning of Audio Segment Representations Using Sequence-to-Sequence Autoencoder

Chung, Y.-A., Wu, C.-C., Shen, C.-H., Lee, H.-Y., & Lee, L.-S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-82 · Cited by 215

Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network

Wen, J., Yang, J., Jiang, B., Song, H. H., & Wang, H. H. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.3012393 · Cited by 201

Visual Grounding via Accumulated Attention

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A survey of swarm and evolutionary computing approaches for deep learning

Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

Filho, H. C. D. C., Júnior, O. A. D. C., de Carvalho, O. L. F., de Bem, P. P., de Moura, R. D. S., de Albuquerque, A. O., Silva, C. R., Ferreira, P. H. G., Guimarães, R. F., & Gomes, R. A. T. (2020) · Remote Sensing · doi:10.3390/rs12162655 · Cited by 215

A survey on applications of deep learning in microscopy image analysis

Liu, Z., Jin, L., Chen, J., Fang, Q., Ablameyko, S., Yin, Z., & Xu, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104523 · Cited by 218

Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network

Hinchi, A. Z., & Tkiouat, M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.106 · Cited by 196

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

Galván, E., & Mooney, P. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3067574 · Cited by 209

Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

Yıldırım, D. C., Toroslu, İ. H., & Fiore, U. (2021) · Financial Innovation · doi:10.1186/s40854-020-00220-2 · Cited by 215

Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach

Hương, T. T., Bac, T. P., Long, D. M., Luong, T. D., Dan, N. M., Quang, L. A., Cong, L. T., Thang, B. D., & Tran, K. P. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103509 · Cited by 201

Recurrent Face Aging

Wang, W., Cui, Z., Yan, Y., Feng, J., Yan, S., Shu, X., & Sebe, N. (2016) · 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.2016.261 · Cited by 188

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Bikmukhametov, T., & Jäschke, J. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106834 · Cited by 202

Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

Freire, P. J., Osadchuk, Y., Spinnler, B., Napoli, A., Schairer, W., Costa, N. M. S., Prilepsky, J. E., & Turitsyn, S. K. (2021) · Aston Publications Explorer (Aston University) · Cited by 193

Short-term electric vehicle charging demand prediction: A deep learning approach

Wang, S., Zhuge, C., Shao, C., Wang, P., Yang, X., & Wang, S. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121032 · Cited by 197

Data-Driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-Enabled Maritime IoT Systems

Liu, R. W., Nie, J., Garg, S., Xiong, Z., Zhang, Y., & Hossain, M. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028743 · Cited by 196

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 206

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Park, P., Marco, P. D., Shin, H., & Bang, J. (2019) · Sensors · doi:10.3390/s19214612 · Cited by 198

A Supervised Bidirectional Long Short-Term Memory Network for Data-Driven Dynamic Soft Sensor Modeling

Lui, C. F., Liu, Y., & Xie, M. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3152856 · Cited by 188

Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification

Xu, J., Chen, D., Qiu, X., & Huang, X. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1172 · Cited by 201

Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models

Kouadri, S., Pande, C. B., Panneerselvam, B., Moharir, K. N., & Elbeltagi, A. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-17084-3 · Cited by 189

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Hao, Y., Huang, X., Dong, M., & Xu, B. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.007 · Cited by 196

Automatic Modulation Classification: A Deep Architecture Survey

Huynh‐The, T., Pham, Q., Nguyen, T.-V., Nguyen, T. T., Ruby, R., Zeng, M., & Kim, D. (2021) · IEEE Access · doi:10.1109/access.2021.3120419 · Cited by 200

Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

Challita, U., Dong, L., & Saad, W. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2829773 · Cited by 223

FPGA-based accelerator for long short-term memory recurrent neural networks

Guan, Y., Yuan, Z., Sun, G., & Cong, J. (2017) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2017.7858394 · Cited by 186

A deep learning model for short-term power load and probability density forecasting

Guo, Z., Zhou, K., Zhang, X., & Yang, S. (2018) · Energy · doi:10.1016/j.energy.2018.07.090 · Cited by 192

An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Livieris, I. E., Kiriakidou, N., Stavroyiannis, S., & Pintelas, P. (2021) · Electronics · doi:10.3390/electronics10030287 · Cited by 183

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti, M., & Merabet, A. (2021) · Energies · doi:10.3390/en14206782 · Cited by 203

An overview and comparative analysis of Recurrent Neural Networks for Short Term Load ForecastingPreprint

Bianchi, F. M., Maiorino, E., Kampffmeyer, M., Rizzi, A., & Jenssen, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.04378 · Cited by 206

Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization

Coskun, H., Achilles, F., DiPietro, R. S., Navab, N., & Tombari, F. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.589 · Cited by 194

On extended long short-term memory and dependent bidirectional recurrent neural network

Su, Y., & Kuo, C. ‐. J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.044 · Cited by 198

Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction

Li, Y., Wu, H., & Liu, H. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.04.082 · Cited by 182

Research on Text Classification Based on CNN and LSTM

Luan, Y., & Lin, S. (2019) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica.2019.8873454 · Cited by 191

Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

Abbasimehr, H., & Paki, R. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110511 · Cited by 184

Evolving deep neural networks

Miikkulainen, R. P., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96104-2.00002-6 · Cited by 201

Deep learning incorporating biologically inspired neural dynamics and in-memory computing

Woźniak, S., Pantazi, A., Bohnstingl, T., & Eleftheriou, E. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0187-0 · Cited by 186

Intelligent task prediction and computation offloading based on mobile-edge cloud computing

Miao, Y., Wu, G., Li, M., Ghoneim, A., Al-Rakhami, M. S., & Hossain, M. S. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.035 · Cited by 179

A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data

Yuan, X., Qi, S., Wang, Y., & Xia, H. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104614 · Cited by 179

Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch

Kim, Y., & Panda, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.773954 · Cited by 189

Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

Jiang, H., Li, X., Shao, H., & Zhao, K. (2018) · Measurement Science and Technology · doi:10.1088/1361-6501/aab945 · Cited by 181

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Xie, H., Zhang, L., & Lim, C. P. (2020) · IEEE Access · doi:10.1109/access.2020.3021527 · Cited by 180

Jet flavor classification in high-energy physics with deep neural networks

Guest, D., Collado, J., Baldi, P., Hsu, S.-C., Urban, G., & Whiteson, D. (2016) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.94.112002 · Cited by 177

Deep Learning approach for sentiment analysis of short texts

Hassan, A., & Mahmood, A. (2017) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2017.7942788 · Cited by 185

Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis

Zhao, W. Z., Guan, Z., Chen, L., He, X., Cai, D., Wang, B., & Wang, Q. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2756658 · Cited by 183

Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types

Oh, S. L., Ng, E. Y. K., Tan, R., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.12.012 · Cited by 179

CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings

Iyer, A., Das, S. S., Teotia, R., Maheshwari, S., & Sharma, R. R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12310-7 · Cited by 179

A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition

Zeyer, A., Doetsch, P., Voigtlaender, P., Schlüter, R., & Ney, H. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952599 · Cited by 185

An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting

Bai, Y., Zeng, B., Li, C., & Zhang, J. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.01.121 · Cited by 176

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Zheng, Z., Yang, Y., Liu, J., Dai, H., & Zhang, Y. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909904 · Cited by 179

Evolutionary convolutional neural networks: An application to handwriting recognition

Baldominos, A., Sáez, Y., & Isasi, P. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.049 · Cited by 178

A stock price prediction method based on deep learning technology

Ji, X., Wang, J., & Yan, Z. (2021) · International Journal of Crowd Science · doi:10.1108/ijcs-05-2020-0012 · Cited by 175

Intelligent phishing detection scheme using deep learning algorithms

Adebowale, M. A., Lwin, K. T., & Hossain, M. A. (2020) · Journal of Enterprise Information Management · doi:10.1108/jeim-01-2020-0036 · Cited by 174

A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

Shenfield, A., & Howarth, M. (2020) · Sensors · doi:10.3390/s20185112 · Cited by 190

Application of LSTM approach for modelling stress–strain behaviour of soil

Zhang, N., Shen, S., Zhou, A., & Jin, Y. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106959 · Cited by 179

Convolutional Neural Networks for Authorship Attribution of Short Texts

Shrestha, P., Sierra, S., Gonzalez, F. A., Montes, M., Rosso, P., & Solorio, T. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2106 · Cited by 172

Predicting online shopping behaviour from clickstream data using deep learning

Koehn, D., Lessmann, S., & Schaal, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113342 · Cited by 186

Language Modeling with Deep Transformers

Irie, K., Zeyer, A., Schlüter, R., & Ney, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2225 · Cited by 181

Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow

Tang, M., Liu, Y., & Durlofsky, L. J. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113636 · Cited by 172

A new crude oil price forecasting model based on variational mode decomposition

Huang, Y., & Deng, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106669 · Cited by 176

Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Liu, S., Jiang, H., Xu, J., Liu, S., & Wang, X. (2021) · 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/cvpr46437.2021.01445 · Cited by 169

A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction

Shi, J., Zhong, J., Zhang, Y., Xiao, B., Xiao, L., & Zheng, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109821 · Cited by 185

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

Krestinskaya, O., Salama, K. N., & James, A. P. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2018.2866510 · Cited by 174

Bidirectional Convolutional LSTM for the Detection of Violence in Videos

Hanson, A., Pnvr, K., Krishnagopal, S., & Davis, L. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_24 · Cited by 172

Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance

Busari, G. A., & Lim, D. H. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107513 · Cited by 179

On the Origin of Deep LearningPreprint

Wang, H., & Raj, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.07800 · Cited by 312

A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment

Li, Z., Li, J., Wang, Y., & Wang, K. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03557-w · Cited by 171

SDN-based real-time urban traffic analysis in VANET environment

Bhatia, J., Dave, R., Bhayani, H., Tanwar, S., & Nayyar, A. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.011 · Cited by 169

Smart Detection of Fire Source in Tunnel Based on the Numerical Database and Artificial Intelligence

Wu, X., Park, Y., Li, A., Huang, X., Xiao, F., & Usmani, A. S. (2020) · Fire Technology · doi:10.1007/s10694-020-00985-z · Cited by 170

Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-ArtPreprint

Braei, M., & Wagner, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00433 · Cited by 310

Path Planning for UAV Ground Target Tracking via Deep Reinforcement Learning

Li, B., & Wu, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2971780 · Cited by 168

Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method

Deng, S., Xiang, Z., Zhao, P., Taheri, J., Gao, H., Yin, J., & Zomaya, A. Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2974875 · Cited by 167

DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition

Aly, S., & Aly, W. (2020) · IEEE Access · doi:10.1109/access.2020.2990699 · Cited by 177

Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning

Lin, G., Zhu, L., Li, J., Zou, X., & Tang, Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106350 · Cited by 166

An architecture for emergency event prediction using LSTM recurrent neural networks

Cortez, B., Carrera, B., Kim, Y.-J., & Jung, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.12.037 · Cited by 173

Impact load identification of nonlinear structures using deep Recurrent Neural Network

Zhou, J., Dong, L., Guan, W., & Yan, J. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106292 · Cited by 167

Deep learning for procedural content generation

Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G. N., & Togelius, J. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05383-8 · Cited by 195

Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

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