A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works

Untitled work

(2016) · Cited by 11,679 · see citing works

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works

Edge computing: Vision and challenges

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works

Internet of Things for Smart Cities

Zanella, A., Bui, N., Castellani, A. P., Vangelista, L., & Zorzi, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2306328 · Cited by 6,385 · see citing works

Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works

Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733 · see citing works

Generative Adversarial Networks

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works

Next generation 5G wireless networks: a comprehensive survey

Agiwal, M., Roy, A., & Saxena, N. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2532458 · Cited by 3,444 · see citing works

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · see citing works

Deep learning with long short-term memory networks for financial market predictions

Fischer, T. G., & Krauß, C. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.11.054 · Cited by 3,000 · see citing works

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793 · see citing works

SchNet – A deep learning architecture for molecules and materials

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works

Research Directions for the Internet of Things

Stankovic, J. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2312291 · Cited by 1,941 · see citing works

A Survey on the Edge Computing for the Internet of Things

Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520 · cited 4× · see citing works

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989385 · Cited by 1,485 · see citing works

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works

Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Aparicio, J., & Goldberg, K. Y. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.058 · Cited by 1,299 · see citing works

A Survey on Security and Privacy Issues in Internet-of-Things

Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2694844 · Cited by 1,280 · see citing works

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255 · see citing works

Deep Reinforcement Learning for Dialogue Generation

Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1127 · Cited by 1,221 · see citing works

Adversarial machine learning

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194 · see citing works

Deep multimodal learning: a survey on recent advances and trends

Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139 · cited 2× · see citing works

Deep Learning for Identifying Metastatic Breast CancerPreprint

Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028 · see citing works

Neural Networks and Deep Learning

Aggarwal, C. (2018) · Cambridge International Law Journal · doi:10.1007/978-3-319-94463-0 · Cited by 932 · see citing works

Internet of Things and Big Data Analytics for Smart and Connected Communities

Sun, Y., Song, H. H., Jara, A. J., & Bie, R. (2016) · IEEE Access · doi:10.1109/access.2016.2529723 · Cited by 890 · see citing works

Understanding how image quality affects deep neural networks

Dodge, S., & Karam, L. (2016) · 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2016.7498955 · Cited by 835 · see citing works

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810 · see citing works

Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

López‐Martín, M., Carro, B., Sánchez-Esguevillas, A., & Lloret, J. (2017) · IEEE Access · doi:10.1109/access.2017.2747560 · Cited by 772 · see citing works

Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

Severyn, A., & Moschitti, A. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767738 · Cited by 736 · see citing works

Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection

Kim, J., Kim, J., Le, T.-T.-H., & Kim, H. (2016) · International Conference on Platform Technology and Service · doi:10.1109/platcon.2016.7456805 · Cited by 735 · see citing works

Deep learning for event-driven stock prediction

Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015) · International Conference on Artificial Intelligence · Cited by 686 · see citing works

Deep Learning-Based Document Modeling for Personality Detection from Text

Majumder, N., Poria, S., Gelbukh, A. F., & Cambria, E. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.23 · Cited by 671 · see citing works

Enhancing deep learning sentiment analysis with ensemble techniques in social applications

Araque, Ó., Corcuera, I., Sánchez-Rada, J. F., & Iglesias, C. Á. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.002 · Cited by 564 · see citing works

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536 · see citing works

HDLTex: Hierarchical Deep Learning for Text Classification

Kowsari, K., Brown, D. E., Heidarysafa, M., Meimandi, K. J., Gerber, M. S., & Barnes, L. E. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-134 · Cited by 484 · see citing works

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2727878 · Cited by 474 · see citing works

Multi-key privacy-preserving deep learning in cloud computing

Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S., & Chen, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.006 · Cited by 462 · see citing works

A deep learning approach to on-node sensor data analytics for mobile or wearable devices

Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2633287 · Cited by 439 · see citing works

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Mohammadi, M., Al‐Fuqaha, A., Guizani, M., & Oh, J.-S. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712560 · Cited by 435 · see citing works

A deep learning ensemble approach for crude oil price forecasting

(2017) · doi:10.1016/j.eneco.2017.05.023 · Cited by 382 · see citing works

A Survey on Big Data Market: Pricing, Trading and Protection

Liang, F., Yu, W., An, D., Yang, Q., Fu, X., & Zhao, W. (2018) · IEEE Access · doi:10.1109/access.2018.2806881 · Cited by 361 · see citing works

Benchmarking State-of-the-Art Deep Learning Software Tools

Shi, S., Wang, Q., Xu, P., & Chu, X. (2016) · doi:10.1109/ccbd.2016.029 · Cited by 334 · see citing works

Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Kahn, G., Villaflor, A., Ding, B., Abbeel, P., & Levine, S. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460655 · Cited by 308 · see citing works

A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things

Zhu, J., Song, Y., Jiang, D., & Song, H. H. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2759728 · Cited by 295 · see citing works

Deep learning in color: towards automated quark/gluon jet discrimination

Komiske, P. T., Metodiev, E. M., & Schwartz, M. D. (2017) · Journal of High Energy Physics · doi:10.1007/jhep01(2017)110 · Cited by 291 · see citing works

Automated analysis of high‐content microscopy data with deep learning

Kraus, O. Z., Grys, B. T., Ba, J. L., Chong, Y. T., Frey, B. J., Boone, C. M., & Andrews, B. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177551 · Cited by 274 · see citing works

Reconstructing cell cycle and disease progression using deep learning

Eulenberg, P., Köhler, N., Blasi, T., Filby, A. J., Carpenter, A. E., Rees, P. C. T., Theis, F. J., & Wolf, F. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00623-3 · Cited by 272 · see citing works

Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method

Wang, J., Gu, Q., Wu, J., Liu, G., & Xiong, Z. (2016) · Proceedings · doi:10.1109/icdm.2016.0061 · Cited by 259 · see citing works

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

Gabbard, H., Williams, M. J., Hayes, F. J., et al. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.141103 · Cited by 250 · see citing works

Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition

Sainath, T. N., Weiss, R. J., Wilson, K., Li, B., Narayanan, A., Variani, E., Bacchiani, M., Izhak Shafran, J., Senior, A., Chin, K. K., et al. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2672401 · Cited by 231 · see citing works

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

Schirrmeister, R. T., Gemein, L. A. W., Eggensperger, K., Hutter, F., & Tonio Ball, A. (2017) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2017.8257015 · Cited by 221 · see citing works

Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning

Yang, P.-C., Sasaki, K., Suzuki, K., Kase, K., Sugano, S., & Ogata, T. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2633383 · Cited by 220 · see citing works

Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection

Aminanto, M. E., Choi, R., Tanuwidjaja, H. C., Yoo, P. D., & Kim, K. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2762828 · Cited by 219 · see citing works

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

Raykov, Y. P., Boukouvalas, A., Baig, F., & Little, M. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162259 · Cited by 217 · see citing works

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

Saito, Y., Takamichi, S., & Saruwatari, H. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2761547 · Cited by 217 · see citing works

Adversarial Examples: Attacks and Defenses for Deep LearningPreprint

Yuan, X., He, P. L., Zhu, Q., & Li, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.07107 · Cited by 212 · see citing works

Deep fully-connected networks for video compressive sensing

Iliadis, M., Spinoulas, L., & Katsaggelos, A. K. (2017) · Digital Signal Processing · doi:10.1016/j.dsp.2017.09.010 · Cited by 210 · see citing works

A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

Du, L., Du, Y., Li, Y., Su, J., Kuan, Y.-C., Liu, C.-C., & Chang, M.-C. F. (2018) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2017.2735490 · Cited by 205 · see citing works

Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Deen, M. J. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2739340 · Cited by 199 · see citing works

AdaNet: Adaptive Structural Learning of Artificial Neural NetworksPreprint

Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M., & Yang, S. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.01097 · Cited by 182 · see citing works

Human action recognition in RGB-D videos using motion sequence information and deep learning

Ijjina, E. P., & Chalavadi, K. M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.07.013 · Cited by 170 · see citing works

Enabling Deep Learning on IoT Devices

Tang, J., Sun, D., Liu, S., & Gaudiot, J. (2017) · Computer · doi:10.1109/mc.2017.3641648 · Cited by 164 · see citing works

Deep Learning on FPGAs: Past, Present, and FuturePreprint

Lacey, G., Taylor, G. W., & Areibi, S. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04283 · Cited by 157 · see citing works

On Optimal PMU Placement-based Defense against Data Integrity Attacks in Smart Grid

Yang, Q., An, D., Min, R., et al. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2686367 · Cited by 152 · see citing works

Deep learning in color: towards automated quark/gluon jet discrimination

Schwartz, M. D. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.1007/jhep01%282017%29110 · Cited by 149 · see citing works

Deep regression for monocular camera-based 6-DoF global localization in outdoor environments

Naseer, T., & Burgard, W. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205957 · Cited by 141 · see citing works

A Real-Time En-Route Route Guidance Decision Scheme for Transportation-Based Cyberphysical Systems

Lin, J., Yu, W., Yang, X., et al. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2572123 · Cited by 125 · see citing works

Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data

Ducournau, A., & Fablet, R. (2016) · IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) · doi:10.1109/prrs.2016.7867019 · Cited by 124 · see citing works

Early Visual Concept Learning with Unsupervised Deep LearningPreprint

Higgins, I., Matthey, L., Glorot, X., Pal, A., Uría, B., Blundell, C., Mohamed, S., & Lerchner, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05579 · Cited by 120 · see citing works

Fast Single Shot Detection and Pose Estimation

Poirson, P., Ammirato, P., Fu, C.-Y., Liu, W., Košecká, J., & Berg, A. C. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.78 · Cited by 113 · see citing works

Multi-View 3D Object Retrieval With Deep Embedding Network

Guo, H., Wang, J., Gao, Y., Li, J., & Lu, H. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2609814 · Cited by 112 · see citing works

Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

Shao, Z., Zhang, L., & Wang, L. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2748341 · Cited by 110 · see citing works

DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data

Zhu, D., Jin, H., Yang, Y., et al. (2017) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/iscc.2017.8024568 · Cited by 107 · see citing works

Ultra-Dense Networks: Survey of State of the Art and Future Directions

Yu, W., Xu, H., Zhang, H., et al. (2016) · International Conference on Computer Communication and Networks (ICCCN) · doi:10.1109/icccn.2016.7568592 · Cited by 106 · see citing works

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification

Wang, Q., Zheng, Y., Yang, G., Jin, W., Chen, X., & Yin, Y. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2685586 · Cited by 104 · see citing works

System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey

Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2017) · IEEE Access · doi:10.1109/access.2017.2777827 · Cited by 100 · see citing works

Survey on Improving Data Utility in Differentially Private Sequential Data Publishing

Yang, X., Wang, T., Ren, X., & Yu, W. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2715334 · Cited by 94 · see citing works

When deep learning meets edge computing

Huang, Y., Ma, X., Fan, X., Liu, J., & Gong, W. (2017) · 2017 IEEE 25th International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2017.8117585 · Cited by 89 · see citing works

Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

Dairi, A., Harrou, F., Senouci, M., & Sun, Y. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.11.014 · Cited by 87 · see citing works

Real-time deep learning of robotic manipulator inverse dynamics

Polydoros, A., Nalpantidis, L., & Krüger, V. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353857 · Cited by 84 · see citing works

Parking-stall vacancy indicator system, based on deep convolutional neural networks

Valipour, S., Siam, M., Stroulia, E., & Jägersand, M. (2016) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2016.7845408 · Cited by 81 · see citing works

Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning

González, J. A., Cheah, L. A., Gomez, A. M., Green, P., Gilbert, J. M., Ell, S. R., Moore, R. K., & Holdsworth, E. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2757263 · Cited by 78 · see citing works

Application of Deep Belief Networks for opcode based malware detection

Ding, Y., Chen, S., & Xu, J. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727705 · Cited by 76 · see citing works

Smart city: The state of the art, datasets, and evaluation platforms

Mallapuram, S., Ngwum, N., Fang, Y., et al. (2017) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2017.7960034 · Cited by 72 · see citing works

On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach

Qu, Y.-H., Yu, H., Gong, X., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188129 · Cited by 71 · see citing works

DSCNN: Hardware-oriented optimization for Stochastic Computing based Deep Convolutional Neural Networks

Li, Z., Ren, A., Li, J., et al. (2016) · Proceedings, IEEE International Conference on Computer Design/Proceedings - IEEE International Conference on Computer Design · doi:10.1109/iccd.2016.7753357 · Cited by 67 · see citing works

Increasing web service availability by detecting application-layer DDoS attacks in encrypted traffic

Zolotukhin, M., Hämäläinen, T., Kokkonen, T., & Siltanen, J. (2016) · doi:10.1109/ict.2016.7500408 · Cited by 65 · see citing works

A Gaussian-Mixture Model Based Detection Scheme against Data Integrity Attacks in the Smart Grid

Yang, X., Zhao, P. F., Zhang, X., Lin, J., & Yu, W. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2631520 · Cited by 61 · see citing works

A cloud computing based architecture for cyber security situation awareness

Yu, W., Xu, G., Chen, Z., et al. (2013) · IEEE Conference on Communications and Network Security · doi:10.1109/cns.2013.6682765 · Cited by 56 · see citing works

Reinforcement distribution in fuzzy Q-learning

Bonarini, A., Lazaric, A., Montrone, F., et al. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2008.11.026 · Cited by 54 · see citing works

Multimodal deep learning for solar radio burst classification

Ma, L., Chen, Z., Xu, L., & Yan, Y. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.04.013 · Cited by 54 · see citing works

A laguerre neural network-based ADP learning scheme with its application to tracking control in the Internet of Things

Luo, X., Lv, Y., Zhou, M., et al. (2016) · Personal and Ubiquitous Computing · doi:10.1007/s00779-016-0916-x · Cited by 50 · see citing works

Discriminative Dimensionality Reduction for Multi-Dimensional Sequences

Su, B., Ding, X., Wang, H., & Wu, Y. N. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2665545 · Cited by 49 · see citing works

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

Yang, X., Luo, X., Wang, W., et al. (2017) · Sensors · doi:10.3390/s17010135 · Cited by 48 · see citing works

Natural language description of remote sensing images based on deep learning

Zhang, X., Li, X., An, J., et al. (2017) · IEEE International Geoscience and Remote Sensing Symposium · doi:10.1109/igarss.2017.8128075 · Cited by 48 · see citing works

Towards statistical modeling and machine learning based energy usage forecasting in smart grid

Yu, W., An, D., GRIFFITH, D. W. T., et al. (2015) · ACM SIGAPP Applied Computing Review · doi:10.1145/2753060.2753061 · Cited by 47 · see citing works

Big IoT data mining for real-time energy disaggregation in buildings

Mocanu, D. C., Mocanu, E., Nguyen, P. H., et al. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844820 · Cited by 45 · see citing works

Robust acoustic event classification using deep neural networks

Sharan, R. V., & Moir, T. J. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.013 · Cited by 45 · see citing works

Deep Learning for Financial Time Series Forecasting in A-Trader System

Korczak, J. J., & Hernes, M. (2017) · Annals of Computer Science and Information Systems · doi:10.15439/2017f449 · Cited by 44 · see citing works

A Distributed Anomaly Detection Method of Operation Energy Consumption Using Smart Meter Data

Yuan, Y., & Jia, K. (2015) · International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) · doi:10.1109/iih-msp.2015.38 · Cited by 43 · see citing works

Integrating mobile and cloud for PPG signal selection to monitor heart rate during intensive physical exercise

Jindal, V. (2016) · IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft) · doi:10.1145/2897073.2897132 · Cited by 43 · see citing works

On behavior-based detection of malware on Android platform

Yu, W., Zhang, H., Ge, L., et al. (2013) · Globecom · doi:10.1109/glocom.2013.6831173 · Cited by 40 · see citing works

Supervised autonomy for online learning in human-robot interaction

Senft, E., Baxter, P. E., Kennedy, J., Lemaignan, S., & Belpaeme, T. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.03.015 · Cited by 39 · see citing works

Design and Realization of WInternet

Wu, J., & Zhao, W. (2016) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/2872332 · Cited by 38 · see citing works

Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices

Wang, Y., Li, H., & Li, X. (2016) · doi:10.1145/2966986.2967068 · Cited by 30 · see citing works

Smart Transportation Systems: Architecture, Enabling Technologies, and Open Issues

Xu, H., Lin, J., & Yu, W. (2017) · SpringerBriefs in computer science · doi:10.1007/978-981-10-3892-1_2 · Cited by 30 · see citing works

Tuning Deep Learning Performance for Android Malware Detection

Booz, J., McGiff, J., Hatcher, W. G., Yu, W., Nguyen, J. H., & Lu, C. (2018) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2018.8441128 · Cited by 30 · see citing works

DifFUZZY: a fuzzy clustering algorithm for complex datasets

Cominetti, O., Matzavinos, A., Samarasinghe, S., et al. (2010) · International Journal of Computational Intelligence in Bioinformatics and Systems Biology · doi:10.1504/ijcibsb.2010.038222 · Cited by 29 · see citing works

Towards experimental evaluation of intelligent Transportation System safety and traffic efficiency

Ekedebe, N. N., Lü, C., & Yu, W. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248909 · Cited by 28 · see citing works

Big data machine learning and graph analytics: Current state and future challenges

Huang, H. H., & Liu, H. (2014) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2014.7004471 · Cited by 28 · see citing works

On Binary Decomposition Based Privacy-Preserving Aggregation Schemes in Real-Time Monitoring Systems

Yang, X., Ren, X., Lin, J., & Yu, W. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2516983 · Cited by 28 · see citing works

Image based search engine using deep learning

Jain, S., & Dhar, J. (2017) · International Conference on Contemporary Computing · doi:10.1109/ic3.2017.8284301 · Cited by 28 · see citing works

Eyeball gesture controlled automatic wheelchair using deep learning

Rajesh, A., & Mantur, M. (2017) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc.2017.8288981 · Cited by 25 · see citing works

Deep learning for credit card data analysis

Niimi, A. (2015) · World Congress on Internet Security · doi:10.1109/worldcis.2015.7359417 · Cited by 24 · see citing works

Towards Neural Network Based Malware Detection on Android Mobile Devices

Yu, W., Ge, L., Xu, G., et al. (2014) · Advances in information security · doi:10.1007/978-3-319-10374-7_7 · Cited by 23 · see citing works

Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification

Arsa, D. M. S., Jati, G., Mantau, A. J., & Wasito, I. (2016) · International Workshop on Big Data and Information Security (IWBIS) · doi:10.1109/iwbis.2016.7872892 · Cited by 22 · see citing works

A Fast Feature Fusion Algorithm in Image Classification for Cyber Physical Systems

Wang, Y., Song, B., Zhang, P., Xin, N., & Cao, G. (2017) · IEEE Access · doi:10.1109/access.2017.2705798 · Cited by 22 · see citing works

Deep Learning for Real-Time Crime Forecasting and its TernarizationPreprint

Wang, B., Yin, P., Bertozzi, A. L., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08833 · Cited by 20 · see citing works

Numerical encoding to Tame SQL injection attacks

Uwagbole, S. O., Buchanan, W. J., & Fan, L. (2016) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2016.7502997 · Cited by 17 · see citing works

DeepRT: deep learning for peptide retention time prediction in proteomicsPreprint

Ma, C., Zhu, Z., Ye, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05368 · Cited by 17 · see citing works

Deep learning approach to the Higgs boson CP measurement in H→ττ decay and associated systematics

Barberio, E. L., Le, B., Richter-Was, E., et al. (2017) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.96.073002 · Cited by 17 · see citing works

On data integrity attacks against route guidance in transportation-based cyber-physical systems

Lin, J., Yu, W., Zhang, N., Yang, X., & Ge, L. (2017) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2017.7983125 · Cited by 15 · see citing works

Noise power spectral density estimation for binaural noise reduction exploiting direction of arrival estimates

Marquardt, D., & Doclo, S. (2017) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2017.8170030 · Cited by 15 · see citing works

ApesNet: a pixel‐wise efficient segmentation network for embedded devices

Wu, C., Cheng, H.-P., Li, S., Li, H. H., & Chen, Y. (2016) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/iet-cps.2016.0027 · Cited by 14 · see citing works

Estimation of intersection traffic density on decentralized architectures with deep networks

Yeshwanth, C., Sooraj, P. S. A., Sudhakaran, V., & Raveendran, V. (2017) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2017.8090799 · Cited by 14 · see citing works

Fuzzy DBN with rule-based knowledge representation and high interpretability

Zhang, X., Pan, X., & Wang, S. (2017) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske.2017.8258762 · Cited by 14 · see citing works

Audio recapture detection using deep learning

Luo, D., Wu, H., & Huang, J. (2015) · IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) · doi:10.1109/chinasip.2015.7230448 · Cited by 13 · see citing works

A streaming-based network monitoring and threat detection system

Chen, Z., Zhang, H., Hatcher, W. G., et al. (2016) · IEEE International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera.2016.7516125 · Cited by 13 · see citing works

Machine learning-based mobile threat monitoring and detection

Hatcher, W. G., Maloney, D., & Yu, W. (2016) · IEEE International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera.2016.7516130 · Cited by 12 · see citing works

Device-free activity recognition using CSI & big data analysis: A survey

Kim, S. (2017) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn.2017.7993844 · Cited by 12 · see citing works

A Real Time and Non-Contact Multiparameter Wearable Device for Health Monitoring

Mahmud, M. S., Wang, H., Alam, E. E., et al. (2016) · Globecom · doi:10.1109/glocom.2016.7841538 · Cited by 11 · see citing works

DeepCorrect: Correcting DNN models against Image DistortionsPreprint

Borkar, T., & Karam, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02406 · Cited by 11 · see citing works

Minimalist Design for Accelerating Convolutional Neural Networks for Low-End FPGA Platforms

Morcel, R., Akkary, H. H., Hajj, H. M., et al. (2017) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2017.62 · Cited by 8 · see citing works

Analysis of Facial Expressiveness Captured in Reaction to Videos

Srinivasa, K. G., Anupindi, S., Sharath, R., & Chaitanya, S. K. (2017) · IEEE International Advance Computing Conference · doi:10.1109/iacc.2017.0140 · Cited by 7 · see citing works

Object category learning and retrieval with weak supervisionPreprint

Hickson, S., Angelova, A., Essa, I. A., & Sukthankar, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08985 · Cited by 7 · see citing works

Speech separation by cost-sensitive deep learning

Zhang, X.-L. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282020 · Cited by 7 · see citing works

Terminal Replacement Prediction Based on Deep Belief Networks

Zhao, Z., Guo, J. F., Ding, E., et al. (2015) · International Conference on Network and Information Systems for Computers (ICNISC) · doi:10.1109/icnisc.2015.96 · Cited by 6 · see citing works

Deep Features for Person Re-identification

Wang, S., Shang, Y., Wang, J., et al. (2015) · International Conference on Semantics, Knowledge and Grids (SKG) · doi:10.1109/skg.2015.19 · Cited by 5 · see citing works

Situation reasoning framework for the Internet of Things environments using deep learning results

Park, S., Sohn, M. M., Jin, H., et al. (2016) · doi:10.1109/ickea.2016.7803006 · Cited by 5 · see citing works

Deep convolutional Triplet network for quantitative medical image analysis with comparative case study of gamma image classification

Thammasorn, P., Chaovalitwongse, W. A., Wootton, L. S., Ford, E., & Nyflot, M. J. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217813 · Cited by 4 · see citing works

Weakly Supervised Hand Pose Recovery with Domain Adaptation by Low-Rank Alignment

Hong, C., Yu, J., Xie, R., & Tao, D. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0070 · Cited by 3 · see citing works

Image registration based on SOFM neural network clustering

Ge, L.-L., Wu, Y., Hua, B., et al. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8028313 · Cited by 3 · see citing works

Hardware design exploration of fully-connected deep neural network with binary parameters

Kim, J., Kim, J., Kim, B., et al. (2016) · International SoC Design Conference · doi:10.1109/isocc.2016.7799799 · Cited by 1 · see citing works

A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective

Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018) · IEEE Access · doi:10.1109/access.2018.2884906 · Cited by 637 · see citing works

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks

Savazzi, S., Nicoli, M., & Rampa, V. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2964162 · Cited by 431 · see citing works

Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions

Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106368 · Cited by 241 · see citing works

A Systematic Review of Deep Learning Approaches to Educational Data Mining

Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019) · Complexity · doi:10.1155/2019/1306039 · Cited by 230 · see citing works

Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

Liu, X., Qian, C., Hatcher, W. G., Xu, H., Liao, W., & Yu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2920763 · Cited by 152 · see citing works

Effect of data leakage in brain MRI classification using 2D convolutional neural networks

Yagis, E., Atnafu, S. W., García Seco de Herrera, A., Marzi, C., Scheda, R., Giannelli, M., Tessa, C., Citi, L., & Diciotti, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01681-w · Cited by 161 · see citing works

Engineering AI Systems: A Research Agenda

Bosch, J., Olsson, H. H., & Crnkovic, I. (2021) · Advances in Systems Analysis, Software Engineering, and High Performance Computing · doi:10.4018/978-1-7998-5101-1.ch001 · Cited by 108 · see citing works

Soft errors in DNN accelerators: A comprehensive review

Ibrahim, Y., Wang, H., Liu, J., Wei, J., Chen, L., Rech, P., Adam, K., & Guo, G. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113969 · Cited by 102 · see citing works

Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector

Domingos, E., Ojeme, B., & Daramola, O. (2021) · Computation · doi:10.3390/computation9030034 · Cited by 77 · see citing works

Soft Error Resilience of Deep Residual Networks for Object Recognition

Ibrahim, Y., Wang, H., Bai, M., Liu, Z., Wang, J., Yang, Z., & Chen, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968129 · Cited by 72 · see citing works

QATFP-YOLO: Optimizing Object Detection on Non-GPU Devices with YOLO Using Quantization-Aware Training and Filter Pruning

Idama, G., Guo, Y., & Yu, W. (2024) · 2024 33rd International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637599 · Cited by 5 · see citing works

A Study on Big Data Analytics Tools

Devi, R., & Sharmila, K. (2023) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-8533 · Cited by 2 · see citing works

Deciphering Big Data Research Themes

Chen, T. T., & Lee, M. R. (2018) · doi:10.1109/compsac.2018.00066

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Mehta, Y., Ramkrishana, S., & Buch, S. (2015) · Cited by 5 · see citing works

Sketch of Big Data Real-Time Analytics Model

Amen, B. M., & Lu, J. (2015) · University of Huddersfield Repository (University of Huddersfield) · Cited by 7 · see citing works

Survey on Big Data Analytics

Dhruva, M. S. (2017) · doi:10.24032/ijeacs/0206/02 · Cited by 5 · see citing works