State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Abstract
Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
There are many potential areas where DL can be applied, including online content streaming, routing and traffic control, etc.
“In this regard, there are many potential areas where DL can be applied, including online content streaming [285], routing and traffic control [286] [287], etc.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [287]
Cites
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
(2014) · Cited by 33,419 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032 · 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
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · see citing works
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works
Robust Face Recognition via Sparse Representation
Wright, J. N., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,395 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
An introduction to computing with neural nets
Lippmann, R. P. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,848 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
(2011) · Cited by 6,993 · see citing works
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works
An efficient k-means clustering algorithm: analysis and implementation
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017616 · Cited by 5,660 · see citing works
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“In other words, in each level, a new transformation of the previously learned features is performed that is served as the input to the next level [28]–[31].”
BackgroundA unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
(2010) · Cited by 4,411 · see citing works
A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R., & Monard, M. (2004) · SKDD · doi:10.1145/1007730.1007735 · Cited by 4,271 · see citing works
Bayesian Inference in Statistical Analysis.
Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,869 · see citing works
A survey of decision tree classifier methodology
Safavian, S. R., & Landgrebe, D. A. (1991) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.97458 · Cited by 3,805 · see citing works
Multi-column deep neural networks for image classification
Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · 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.2012.6248110 · Cited by 3,760 · see citing works
(2013) · Cited by 3,740 · see citing works
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451 · see citing works
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318 · see citing works
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309 · see citing works
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300 · see citing works
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120 · see citing works
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000094 · Cited by 2,939 · see citing works
(2011) · Cited by 2,752 · see citing works
White, T. (2009) · Cited by 2,705 · see citing works
A neural probabilistic language model
BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,646 · see citing works
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403 · see citing works
Convolutional Neural Networks for Speech Recognition
Abdel‐Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2339736 · Cited by 2,268 · see citing works
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890 · see citing works
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664 · see citing works
Convolutional Radio Modulation Recognition Networks
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-44188-7_16 · Cited by 1,620 · see citing works
Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,514 · see citing works
Bayesian Inference in Statistical Analysis
Hill, B. M., Box, G. E. P., & Tiao, G. C. (1974) · Technometrics · doi:10.2307/1267683 · Cited by 1,465 · see citing works
Auto-association by multilayer perceptrons and singular value decomposition
Bourlard, H., & Kamp, Y. (1988) · Biological Cybernetics · doi:10.1007/bf00332918 · Cited by 1,332 · see citing works
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,266 · see citing works
Discriminative K-SVD for dictionary learning in face recognition
Zhang, Q., & Li, B. (2010) · 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.2010.5539989 · Cited by 1,259 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216 · see citing works
A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective
Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2337336 · Cited by 1,212 · see citing works
Discovering regions of different functions in a city using human mobility and POIs
Yuan, J., Zheng, Y., & Xie, X. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339561 · Cited by 1,118 · see citing works
Connectionist Speech Recognition: A Hybrid Approach
Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115 · see citing works
Multimodal Learning with Deep Boltzmann Machines
Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070 · see citing works
Nonnegative Matrix Factorization: A Comprehensive Review
Wang, Y.-X., & Zhang, Y. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.51 · Cited by 983 · see citing works
Moy, J. (1998) · doi:10.17487/rfc2328 · Cited by 971 · see citing works
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965 · see citing works
Zheng, Y., Liu, F., & Hsieh, H.-P. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2488188 · Cited by 937 · see citing works
Detection, classification, and tracking of targets
Li, D., Wong, K. D., Hu, Y. H., & Sayeed, A. M. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.985674 · Cited by 934 · see citing works
(2016) · doi:10.1109/ieeestd.2016.7786995 · Cited by 929 · see citing works
(2003) · Technometrics · doi:10.1198/tech.2003.s783 · Cited by 908 · see citing works
Conversational speech transcription using context-dependent deep neural networks
Seide, F. T. B., Li, G., & Yu, D. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-169 · Cited by 877 · see citing works
Parsing with Compositional Vector Grammars
Socher, R., Bauer, J., Manning, C. D., & Ng, A. Y. (2013) · Cited by 830 · see citing works
Learning distributed representations of concepts.
Hinton, G. E. (1989) · eScholarship (California Digital Library) · Cited by 815 · see citing works
Accurate, scalable in-network identification of p2p traffic using application signatures
Sen, S. B., Spatscheck, O., & Wang, D. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988742 · Cited by 807 · see citing works
Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799 · see citing works
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksPreprint
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2014) · arXiv (Cornell University) · Cited by 790 · see citing works
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Socher, R., Huang, E., Pennin, J., Manning, C. D., & Ng, A. Y. (2011) · Cited by 780 · see citing works
Ramírez, I., Sprechmann, P., & Sapiro, G. (2010) · 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.2010.5539964 · Cited by 762 · see citing works
Large-scale deep unsupervised learning using graphics processors
Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742 · see citing works
A tutorial survey of architectures, algorithms, and applications for deep learning
Deng, L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2013.9 · Cited by 737 · see citing works
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Cited by 718 · see citing works
Traffic classification using clustering algorithms
Erman, J., Arlitt, M. F., & Mahanti, A. (2006) · doi:10.1145/1162678.1162679 · Cited by 712 · see citing works
Williams, N., Zander, S., & Armitage, G. J. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1163593.1163596 · Cited by 698 · see citing works
Human mobility, social ties, and link prediction
Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabási, A.-Ĺ. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020581 · Cited by 671 · see citing works
Rahbar, A. G., & Yang, O. W. W. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080408 · Cited by 668 · see citing works
Steinwart, I., & Christmann, A. (2008) · Information science and statistics · doi:10.1007/978-0-387-77242-4 · Cited by 654 · see citing works
Seide, F. T. B., Li, G., Xie, C., & Yu, D. (2011) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2011.6163899 · Cited by 641 · see citing works
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612 · see citing works
Analyzing Peer-To-Peer Traffic Across Large Networks
Sen, S. B., & Wang, J. (2004) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2004.826277 · Cited by 587 · see citing works
Deep Learning via Semi-supervised Embedding
Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 577 · see citing works
Intelligent Selection of Language Model Training Data
Moore, R. C., & Lewis, W. D. (2010) · Cited by 530 · see citing works
Zheng, Y., Liu, Y., Yuan, J. Y., & Xie, X. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030126 · Cited by 529 · see citing works
A survey of Internet-of-Things: Future vision, architecture, challenges and services
Singh, D., Tripathi, G., & Jara, A. J. (2014) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2014.6803174 · Cited by 511 · see citing works
Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494 · see citing works
Learning Convolutional Feature Hierarchies for Visual Recognition
Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., & LeCun, Y. (2010) · Cited by 488 · see citing works
Advances in optimizing recurrent networks
Bengio, Y., Boulanger-Lewandowski, N., & Pascanu, R. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639349 · Cited by 478 · see citing works
An Efficient Learning Procedure for Deep Boltzmann Machines
Salakhutdinov, R., & Hinton, G. E. (2012) · Neural Computation · doi:10.1162/neco_a_00311 · Cited by 477 · see citing works
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore
Jiang, S., Ferreira, J., & González, M. C. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2631141 · Cited by 470 · see citing works
Real-time forest fire detection with wireless sensor networks
Yu, L., Wang, N., & Meng, X. (2005) · International Conference on Wireless Communications, Networking and Mobile Computing · doi:10.1109/wcnm.2005.1544272 · Cited by 463 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064220 · Cited by 460 · see citing works
Scaling Learning Algorithms toward AI
Bengio, Y., & LeCun, Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7496.003.0016 · Cited by 457 · see citing works
Types of machine learning algorithms
Oladipupo, T. (2010) · New Advances in Machine Learning · doi:10.5772/9385 · Cited by 443 · see citing works
Face recognition with support vector machines: global versus component-based approach
Heisele, B., Ho, P., & Poggio, T. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937693 · Cited by 426 · see citing works
Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet‐Ros, P., Alarcón, E., Solé, M., Muntés-Mulero, V., Meyer, D., Barkai, S., Hibbett, M. J., et al. (2017) · ACM SIGCOMM Computer Communication Review · doi:10.1145/3138808.3138810 · Cited by 411 · see citing works
A Survey of Self Organisation in Future Cellular Networks
Aliu, O. G., Imran, A., Imran, M. A., & Evans, B. G. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.021312.00116 · Cited by 411 · see citing works
Deep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401 · see citing works
Kato, N., Fadlullah, Z. M., Mao, B., Tang, F., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1600317wc · Cited by 400 · see citing works
Self-citationTraining recurrent neural networks
Hinton, G. E., & Sutskever, I. (2013) · Cited by 380 · see citing works
Crowd sensing of traffic anomalies based on human mobility and social media
Pan, B., Zheng, Y., Wilkie, D., & Shahabi, C. (2013) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2525314.2525343 · Cited by 377 · see citing works
Leveraging social media networks for classification
Tang, L., & Liu, H. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0210-x · Cited by 360 · see citing works
Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Transactions on Computers · doi:10.1109/tc.2017.2709742 · Cited by 350 · see citing works
Self-citationNatural Human Mobility Patterns and Spatial Spread of Infectious Diseases
Belik, V., Geisel, T., & Brockmann, D. (2011) · Physical Review X · doi:10.1103/physrevx.1.011001 · Cited by 337 · see citing works
Applications of Machine Learning to Cognitive Radio Networks
Clancy, C., Hecker, J., Stuntebeck, E., & O'Shea, T. (2007) · IEEE Wireless Communications · doi:10.1109/mwc.2007.4300983 · Cited by 328 · see citing works
Hasan, S., Zhan, X., & Ukkusuri, S. V. (2013) · doi:10.1145/2505821.2505823 · Cited by 309 · see citing works
Online Demand Under Limited Consumer Search
Kim, J. B., Albuquerque, P., & Bronnenberg, B. J. (2010) · Marketing Science · doi:10.1287/mksc.1100.0574 · Cited by 293 · see citing works
Better Mixing via Deep RepresentationsPreprint
Bengio, Y., Mesnil, G., Dauphin, Y., & Rifai, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4404 · Cited by 288 · see citing works
Gravity model in the Korean highway
Jung, W., Wang, F. Z., & Stanley, E. (2008) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/81/48005 · Cited by 288 · see citing works
Offline/realtime traffic classification using semi-supervised learning
Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. (2007) · Performance Evaluation · doi:10.1016/j.peva.2007.06.014 · Cited by 287 · see citing works
Self-organizing maps of symbol strings
Kohonen, T., & Somervuo, P. J. (1998) · Neurocomputing · doi:10.1016/s0925-2312(98)00031-9 · Cited by 283 · see citing works
In-network outlier detection in wireless sensor networks
Branch, J. W., Giannella, C. R., Szymanski, B. K., Wolff, R., & Kargupta, H. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0474-5 · Cited by 272 · see citing works
Neural networks for shortest path computation and routing in computer networks
Ali, M., & Kamoun, F. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.286889 · Cited by 272 · see citing works
Unsupervised learning of distributions on binary vectors using two layer networks
Freund, Y., & Haussler, D. H. (1991) · Cited by 267 · see citing works
Incremental extreme learning machine with fully complex hidden nodes
Huang, G.-B., Li, M.-B., Chen, L., & Siew, C. (2007) · Neurocomputing · doi:10.1016/j.neucom.2007.07.025 · Cited by 259 · see citing works
A Simple Relational Classifier
Macskassy, S. A., & Provost, F. (2003) · doi:10.21236/ada452802 · Cited by 255 · see citing works
Application of sampling methodologies to network traffic characterization
claffy, K., Polyzos, G. C., & Braun, H.-W. (1993) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/166237.166256 · Cited by 243 · see citing works
Xiao, T., Zhang, J., Yang, K., Peng, Y., & Zhang, Z. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654926 · Cited by 235 · see citing works
Scalable learning of collective behavior based on sparse social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233 · see citing works
Estimating flow distributions from sampled flow statistics
Duffield, N. G., Lund, C., & Thorup, M. (2003) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/863955.863992 · Cited by 230 · see citing works
A Hybrid Malicious Code Detection Method based on Deep Learning
Li, Y., Ma, R., & Jiao, R. (2015) · International Journal of Security and Its Applications · doi:10.14257/ijsia.2015.9.5.21 · Cited by 229 · see citing works
Feature Learning in Deep Neural Networks - Studies on Speech Recognition TasksPreprint
Yu, D., Seltzer, M. L., Li, J., Huang, J.-T., & Seide, F. T. B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3605 · Cited by 228 · see citing works
Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning
Chen, C. L. P., Zhang, C.-Y., Chen, L., & Gan, M. (2015) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2015.2406889 · Cited by 223 · see citing works
Localization In Wireless Sensor Networks Based on Support Vector Machines
Tran, D. A., & Nguyen, T. X. (2008) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2007.70800 · Cited by 210 · see citing works
Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines
Kaplantzis, S., Shilton, A. P., Mani, N., & Şekercioğlu, Y. A. (2007) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2007.4496866 · Cited by 207 · see citing works
Global optimization of a neural network-hidden Markov model hybrid
Bengio, Y., Mori, R. D., Flammia, G., & Kompe, R. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.125866 · Cited by 201 · see citing works
Kato, N., Suzuki, M., Omachi, S., Aso, H., & NEMOTO, Y. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.754617 · Cited by 191 · see citing works
Self-citationQuarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks
Rajasegarar, S., Leckie, C. A., Palaniswami, M. S., & Bezdek, J. C. (2007) · International Conference on Communications · doi:10.1109/icc.2007.637 · Cited by 177 · see citing works
Quantifying the semantics of search behavior before stock market moves
Curme, C., Preis, T., Stanley, E., & Moat, H. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324054111 · Cited by 172 · see citing works
Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks
Janakiram, D., Kumar, A. V. U. P., & Reddy, A. M. (2006) · COMmunication System softWAre and MiddlewaRE · doi:10.1109/comswa.2006.1665221 · Cited by 164 · see citing works
iMouse: An Integrated Mobile Surveillance and Wireless Sensor System
Tseng, Y., Wang, Y., Cheng, K.-Y., & Hsieh, Y.-Y. (2007) · Computer · doi:10.1109/mc.2007.211 · Cited by 164 · see citing works
Online Incremental Feature Learning with Denoising Autoencoders
Zhou, G., Sohn, K., & Lee, H. (2012) · Cited by 162 · see citing works
Fridrich, J. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9781139192903.019 · Cited by 159 · see citing works
Deep Learning for Efficient Discriminative Parsing
Collobert, R. (2011) · Cited by 156 · see citing works
A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs
Wang, P., Lin, S.-C., & Luo, M. (2016) · 2016 IEEE International Conference on Services Computing (SCC) · doi:10.1109/scc.2016.133 · Cited by 155 · see citing works
Measuring social functions of city regions from large-scale taxi behaviors
Qi, G., Li, X., Li, S., Pan, G., Wang, Z., & Zhang, D. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766912 · Cited by 153 · see citing works
Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home
Lu, C.-H., & Fu, L. (2009) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2009.2021981 · Cited by 152 · see citing works
Face Recognition with Support Vector Machines: Global versus Component-Based Approach
Heisele, B., Ho, P., & Poggio, T. (2001) · doi:10.21236/ada459707 · Cited by 149 · see citing works
Moustapha, A. I., & Šelmić, R. R. (2008) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2007.913803 · Cited by 147 · see citing works
Self-Supervised Visual Descriptor Learning for Dense Correspondence
Schmidt, T., Newcombe, R., & Fox, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2634089 · Cited by 146 · see citing works
Recent advances on artificial intelligence and learning techniques in cognitive radio networks
Abbas, N., Nasser, Y., & Ahmad, K. E. (2015) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-015-0381-7 · Cited by 143 · see citing works
Traffic Classification Using a Statistical Approach
Zuev, D., & Moore, A. W. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31966-5_25 · Cited by 140 · see citing works
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Chon, Y., Talipov, E., Shin, H. J., & Cha, H. (2011) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2070942.2070952 · Cited by 139 · see citing works
Modeling word perception using the Elman network
Liou, C., Huang, J.-C., & Yang, W.-C. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.04.030 · Cited by 138 · see citing works
Selecting Receptive Fields in Deep Networks
Coates, A. (2024) · doi:10.57702/4h3zav33 · Cited by 138 · see citing works
Time series models for internet traffic
Basu, S., Mukherjee, A., & Klivansky, S. M. (1996) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.1996.493355 · Cited by 138 · see citing works
Zhang, Y., Meratnia, N., & Havinga, P. (2012) · Ad Hoc Networks · doi:10.1016/j.adhoc.2012.11.001 · Cited by 132 · see citing works
Deep Learning for NLP (without Magic)
Socher, R., Bengio, Y., & Manning, C. D. (2012) · Cited by 131 · see citing works
Urban human mobility data mining: An overview
Zhao, K., Tarkoma, S., Liu, S., & Vo, H. T. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840811 · Cited by 130 · see citing works
Utgoff, P. E., & Stracuzzi, D. J. (2002) · Neural Computation · doi:10.1162/08997660260293319 · Cited by 129 · see citing works
Deep and Wide: Multiple Layers in Automatic Speech Recognition
Morgan, N. H. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2116010 · Cited by 128 · see citing works
Thu, T. N. T., & Armitage, G. J. (2006) · Conference on Local Computer Networks · doi:10.1109/lcn.2006.322122 · Cited by 123 · see citing works
Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks
Takaishi, D., Nishiyama, H., Kato, N., & Miura, R. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2318177 · Cited by 123 · see citing works
Self-citationGenerating multi-fingered robotic grasps via deep learning
Varley, J., Weisz, J., Weiss, J., & Allen, P. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354004 · Cited by 121 · see citing works
Zorzi, M., Zanella, A., Testolin, A., Grazia, M. D. F. D., & Zorzi, M. (2015) · IEEE Access · doi:10.1109/access.2015.2471178 · Cited by 117 · see citing works
Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics
Zander, S., Thu, T. N. T., & Armitage, G. J. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31966-5_26 · Cited by 116 · see citing works
Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks
Mancini, M., Costante, G., Valigi, P., & Ciarfuglia, T. A. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759632 · Cited by 110 · see citing works
Extending the lifetime of wireless sensor networks: A hybrid routing algorithm
Abdulla, A. E. A. A., Nishiyama, H., & Kato, N. (2011) · Computer Communications · doi:10.1016/j.comcom.2011.10.001 · Cited by 108 · see citing works
Self-citationIdentification and Prediction of Internet Traffic Using Artificial Neural Networks
Chabaa, S., Zeroual, A., & Antari, J. (2010) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2010.23018 · Cited by 102 · see citing works
Predicting link quality using supervised learning in wireless sensor networks
Wang, Y., Martonosi, M., & Peh, L.-S. (2007) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/1317425.1317434 · Cited by 101 · see citing works
Modification of the gravity model and application to the metropolitan Seoul subway system
Goh, S., Lee, K., Park, J. S., & Choi, M. Y. (2012) · Physical Review E · doi:10.1103/physreve.86.026102 · Cited by 92 · see citing works
HYMN: A Novel Hybrid Multi-Hop Routing Algorithm to Improve the Longevity of WSNs
Abdulla, A. E. A. A., Nishiyama, H., Yang, J., Ansari, N., & Kato, N. (2012) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2012.050112.111222 · Cited by 91 · see citing works
Self-citationTarget Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks
Kim, W., Park, J., Yoo, J., Kim, H. J., & Park, C. G. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2226151 · Cited by 88 · see citing works
Data Acquisition through Joint Compressive Sensing and Principal Component Analysis
Masiero, R., Quer, G., Munaretto, D., Rossi, M., Widmer, J., & Zorzi, M. (2009) · Globecom · doi:10.1109/glocom.2009.5425458 · Cited by 88 · see citing works
Neural network based secure media access control protocol for wireless sensor networks
Kulkarni, R. V., & Venayagamoorthy, G. K. (2009) · doi:10.1109/ijcnn.2009.5179075 · Cited by 87 · see citing works
Fault-resilient sensing in wireless sensor networks
Nakayama, H., Ansari, N., Jamalipour, A., & Kato, N. (2007) · Computer Communications · doi:10.1016/j.comcom.2007.04.023 · Cited by 86 · see citing works
Self-citationOn Minimizing the Impact of Mobility on Topology Control in Mobile Ad Hoc Networks
Nishiyama, H., Ngo, T., Ansari, N., & Kato, N. (2012) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2012.010312.110783 · Cited by 85 · see citing works
Self-citationReal-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
Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation
Schwenk, H., Rousseau, A., & Attik, M. (2012) · HAL · Cited by 84 · see citing works
Razavi, R., Klein, S., & Claußen, H. (2010) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2010.5671622 · Cited by 81 · see citing works
FROMS: Feedback Routing for Optimizing Multiple Sinks in WSN with Reinforcement Learning
Forster, A., & Murphy, A. L. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information · doi:10.1109/issnip.2007.4496872 · Cited by 81 · see citing works
Kawamoto, Y., Nishiyama, H., Fadlullah, Z. M., & Kato, N. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2262676 · Cited by 80 · see citing works
Self-citationA Novel Scheme for WSAN Sink Mobility Based on Clustering and Set Packing Techniques
Nakayama, H., Fadlullah, Z. M., Ansari, N., & Kato, N. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2163872 · Cited by 80 · see citing works
Self-citationTraining Recurrent Neural Networks
Sutskever, I. (2013) · Cited by 79 · see citing works
A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data
Gao, S., Huacan, P., Gallinari, P., Guo, J. H., & Kato, N. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2684160 · Cited by 76 · see citing works
Self-citationYu, J., Weng, K., Liang, G., & Xie, G. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739623 · Cited by 75 · see citing works
A fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks
Razavi, R., Klein, S., & Claußen, H. (2010) · Bell Labs Technical Journal · doi:10.1002/bltj.20463 · Cited by 73 · see citing works
Intrusion detection system (IDS) for combating attacks against cognitive radio networks
Fadlullah, Z. M., Nishiyama, H., Kato, N., & Fouda, M. M. (2013) · IEEE Network · doi:10.1109/mnet.2013.6523809 · Cited by 72 · see citing works
Self-citationLocalization Using Neural Networks in Wireless Sensor Networks
Shareef, A., Zhu, Y., & Musavi, M. T. (2008) · doi:10.4108/icst.mobilware2008.2901 · Cited by 71 · see citing works
der Merwe, J. V., Cáceres, R., Chu, Y.-H., & Sreenan, C. J. (2000) · ACM SIGCOMM Computer Communication Review · doi:10.1145/505672.505678 · Cited by 71 · see citing works
Reinforcement learning strategies for self-organized coverage and capacity optimization
Islam, M. N. U., & Mitschele‐Thiel, A. (2012) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2012.6214281 · Cited by 69 · see citing works
Behavioral authentication of server flows
Early, J. P., Brodley, C. E., & Rosenberg, C. (2004) · Annual Computer Security Applications Conference · doi:10.1109/csac.2003.1254309 · Cited by 68 · see citing works
Reducing the data transmission in Wireless Sensor Networks using the Principal Component Analysis
Rooshenas, A., Rabiee, H. R., Movaghar, A., & Naderi, M. Y. (2010) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2010.5706781 · Cited by 68 · see citing works
Zhang, L., & Thomopoulos, S. C. A. (1989) · doi:10.1109/ijcnn.1989.118375 · Cited by 66 · see citing works
Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks
Ni, S., & Weng, W. (2009) · Physical Review E · doi:10.1103/physreve.79.016111 · Cited by 65 · see citing works
Q-Probabilistic Routing in Wireless Sensor Networks
Arroyo-Valles, R., Alaiz-Rodriguez, R., Guerrero-Curieses, A., & Cid-Sueiro, J. (2007) · 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information · doi:10.1109/issnip.2007.4496810 · Cited by 65 · see citing works
Consensus-based distributed principal component analysis in wireless sensor networks
Macua, S. V., Belanović, P., & Zazo, S. (2010) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2010.5671089 · Cited by 64 · see citing works
Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network
SHEN, Y., & Wang, M. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.10.024 · Cited by 61 · see citing works
An Infinite Restricted Boltzmann Machine
Côté, M.-A., & Larochelle, H. (2016) · Neural Computation · doi:10.1162/neco_a_00848 · Cited by 60 · see citing works
Reinforcement Learning Based Geographic Routing Protocol for UWB Wireless Sensor Network
Dong, S., Agrawal, P., & Sivalingam, K. M. (2007) · doi:10.1109/glocom.2007.127 · Cited by 59 · see citing works
Traffic sign recognition with convolutional neural network based on max pooling positions
Qian, R., Yue, Y., Coenen, F., & Zhang, B. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603237 · Cited by 58 · see citing works
Suto, K., Nishiyama, H., Kato, N., & Huang, C. (2015) · IEEE Access · doi:10.1109/access.2015.2443171 · Cited by 57 · see citing works
Self-citationDirani, M., & Altman, Z. (2010) · Cited by 56 · see citing works
Autonomic network management principles : from concepts to applications
Agoulmine, N. (2011) · Cited by 52 · see citing works
Pasquale, G., Ciliberto, C., Rosasco, L., & Natale, L. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759720 · Cited by 51 · see citing works
Zhang, Y., Meratnia, N., & Havinga, P. (2008) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2008.4761978 · Cited by 50 · see citing works
Large-Scale Feature Learning With Spike-and-Slab Sparse CodingPreprint
Goodfellow, I., Courville, A. C., & Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6407 · Cited by 49 · see citing works
Deep Learning Applied to Mobile Phone Data for Individual Income Classification
Sundsøy, P., Bjelland, J., Reme, B., M.Iqbal, A., & Jahani, E. (2016) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.2991/icaita-16.2016.24 · Cited by 48 · see citing works
A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks
Masiero, R., Quer, G., Rossi, M., & Zorzi, M. (2009) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt.2009.5345599 · Cited by 47 · see citing works
Flagged first, then by citations
14 of the 266 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,111 · see citing works
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,847 · see citing works
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566 · see citing works
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works
“In this regard, there are many potential areas where DL can be applied, including online content streaming [245], routing and traffic control [246] [247], etc.”
BackgroundDeep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471 · see citing works
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,312 · see citing works
Emerging Artificial Intelligence Methods in Structural Engineering
Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068 · see citing works
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Network Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 945 · see citing works
Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
Huang, H., Yang, J., Huang, H., Song, Y., & Gui, G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2851783 · Cited by 821 · see citing works
Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios
Wang, Y., Liu, M., Yang, J., & Gui, G. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2900460 · Cited by 713 · see citing works
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700 · see citing works
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678 · see citing works
A Deep Reinforcement Learning Network for Traffic Light Cycle Control
Liang, X., Du, X., Wang, G., & Han, Z. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890726 · Cited by 676 · see citing works
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654 · see citing works
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. A., Elkhatib, Y., Hussain, A., & Al‐Fuqaha, A. (2019) · IEEE Access · doi:10.1109/access.2019.2916648 · Cited by 621 · see citing works
Networking and Communications in Autonomous Driving: A Survey
Wang, J., Liu, J., & Kato, N. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2888904 · Cited by 535 · see citing works
Self-citationThirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 557 · see citing works
Deep Learning for an Effective Nonorthogonal Multiple Access Scheme
Gui, G., Huang, H., Song, Y., & Sari, H. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2848294 · Cited by 505 · see citing works
Deep Learning and Medical Diagnosis: A Review of Literature
Bakator, M., & Radosav, D. (2018) · Multimodal Technologies and Interaction · doi:10.3390/mti2030047 · Cited by 509 · see citing works
Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
Huang, H., Song, Y., Yang, J., Gui, G., & Adachi, F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2893928 · Cited by 476 · see citing works
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Wang, J., Zhao, L., Liu, J., & Kato, N. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902661 · Cited by 450 · see citing works
Self-citationMachine Learning for Networking: Workflow, Advances and Opportunities
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2017) · IEEE Network · doi:10.1109/mnet.2017.1700200 · Cited by 448 · see citing works
Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey
Abbasi, M., Shahraki, A., & Taherkordi, A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.01.021 · Cited by 464 · see citing works
Sharma, S. K., & Wang, X. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916177 · Cited by 440 · see citing works
Machine learning driven smart electric power systems: Current trends and new perspectives
Ibrahim, M. S., Dong, W., & Yang, Q. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115237 · Cited by 437 · see citing works
Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019) · IEEE Access · doi:10.1109/access.2019.2942390 · Cited by 421 · see citing works
Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
Kato, N., Fadlullah, Z. M., Tang, F., Mao, B., Tani, S., Okamura, A., & Liu, J. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1800365 · Cited by 407 · see citing works
Self-citationTen Challenges in Advancing Machine Learning Technologies toward 6G
Kato, N., Mao, B., Tang, F., Kawamoto, Y., & Liu, J. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900476 · Cited by 395 · see citing works
Self-citationHolistic Network Virtualization and Pervasive Network Intelligence for 6G
Shen, X., Gao, J., Wu, W. R., Li, M., Zhou, C., & Zhuang, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3135829 · Cited by 395 · see citing works
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
Azmoodeh, A., Dehghantanha, A., & Choo, K. R. (2018) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2809665 · Cited by 379 · see citing works
IoT Connectivity Technologies and Applications: A Survey
Ding, J., Nemati, M., Ranaweera, C., & Choi, J. (2020) · IEEE Access · doi:10.1109/access.2020.2985932 · Cited by 377 · see citing works
Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning
Tan, L. T., & Hu, R. Q. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2867191 · Cited by 356 · see citing works
Thakkar, A., & Lohiya, R. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09496-0 · Cited by 368 · see citing works
Mao, B., Fadlullah, Z. M., Tang, F., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Transactions on Computers · doi:10.1109/tc.2017.2709742 · Cited by 350 · see citing works
Self-citationAI-Assisted Network-Slicing Based Next-Generation Wireless Networks
Shen, X., Gao, J., Wu, W. R., Lyu, K., Li, M., Zhuang, W., Li, X., & Rao, J. B. (2020) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2020.2965100 · Cited by 343 · see citing works
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020) · Electronics · doi:10.3390/electronics9071177 · Cited by 378 · see citing works
Benzaïd, C., & Taleb, T. (2020) · IEEE Network · doi:10.1109/mnet.001.1900252 · Cited by 315 · see citing works
Blockchain and Machine Learning for Communications and Networking Systems
Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975911 · Cited by 325 · see citing works
AI Models for Green Communications Towards 6G
Mao, B., Tang, F., Kawamoto, Y., & Kato, N. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3130901 · Cited by 311 · see citing works
Self-citationA survey on application of machine learning for Internet of Things
Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0834-5 · Cited by 304 · see citing works
Wu, Y., Ni, K., Zhang, C., Qian, L., & Tsang, D. H. K. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2875337 · Cited by 292 · see citing works
Fast Beamforming Design via Deep Learning
Huang, H., Peng, Y., Yang, J., Xia, W., & Gui, G. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2949122 · Cited by 282 · see citing works
Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications
Anwar, M. Z., Kaleem, Z., & Jamalipour, A. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2893615 · Cited by 276 · see citing works
Tang, F., Fadlullah, Z. M., Mao, B., & Kato, N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2838574 · Cited by 274 · see citing works
Self-citationLightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing
Wang, Y., Yang, J., Liu, M., & Gui, G. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2971001 · Cited by 273 · see citing works
Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing
Zhou, Z., Liao, H., Gu, B., Huq, K. M. S., Mumtaz, S., & Rodriguez, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700442 · Cited by 260 · see citing works
Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC
Liu, M., Song, T., & Gui, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2876152 · Cited by 264 · see citing works
Machine learning in cybersecurity: a comprehensive survey
Dasgupta, D., Akhtar, Z., & Sen, S. (2020) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/1548512920951275 · Cited by 262 · see citing works
Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling
Ning, Z., Huang, J., Wang, X., Rodrigues, J. J. P. C., & Guo, L. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800309 · Cited by 260 · see citing works
A Gentle Introduction to Reinforcement Learning and its Application in Different Fields
Naeem, M., Rizvi, S. T. H., & Coronato, A. (2020) · IEEE Access · doi:10.1109/access.2020.3038605 · Cited by 264 · see citing works
Tang, F., Mao, B., Fadlullah, Z. M., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1700244 · Cited by 247 · see citing works
Self-citationRodrigues, T. K., Suto, K., Nishiyama, H., Liu, J., & Kato, N. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2943405 · Cited by 246 · see citing works
Self-citationScope of machine learning in materials research—A review
Mobarak, M. H., Mimona, M. A., Islam, M. A., Hossain, N., Zohura, F. T., Imtiaz, I., & Rimon, M. I. H. (2023) · Applied Surface Science Advances · doi:10.1016/j.apsadv.2023.100523 · Cited by 243 · see citing works
A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks
Deebak, B. D., & Al-Turjman, F. M. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.102022 · Cited by 227 · see citing works
Unsupervised Learning-Based Fast Beamforming Design for Downlink MIMO
Huang, H., Xia, W., Xiong, J., Yang, J., Zheng, G., & Zhu, X. (2018) · IEEE Access · doi:10.1109/access.2018.2887308 · Cited by 224 · see citing works
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Elbadawi, M., Muñiz, B., Gavins, F. K. H., Ong, J. J., Gaisford, S., Pérez, G., Basit, A. W., Cabalar, P., & Goyanes, Á. (2020) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2020.119837 · Cited by 231 · see citing works
Sun, J., Shi, W.-J., Yang, Z., Yang, J., & Gui, G. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2925562 · Cited by 222 · see citing works
Cost-Efficient Service Function Chain Orchestration for Low-Latency Applications in NFV Networks
Sun, G., Zhu, G., Liao, D., Yu, H., Du, X., & Guizani, M. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2879883 · Cited by 221 · see citing works
Tang, F., Mao, B., Kato, N., & Gui, G. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3089688 · Cited by 221 · see citing works
Self-citationTang, F., Zhou, Y., & Kato, N. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3005495 · Cited by 216 · see citing works
Self-citationA Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues
Abdulkareem, K. H., Mohammed, M. A., Gunasekaran, S. S., Al‐Mhiqani, M. N., Mutlag, A. A., Mostafa, S. A., Ali, N. S., & Ibrahim, D. A. (2019) · IEEE Access · doi:10.1109/access.2019.2947542 · Cited by 216 · see citing works
Routing optimization meets Machine Intelligence: A perspective for the future network
Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., & Xu, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.093 · Cited by 215 · see citing works
Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
Hu, X., Liu, Z., Yu, X., Zhao, Y., Chen, W., Hu, B., Du, X., Li, X., Helaoui, M., Wang, W., et al. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3054867 · Cited by 203 · see citing works
ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications
Chen, X., Shi, Q., Yang, L., & Xu, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700145 · Cited by 201 · see citing works
Tang, F., Mao, B., Kawamoto, Y., & Kato, N. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3073009 · Cited by 198 · see citing works
Self-citationAhmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197 · see citing works
Convergence of Satellite and Terrestrial Networks: A Comprehensive Survey
Wang, P., Zhang, J., Zhang, X., Yan, Z., Evans, B. G., & Wang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2963223 · Cited by 195 · see citing works
An improved anomaly detection model for IoT security using decision tree and gradient boosting
Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04783-y · Cited by 194 · see citing works
Liu, M., Song, T., Hu, J., Yang, J., & Gui, G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2883669 · Cited by 191 · see citing works
Anomaly-based intrusion detection system for IoT application
Bhavsar, M., Roy, K., Kelly, J. C., & Odeyomi, O. T. (2023) · Discover Internet of Things · doi:10.1007/s43926-023-00034-5 · Cited by 187 · see citing works
DROM: Optimizing the Routing in Software-Defined Networks With Deep Reinforcement Learning
Yu, C., Lan, J., Guo, Z., & Hu, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2877686 · Cited by 186 · see citing works
Gumusbas, D., Yıldırım, T., Genovese, A., & Scotti, F. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.2992966 · Cited by 183 · see citing works
Ning, Z., Dong, P., Wang, X., Guo, L., Rodrigues, J. J. P. C., Kong, X., Huang, J., & Kwok, R. Y. K. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2930521 · Cited by 181 · see citing works
Tang, F., Hofner, H., Kato, N., Kaneko, K., Yamashita, Y., & Hangai, M. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126073 · Cited by 181 · see citing works
Self-citationZhao, Y., Li, Y., Zhang, X., Geng, G., Zhang, W., & Sun, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2928564 · Cited by 176 · see citing works
Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Barja-Martínez, S., Aragüés‐Peñalba, M., Munné‐Collado, Í., Lloret-Gallego, P., Bullich‐Massagué, E., & Villafáfila‐Robles, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111459 · Cited by 187 · see citing works
AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges
Al-Doghman, F., Moustafa, N., Khalil, I., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3155447 · Cited by 179 · see citing works
Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
Nancy, A. A., Ravindran, D., Vincent, P. M. D. R., Srinivasan, K., & Hu, Y. (2021) · Sensors · doi:10.3390/s22010196 · Cited by 177 · see citing works
Intelligent Routing Based on Reinforcement Learning for Software-Defined Networking
Casas-Velasco, D. M., Rendón, O. M. C., & da Fonseca, N. L. S. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3036911 · Cited by 170 · see citing works
Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities
Zhang, S., & Zhu, D. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107556 · Cited by 170 · see citing works
A survey of 5G network systems: challenges and machine learning approaches
Fourati, H., Mâaloul, R., & Fourati, L. C. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01178-4 · Cited by 166 · see citing works
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., Seneviratne, A., Li, Y., & Poor, H. V. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3024783 · Cited by 160 · see citing works
A survey of neural networks usage for intrusion detection systems
Drewek-Ossowicka, A., Pietrołaj, M., & Rumiński, J. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02014-x · Cited by 157 · see citing works
Deep Neural Network for Robust Modulation Classification Under Uncertain Noise Conditions
Hu, S., Pei, Y., Liang, P. P., & Liang, Y. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2951594 · Cited by 155 · see citing works
Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT
Guo, X., Lin, H., Li, Z., & Peng, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2960033 · Cited by 155 · see citing works
A survey on Zero touch network and Service Management (ZSM) for 5G and beyond networks
Liyanage, M., Pham, Q., Dev, K., Bhattacharya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Yenduri, G. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103362 · Cited by 152 · see citing works
Distributed and efficient object detection in edge computing: Challenges and solutions
Ren, J., Guo, Y., Zhang, D., Liu, Q., & Zhang, Y. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700415 · Cited by 148 · see citing works
Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things
Li, S., Zhang, N., Lin, S., Kong, L., Katangur, A. K., Khan, M. K., Ni, M., & Zhu, G. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700163 · Cited by 149 · see citing works
Chen, C.-H., Lin, M., & Liu, C.-C. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700146 · Cited by 149 · see citing works
Dual Cross-Entropy Loss for Small-Sample Fine-Grained Vehicle Classification
Li, X., Yu, L., Chang, D., Ma, Z., & Cao, J. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2895651 · Cited by 148 · see citing works
Zhao, F., Zeng, G., & Lu, K. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2952605 · Cited by 146 · see citing works
Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities
Yao, H., Gao, P., Wang, J., Zhang, P., Jiang, C., & Han, Z. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2901348 · Cited by 144 · see citing works
Efficient and Privacy-Preserving Truth Discovery in Mobile Crowd Sensing Systems
Xu, G., Li, H., Liu, S., Wen, M., & Lu, R. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2895834 · Cited by 142 · see citing works
Deep Learning-Based Cooperative Automatic Modulation Classification Method for MIMO Systems
Wang, Y., Wang, J., Zhang, W., Yang, J., & Gui, G. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2976942 · Cited by 139 · see citing works
Amma, N. G. B., & Selvakumar, S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.020 · Cited by 141 · see citing works
Butt, F. A., Chattha, J. N., Ahmad, J., Zia, M. U., Rizwan, M., & Naqvi, I. H. (2022) · IEEE Access · doi:10.1109/access.2022.3145972 · Cited by 140 · see citing works
Fine-Grained Vehicle Classification With Channel Max Pooling Modified CNNs
Ma, Z., Chang, D., Xie, J., Ding, Y., Wen, S., Li, X., Si, Z., & Guo, J. H. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2899972 · Cited by 140 · see citing works
A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing
Wang, J.-B., Wang, J., Wu, Y., Wang, J., Zhu, H., Lin, M., & Wang, J. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700293 · Cited by 137 · see citing works
- Authors
- Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K.
- Affiliations
- Tohoku University (JP)
- NTT (Japan) (JP)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2017
- Volume
- 19
- Issue
- 4
- Pages
- 2432-2455
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2617931713
- Cited by
- 810
- References
- 266
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 82 · 2021: 122 · 2020: 170 · 2019: 135 · 2018: 80 · 2017: 10
- FWCI
- 71.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (16H05858)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- deep learning, network traffic management, intelligent routing, autonomous network management, deep learning architectures, network traffic growth, packet-switched networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
