Applications of artificial intelligence and machine learning in smart cities

High-performance medicine: the convergence of human and artificial intelligence

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Dropout as a bayesian approximation: Representing model uncertainty in deep learning

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International evaluation of an AI system for breast cancer screening

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Advances in neural information processing systems 11

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Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000 · see citing works

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

Zhang, J., Zheng, Y., & Qi, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10735 · Cited by 2,396 · see citing works

Current trends in Smart City initiatives: Some stylised facts

Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014) · Cities · doi:10.1016/j.cities.2013.12.010 · Cited by 2,322 · see citing works

Artificial intelligence in cancer imaging: Clinical challenges and applications

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LSTM network: a deep learning approach for short‐term traffic forecast

Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2016.0208 · Cited by 1,842 · see citing works

Actor-critic algorithms

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Big data and machine learning algorithms for health-care delivery

Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · 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

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang, Y., Chen, Q., Hong, T., & Kang, C. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2818167 · Cited by 1,384 · see citing works

On big data, artificial intelligence and smart cities

Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178 · see citing works

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48995-4 · Cited by 1,110 · see citing works

Distributed attack detection scheme using deep learning approach for Internet of Things

Diro, A. A., & Chilamkurti, N. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,100 · see citing works

Big Data Analytics in Intelligent Transportation Systems: A Survey

Zhu, L., Yu, F. R., Wang, Y., Ning, B., & Tang, T. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2815678 · Cited by 1,079 · see citing works

2018 IEEE International Conference on Communications (ICC)

(2018) · doi:10.1109/icc34414.2018 · Cited by 1,042 · see citing works

Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works

Deep Reinforcement Learning Based Resource Allocation for V2V Communications

Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870 · see citing works

Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience

Chen, M., Mozaffari, M., Saad, W., Yin, C., Debbah, M., & Hong, C. S. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2680898 · Cited by 742 · see citing works

Healthfog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments

Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.10.043 · Cited by 669 · see citing works

Artificial intelligence-enabled healthcare delivery

Reddy, S., Fox, J., & Purohit, M. P. (2019) · Journal of the Royal Society of Medicine · doi:10.1177/0141076818815510 · Cited by 635 · see citing works

Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800254 · Cited by 595 · see citing works

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network

Zhu, F., Ye, F., Fu, Y., Liu, Q., & Shen, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-42516-z · Cited by 542 · see citing works

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Hossain, E., Khan, I., Un-Noor, F., Sikander, S. S., & Sunny, M. S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2894819 · Cited by 514 · see citing works

Redefining the Smart City: Culture, Metabolism and Governance

Allam, Z., & Newman, P. W. G. (2018) · Smart Cities · doi:10.3390/smartcities1010002 · Cited by 508 · see citing works

Putting the 'smarts' into the smart grid

Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012) · Communications of the ACM · doi:10.1145/2133806.2133825 · Cited by 465 · see citing works

Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

Challita, U., Saad, W., & Bettstetter, C. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2900035 · Cited by 441 · see citing works

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

Huang, H., Guo, S., Gui, G., Yang, Z., Zhang, J., Sari, H., & Adachi, F. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1900027 · Cited by 433 · see citing works

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410 · see citing works

Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends

Veres, M., & Moussa, M. A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2929020 · Cited by 398 · see citing works

Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Lu, R., & Hong, S. H. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.12.061 · Cited by 397 · see citing works

An intrusion detection system for connected vehicles in smart cities

Aloqaily, M., Otoum, S., Ridhawi, I. A., & Jararweh, Y. (2019) · Ad Hoc Networks · doi:10.1016/j.adhoc.2019.02.001 · Cited by 394 · see citing works

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach

Liu, X., Liu, Y., Chen, Y., & Hanzo, L. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2920284 · Cited by 359 · see citing works

DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids

Ferrag, M. A., & Maglaras, L. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2922936 · Cited by 358 · see citing works

A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K. R., & Leung, H. (2019) · IEEE Access · doi:10.1109/access.2019.2920326 · Cited by 357 · see citing works

Security and privacy challenges in smart cities

Braun, T., Fung, B. C. M., Iqbal, F., & Shah, B. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.02.039 · Cited by 354 · see citing works

A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks

Zhang, L., Zhao, H. M., Hou, S., Zhao, Z. W., Xu, H., Wu, X., Wu, Q., & Zhang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2929241 · Cited by 352 · see citing works

5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions

Ahad, A., Tahir, M., & Yau, K.-L. A. (2019) · IEEE Access · doi:10.1109/access.2019.2930628 · Cited by 352 · see citing works

2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)

(2017) · doi:10.1109/ccnc38614.2017 · Cited by 345 · see citing works

Intelligence in the Internet of Medical Things era: A systematic review of current and future trends

Al-Turjman, F. M., Nawaz, M. H., & Uluşar, Ü. D. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.12.030 · Cited by 338 · see citing works

AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning

Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R. S., Zohdy, M. A., & Ming, H. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666450 · Cited by 328 · see citing works

Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms

Petrolo, R., Loscrì, V., & Mitton, N. (2015) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.2931 · Cited by 319 · see citing works

A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks

Maimó, L. F., Gómez, Á. L. P., Clemente, F. J. G., Pérez, M. G., & Pérez, G. M. (2018) · IEEE Access · doi:10.1109/access.2018.2803446 · Cited by 302 · see citing works

Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning

Liu, C. H., Ma, X., Gao, X., & Tang, J. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2908171 · Cited by 302 · see citing works

Deep Reinforcement Learning for Vehicular Edge Computing

Ning, Z., Dong, P., Wang, X., Rodrigues, J. J. P. C., & Xia, F. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3317572 · Cited by 296 · see citing works

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M. U., Cheung, K. W., Tonkoski, R., Hovsapian, R. O., Myers, K. S., Zhang, R., Zhao, P. Q., et al. (2019) · IET Smart Grid · doi:10.1049/iet-stg.2018.0261 · Cited by 292 · see citing works

2019 Chinese Control Conference (CCC)

(2019) · doi:10.23919/ccc47130.2019 · Cited by 277 · see citing works

Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

Shihavuddin, A., Chen, X. D., Fedorov, V., Riis, N. A. B., Christensen, A. N., Branner, K., Dahl, A. B., & Paulsen, R. R. (2019) · Energies · doi:10.3390/en12040676 · Cited by 265 · see citing works

2018 27th International Conference on Computer Communication and Networks (ICCCN)

(2018) · doi:10.1109/icccn43666.2018 · Cited by 264 · see citing works

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

Fallah, N., Deo, R. C., Shojafar, M., Conti, M., & Shamshirband, S. (2018) · Energies · doi:10.3390/en11030596 · Cited by 255 · see citing works

Health intelligence: how artificial intelligence transforms population and personalized health

Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0058-9 · Cited by 252 · see citing works

Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

Dhahri, H., Maghayreh, E. A., Mahmood, A., Elkilani, W. S., & Faisal, M. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/4253641 · Cited by 240 · see citing works

Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs

Challita, U., Ferdowsi, A., Chen, M., & Saad, W. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1800155 · Cited by 239 · see citing works

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Wu, M., Song, Z., & Moon, Y. B. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-017-1315-5 · Cited by 238 · see citing works

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

Nguyen, V. N., Jenssen, R., & Roverso, D. (2019) · IEEE Power and Energy Technology Systems Journal · doi:10.1109/jpets.2018.2881429 · Cited by 226 · see citing works

Demand response algorithms for smart-grid ready residential buildings using machine learning models

Pallonetto, F., Роса, М. Д., Milano, F., & Finn, D. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.02.020 · Cited by 215 · see citing works

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview

Ferdowsi, A., Challita, U., & Saad, W. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2018.2883777 · Cited by 191 · see citing works

A Dynamic Service Migration Mechanism in Edge Cognitive Computing

Chen, M., Li, W., Fortino, G., Hao, Y., Hu, L., & Humar, I. (2019) · ACM Transactions on Internet Technology · doi:10.1145/3239565 · Cited by 185 · see citing works

When UAV Swarm Meets Edge-Cloud Computing: The QoS Perspective

Chen, W., Liu, B., Huang, H., Guo, S., & Zheng, Z. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800222 · Cited by 184 · see citing works

An overview of GeoAI applications in health and healthcare

Boulos, M. N. K., Peng, G., & VoPham, T. (2019) · International Journal of Health Geographics · doi:10.1186/s12942-019-0171-2 · Cited by 183 · see citing works

Future generation 5G wireless networks for smart grid: a comprehensive review

S, S. R., Dragičević, T., Siano, P., & Prabaharan, S. R. S. (2019) · Energies · doi:10.3390/en12112140 · Cited by 162 · see citing works

HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems

Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931716 · Cited by 149 · see citing works

Detection of GPS Spoofing Attacks on Unmanned Aerial Systems

Manesh, M. R., Kenney, J., Hu, W. C., et al. (2019) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc.2019.8651804 · Cited by 148 · see citing works

Deep Reinforcement Learning for Interference-Aware Path Planning of Cellular-Connected UAVs

Challita, U., Saad, W., & Bettstetter, C. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422706 · Cited by 145 · see citing works

IoT Architecture for Smart Grids

Shahinzadeh, H., Moradi, J., Gharehpetian, G. B., et al. (2019) · doi:10.1109/ipaps.2019.8641944 · Cited by 139 · see citing works

Trajectory Optimization for Autonomous Flying Base Station via Reinforcement Learning

Bayerlein, H., de Kerret, P., & Gesbert, D. (2018) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2018.8445768 · Cited by 137 · see citing works

Citizen-centric data services for smarter cities

Aguilera, U., Peña, O., Belmonte, Ó., & López–de–Ipiña, D. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.10.031 · Cited by 134 · see citing works

Sensing, communication and security planes: A new challenge for a smart city system design

Habibzadeh, H., Soyata, T., Kantarcı, B., Boukerche, A., & Kaptan, C. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.08.001 · Cited by 131 · see citing works

Survey on Machine Learning and Deep Learning Algorithms used in Internet of Things (IoT) Healthcare

Durga, S., Nag, R., & Daniel, E. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819806 · Cited by 125 · see citing works

2019 3rd International Conference on Computing Methodologies and Communication (ICCMC)

(2019) · doi:10.1109/iccmc44992.2019 · Cited by 125 · see citing works

Big Data Issues in Smart Grids: A Survey

Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2931879 · Cited by 123 · see citing works

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

(2020) · doi:10.1109/smartgridcomm47815.2020 · Cited by 121 · see citing works

Toward Privacy and Regulation in Blockchain-Based Cryptocurrencies

Li, Y., Susilo, W., Yang, G., Yu, Y. H., Du, X., Liu, D., & Guizani, N. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800271 · Cited by 120 · see citing works

SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications

Alli, A. A., & Alam, M. M. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100070 · Cited by 120 · see citing works

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Mujeeb, S., Javaid, N., Ilahi, M., Wadud, Z., Ishmanov, F., & Afzal, M. K. (2019) · Sustainability · doi:10.3390/su11040987 · Cited by 117 · see citing works

Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory

Zhang, Q., Saad, W., Bennis, M., Lu, X., Debbah, M. A., & Zuo, W. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3027624 · Cited by 115 · see citing works

2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)

(2019) · doi:10.1109/ccwc45194.2019 · Cited by 113 · see citing works

Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning

Al-Turjman, F. M., Zahmatkesh, H., & Mostarda, L. (2019) · IEEE Access · doi:10.1109/access.2019.2931637 · Cited by 112 · see citing works

Smart Cities Cybersecurity and Privacy

Yang, L., Elisa, N., & Eliot, N. (2018) · Elsevier eBooks · doi:10.1016/c2017-0-02545-4 · Cited by 106 · see citing works

An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

Rezvy, S., Luo, Y., Petridis, M., et al. (2019) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2019.8693059 · Cited by 105 · see citing works

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Li, K., Ni, W., Tovar, E., & Jamalipour, A. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2945037 · Cited by 105 · see citing works

2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

(2017) · doi:10.1109/iccad39534.2017 · Cited by 105 · see citing works

Information-centric sensor networks for cognitive IoT: an overview

Al-Turjman, F. M. (2016) · Annals of Telecommunications · doi:10.1007/s12243-016-0533-8 · Cited by 104 · see citing works

Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles

Liu, G., Xu, Y., He, Z., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2935463 · Cited by 103 · see citing works

Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention

Zack, C. J., Senecal, C., Kinar, Y., et al. (2019) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2019.02.035 · Cited by 100 · see citing works

Intelligence and security in big 5G-oriented IoNT: An overview

Al-Turjman, F. M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.08.009 · Cited by 100 · see citing works

Defending Against Data Integrity Attacks in Smart Grid: A Deep Reinforcement Learning-Based Approach

An, D., Yang, Q., Liu, W., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2933020 · Cited by 99 · see citing works

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

(2020) · doi:10.1109/ccece47787.2020 · Cited by 99 · see citing works

Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders

Toğaçar, M., Ergen, B., & Cömert, Z. (2019) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109503 · Cited by 95 · see citing works

TRec: an efficient recommendation system for hunting passengers with deep neural networks

Huang, Z., Shan, G., Cheng, J., & Sun, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3728-2 · Cited by 94 · see citing works

Deep Learning for Secure Mobile Edge Computing in Cyber-Physical Transportation Systems

Chen, Y., Zhang, Y., Maharjan, S., et al. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800458 · Cited by 93 · see citing works

Electricity Consumption Clustering Using Smart Meter Data

Tureczek, A. M., Nielsen, P. S., & Madsen, H. (2018) · Energies · doi:10.3390/en11040859 · Cited by 87 · see citing works

Machine learning for wearable IoT‐based applications: A survey

Al‐Turjman, F., & Baali, I. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3635 · Cited by 87 · see citing works

Enhanced Deployment Strategy for the 5G Drone-BS Using Artificial Intelligence

Al‐Turjman, F., Lemayian, J. P., Alturjman, S., & Mostarda, L. (2019) · IEEE Access · doi:10.1109/access.2019.2921729 · Cited by 78 · see citing works

2019 IEEE Texas Power and Energy Conference (TPEC)

(2019) · doi:10.1109/tpec45224.2019 · Cited by 75 · see citing works

Machine learning for 5G and beyond: From model-based to data-driven mobile wireless networks

Wang, T., Wang, S., & Zhou, Z. (2019) · China Communications · doi:10.12676/j.cc.2019.01.015 · Cited by 71 · see citing works

Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning

Chackravarthy, S., Schmitt, S., & Li, Y. (2018) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2018.00060 · Cited by 69 · see citing works

Autonomic machine learning platform

Lee, K. M., Yoo, J., Kim, S., Lee, J.-H., & Hong, J. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.07.003 · Cited by 66 · see citing works

UAV Positioning for Throughput Maximization Using Deep Learning Approaches

Munaye, Y. Y., Lin, H., Adege, A. B., & Tarekegn, G. B. (2019) · Sensors · doi:10.3390/s19122775 · Cited by 62 · see citing works

Intelligent Anomaly Detection for Large-scale Smart Grids

Karimipour, H., Geris, S., Dehghantanha, A., & Leung, H. (2019) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2019.8861995 · Cited by 58 · see citing works

Information-centric framework for the Internet of Things (IoT): Traffic modeling & optimization

Al-Turjman, F. M. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.018 · Cited by 53 · see citing works

Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing

Zhang, Q., Bai, C., Chen, Z., Li, P., Yu, H., Wang, S., & Gao, H. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5252 · Cited by 52 · see citing works

ISAAC: The Idaho CPS Smart Grid Cybersecurity Testbed

Oyewumi, I. A., Jillepalli, A. A., Richardson, P., et al. (2019) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec.2019.8662189 · Cited by 47 · see citing works

A Traffic-Aware Approach for Enabling Unmanned Aerial Vehicles (UAVs) in Smart City Scenarios

El‐Sayed, H., Chaqfa, M., Zeadally, S., & Puthal, D. (2019) · IEEE Access · doi:10.1109/access.2019.2922213 · Cited by 46 · see citing works

2018 28th International Telecommunication Networks and Applications Conference (ITNAC)

(2018) · doi:10.1109/itnac44607.2018 · Cited by 43 · see citing works

2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)

(2019) · doi:10.1109/snams47927.2019 · Cited by 43 · see citing works

2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)

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Malicious data deception attacks against power systems: A new case and its detection method

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