Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research
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Ying, 2021, Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance, Eur. Manag. J., 39, 390, 10.1016/j.emj.2020.04.001
Fuertes, G., Zamorano, J., Alfaro, M., Vargas, M., Sabattin, J., Duran, C., Ternero, R., and Rivera, R. (2022). Opportunities of the technological trends linked to Industry 4.0 for achieve sustainable manufacturing objectives. Sustainability, 14.
Saura, 2021, Using data sciences in digital marketing: Framework, methods, and performance metrics, J. Innov. Knowl., 6, 92, 10.1016/j.jik.2020.08.001
Mitchell, T.M. (1997). Machine Learning, McGraw-Hill.
Turing, A.M. (2009). Parsing the Turing Test, Springer.
McCarthy, J. (1958, January 24–27). Programs with common sense. Proceedings of the Symposium on Mechanisation, Teddington, UK.
Collins, 2021, Artificial intelligence in information systems research: A systematic literature review and research agenda, Int. J. Inf. Manag., 60, 102383, 10.1016/j.ijinfomgt.2021.102383
Huang, 2022, A data-driven WSN security threat analysis model based on cognitive computing, J. Sens., 2022, 5013905, 10.1155/2022/5013905
Sreedevi, 2022, Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review, Inf. Process. Manag., 59, 102888, 10.1016/j.ipm.2022.102888
Wu, 2022, Expert systems and risk analytics in service engineering, Expert Syst., 39, e12909, 10.1111/exsy.12909
Naser, 2021, Mechanistically informed machine learning and artificial intelligence in fire engineering and sciences, Fire Technol., 57, 2741, 10.1007/s10694-020-01069-8
Alam, 2022, Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects, Chem. Eng. J., 427, 130011, 10.1016/j.cej.2021.130011
Tolan, 2022, Market power and artificial intelligence work on online labour markets, Res. Policy, 51, 104446, 10.1016/j.respol.2021.104446
Fuertes, 2020, Measure of semantic likeness among business process activities in a telecommunication company, IEEE Access, 8, 32332, 10.1109/ACCESS.2019.2953464
Yaqoob, 2021, Blockchain for healthcare data management: Opportunities, challenges, and future recommendations, Neural Comput. Appl., 34, 11475, 10.1007/s00521-020-05519-w
Izonin, 2021, An approach towards missing data management using improved GRNN-SGTM ensemble method, Eng. Sci. Technol. Int. J., 24, 749
Zhuang, 2021, Digital twin-based assembly data management and process traceability for complex products, J. Manuf. Syst., 58, 118, 10.1016/j.jmsy.2020.05.011
Wang, 2021, A survey on trajectory data management, analytics, and learning, ACM Comput. Surv., 54, 1
Kong, 2021, Data construction method for the applications of workshop digital twin system, J. Manuf. Syst., 58, 323, 10.1016/j.jmsy.2020.02.003
Putz, 2021, EtherTwin: Blockchain-based secure digital twin information management, Inf. Process. Manag., 58, 102425, 10.1016/j.ipm.2020.102425
Wang, 2021, Towards missing electric power data imputation for energy management systems, Expert Syst. Appl., 174, 114743, 10.1016/j.eswa.2021.114743
Pan, 2021, A BIM-data mining integrated digital twin framework for advanced project management, Autom. Constr., 124, 103564, 10.1016/j.autcon.2021.103564
Liu, 2021, Enterprise human resource management platform based on FPGA and data mining, Microprocess. Microsyst., 80, 103330, 10.1016/j.micpro.2020.103330
Jiang, 2021, Data analytics of social media publicity to enhance household waste management, Resour. Conserv. Recycl., 164, 105146, 10.1016/j.resconrec.2020.105146
Hashmi, 2021, Internet of things and cloud computing-based energy management system for demand side management in smart grid, Int. J. Energy Res., 45, 1007, 10.1002/er.6141
Shao, 2021, Multistage implementation framework for smart supply chain management under industry 4.0, Technol. Forecast. Soc. Change, 162, 120354, 10.1016/j.techfore.2020.120354
Selfridge, O.G. (1955). Pattern recognition and modern computers. Proc. West. Jt. Comput. Conf., 91–93.
Biggio, 2018, Wild patterns: Ten years after the rise of adversarial machine learning, Pattern Recognit., 84, 317, 10.1016/j.patcog.2018.07.023
Shamir, L., Delaney, J.D., Orlov, N., Eckley, D.M., and Goldberg, I.G. (2010). Pattern recognition software and techniques for biological image analysis. PLoS Comput. Biol., 6.
Salehi, 2018, Emerging artificial intelligence methods in structural engineering, Eng. Struct., 171, 170, 10.1016/j.engstruct.2018.05.084
Zhang, 2018, Intelligent computing system based on pattern recognition and data mining algorithms, Sustain. Comput. Inform. Syst., 20, 192
Paolanti, 2020, Multidisciplinary pattern recognition applications: A review, Comput. Sci. Rev., 37, 100276, 10.1016/j.cosrev.2020.100276
Wu, 2019, Deep attention-based spatially recursive networks for fine-grained visual recognition, IEEE Trans. Cybern., 49, 1791, 10.1109/TCYB.2018.2813971
Lu, 2019, Myoelectric pattern recognition for controlling a robotic hand: A feasibility study in stroke, IEEE Trans. Biomed. Eng., 66, 365, 10.1109/TBME.2018.2840848
Hu, 2021, Hierarchical pattern recognition for tourism demand forecasting, Tour. Manag., 84, 104263, 10.1016/j.tourman.2020.104263
Cao, X., You, G., Li, H., Li, D., Wang, M., Ren, X., Cao, X., You, G., Li, H., and Li, D. (2019). Comparative investigation for rotten xylem (kuqin) and strip types (tiaoqin) of scutellaria baicalensis georgi based on fingerprinting and chemical pattern recognition. Molecules, 24.
Shi, 2020, Computer vision-based grasp pattern recognition with application to myoelectric control of dexterous hand prosthesis, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 2090, 10.1109/TNSRE.2020.3007625
Bulgarevich, 2018, Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures, Sci. Rep., 8, 2078, 10.1038/s41598-018-20438-6
Entezami, A., Sarmadi, H., Behkamal, B., and Mariani, S. (2020). Big data analytics and structural health monitoring: A statistical pattern recognition-based approach. Sensors, 20.
Cao, 2020, Habit2vec: Trajectory semantic embedding for living pattern recognition in population, IEEE Trans. Mob. Comput., 19, 1096, 10.1109/TMC.2019.2902403
Ignatov, 2018, Real-time human activity recognition from accelerometer data using convolutional neural networks, Appl. Soft Comput., 62, 915, 10.1016/j.asoc.2017.09.027
Lu, W., Teng, J., Zhou, Q., and Peng, Q. (2018). Stress prediction for distributed structural health monitoring using existing measurements and pattern recognition. Sensors, 18.
Seo, 2020, Artificial van der waals hybrid synapse and its application to acoustic pattern recognition, Nat. Commun., 11, 3936, 10.1038/s41467-020-17849-3
Cao, X., Sun, L., Li, D., You, G., Wang, M., and Ren, X. (2018). Quality evaluation of phellodendri chinensis cortex by fingerprint–chemical pattern recognition. Molecules, 23.
Wang, 2019, Deep learning for sensor-based activity recognition: A survey, Pattern Recognit. Lett., 119, 3, 10.1016/j.patrec.2018.02.010
Liu, 2017, Intent pattern recognition of lower-limb motion based on mechanical sensors, IEEE/CAA J. Autom. Sin., 4, 651, 10.1109/JAS.2017.7510619
Wen, 2019, A shape-based clustering method for pattern recognition of residential electricity consumption, J. Clean. Prod., 212, 475, 10.1016/j.jclepro.2018.12.067
Kong, 2020, PANNs: Large-scale pretrained audio neural networks for audio pattern recognition, IEEE/ACM Trans. Audio Speech Lang. Process., 28, 2880, 10.1109/TASLP.2020.3030497
Gan, 2016, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, Mech. Syst. Signal Process., 72–73, 92, 10.1016/j.ymssp.2015.11.014
Sharma, 2018, A pattern recognition algorithm for assessing trajectory completeness, Transp. Res. Part C Emerg. Technol., 96, 432, 10.1016/j.trc.2018.09.027
Ruiz-Tamayo, J., Vazquez-Lopez, J.A., Ruelas-Santoyo, E.A., Hernandez-Lopez, A., Lopez-Juarez, I., and Rios-Lira, A.J. (2021). Multivariate pattern recognition in MSPC using bayesian inference. Mathematics, 9.
Licen, S., Di Gilio, A., Palmisani, J., Petraccone, S., de Gennaro, G., and Barbieri, P. (2020). Pattern recognition and anomaly detection by self-organizing maps in a multi month e-nose survey at an industrial site. Sensors, 20.
Hafezi, 2019, A time-use activity-pattern recognition model for activity-based travel demand modeling, Transportation, 46, 1369, 10.1007/s11116-017-9840-9
Huang, 2018, Agricultural remote sensing big data: Management and applications, J. Integr. Agric., 17, 1915, 10.1016/S2095-3119(17)61859-8
Zan, 2020, Control chart pattern recognition using the convolutional neural network, J. Intell. Manuf., 31, 703, 10.1007/s10845-019-01473-0
Ng, 2019, Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework, Pattern Recognit., 88, 261, 10.1016/j.patcog.2018.11.022
Seo, 2018, Artificial optic-neural synapse for colored and color-mixed pattern recognition, Nat. Commun., 9, 5106, 10.1038/s41467-018-07572-5
2020, In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition, Mech. Syst. Signal Process., 136, 106526, 10.1016/j.ymssp.2019.106526
Addeh, 2018, Control chart pattern recognition using RBF neural network with new training algorithm and practical features, ISA Trans., 79, 202, 10.1016/j.isatra.2018.04.020
Teh, 2020, Understanding limb position and external load effects on real-time pattern recognition control in amputees, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 1605, 10.1109/TNSRE.2020.2991643
Melati, 2019, Mapping the global design space of nanophotonic components using machine learning pattern recognition, Nat. Commun., 10, 4775, 10.1038/s41467-019-12698-1
Ullah, 2017, Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing, J. Intell. Manuf., 28, 1285, 10.1007/s10845-015-1155-0
Pham, 2016, Estimation and generation of training patterns for control chart pattern recognition, Comput. Ind. Eng., 95, 72, 10.1016/j.cie.2016.02.016
Wang, 2018, An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition, Neurocomputing, 310, 213, 10.1016/j.neucom.2018.05.024
Abiodun, 2019, Comprehensive review of artificial neural network applications to pattern recognition, IEEE Access, 7, 158820, 10.1109/ACCESS.2019.2945545
Wang, 2019, A MobileNets convolutional neural network for gis partial discharge pattern recognition in the ubiquitous power internet of things context: Optimization, comparison, and application, IEEE Access, 7, 150226, 10.1109/ACCESS.2019.2946662
Hu, 2020, Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition, Energy, 198, 117298, 10.1016/j.energy.2020.117298
Chaitanya, 2021, Reliable islanding detection scheme for distributed generation based on pattern-recognition, IEEE Trans. Ind. Inform., 17, 5230, 10.1109/TII.2020.3029675
Roshani, 2021, Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness, Alex. Eng. J., 60, 1955, 10.1016/j.aej.2020.11.043
Xing, 2021, SteroidXtract: Deep learning-based pattern recognition enables comprehensive and rapid extraction of steroid-like metabolic features for automated biology-driven metabolomics, Anal. Chem., 93, 5735, 10.1021/acs.analchem.0c04834
Zhao, 2019, A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition, Ocean. Eng., 172, 456, 10.1016/j.oceaneng.2018.12.019
Zhang, 2020, Structural damage identification via physics-guided machine learning: A methodology integrating pattern recognition with finite element model updating, Struct. Health Monit., 20, 1675, 10.1177/1475921720927488
Demin, 2021, Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network, Neural Netw., 134, 64, 10.1016/j.neunet.2020.11.005
Syed, 2021, Deep learning-based short-term load forecasting approach in smart grid with clustering and consumption pattern recognition, IEEE Access, 9, 54992, 10.1109/ACCESS.2021.3071654
Ciucci, 2021, Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome, Nat. Commun., 12, 1926, 10.1038/s41467-021-22135-x
Li, 2021, Image pattern recognition in identification of financial bills risk management, Neural Comput. Appl., 33, 867, 10.1007/s00521-020-05261-3
Astolfi, 2021, Syntactic pattern recognition in computer vision, ACM Comput. Surv., 54, 1, 10.1145/3447241
Shu, 2021, An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box, Sci. Rep., 11, 2471, 10.1038/s41598-021-81115-9
Wei, 2021, Exploring the financial indicators to improve the pattern recognition of economic data based on machine learning, Neural Comput. Appl., 33, 723, 10.1007/s00521-020-05094-0
Urbaniak, 2021, Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network, Commun. Nonlinear Sci. Numer. Simul., 95, 105582, 10.1016/j.cnsns.2020.105582
Lin, 2021, Velocity prediction using Markov Chain combined with driving pattern recognition and applied to dual-motor electric vehicle energy consumption evaluation, Appl. Soft Comput., 101, 106998, 10.1016/j.asoc.2020.106998
Ehya, 2021, Pattern recognition of interturn short circuit fault in a synchronous generator using magnetic flux, IEEE Trans. Ind. Appl., 57, 3573, 10.1109/TIA.2021.3072881
Bai, 2021, Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments, Pattern Recognit., 120, 108102, 10.1016/j.patcog.2021.108102
Meng, 2021, A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern, J. Intell. Manuf., 32, 1147, 10.1007/s10845-020-01607-9
Qiu, 2021, Diagnosis of damage evolution process for asphalt mixtures using pattern recognition with acoustic emission signals, Constr. Build. Mater., 280, 122536, 10.1016/j.conbuildmat.2021.122536
Samuel, 2019, Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: Advances, current challenges, and future prospects, IEEE Access, 7, 10150, 10.1109/ACCESS.2019.2891350
Entezami, 2018, Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods, Struct. Health Monit., 18, 1416, 10.1177/1475921718800306
Xu, 2019, Fracture monitoring and damage pattern recognition for carbon nanotube-crumb rubber mortar using acoustic emission techniques, Struct. Control Health Monit., 26, e2422, 10.1002/stc.2422
Kwon, 2020, Capacitive neural network using charge-stored memory cells for pattern recognition applications, IEEE Electron Device Lett., 41, 493, 10.1109/LED.2020.2969695
Yang, 2019, A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing, IEEE Trans. Ind. Electron., 67, 800, 10.1109/TIE.2019.2898614
Wang, 2019, Network data management model based on Naïve Bayes classifier and deep neural networks in heterogeneous wireless networks, Comput. Electr. Eng., 75, 135, 10.1016/j.compeleceng.2019.02.015
Werbos, P.J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. [Ph.D. Thesis, Harvard University].
Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Proceedings of the Advances in Neural Information Processing Systems; Association for Computing Machinery.
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2007, January 3–6). Greedy layer-wise training of deep networks. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada.
Gori, M., Monfardini, G., and Scarselli, F. (August, January 31). A new model for earning in raph domains. Proceedings of the International Joint Conference on Neural Networks, Montreal, QC, Canada.
Montavon, 2017, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognit., 65, 211, 10.1016/j.patcog.2016.11.008
Yin, 2020, FDC: A secure federated deep learning mechanism for data collaborations in the internet of things, IEEE Internet Things J., 7, 6348, 10.1109/JIOT.2020.2966778
Sariyer, G., Mangla, S.K., Kazancoglu, Y., Ocal Tasar, C., and Luthra, S. (2021). Data analytics for quality management in Industry 4.0 from a MSME perspective. Ann. Oper. Res., 1–29.
Chen, 2018, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell., 40, 834, 10.1109/TPAMI.2017.2699184
Wu, 2020, A comprehensive survey on graph neural networks, IEEE Trans. Neural Netw. Learn. Syst., 32, 4, 10.1109/TNNLS.2020.2978386
Shao, 2018, Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network, IEEE Trans. Ind. Electron., 65, 2727, 10.1109/TIE.2017.2745473
Zhang, 2020, Image de-raining using a conditional generative adversarial network, IEEE Trans. Circuits Syst. Video Technol., 30, 3943, 10.1109/TCSVT.2019.2920407
Oh, 2018, A deep learning approach for Parkinson’s disease diagnosis from EEG signals, Neural Comput. Appl., 32, 10927, 10.1007/s00521-018-3689-5
Kong, 2017, Short-term residential load forecasting based on LSTM recurrent neural network, IEEE Trans. Smart Grid, 10, 841, 10.1109/TSG.2017.2753802
Gama, 2020, Graphs, convolutions, and neural networks: From graph filters to graph neural networks, IEEE Signal Process. Mag., 37, 128, 10.1109/MSP.2020.3016143
Bello, 2019, Deep-learning cardiac motion analysis for human survival prediction, Nat. Mach. Intell., 1, 95, 10.1038/s42256-019-0019-2
Gu, 2018, Recent advances in convolutional neural networks, Pattern Recognit., 77, 354, 10.1016/j.patcog.2017.10.013
Ashrafzadeh, 2019, Evaporation process modelling over northern Iran: Application of an integrative data-intelligence model with the krill herd optimization algorithm, Hydrol. Sci. J., 64, 1843, 10.1080/02626667.2019.1676428
Protopapadakis, E., Doulamis, A., Doulamis, N., and Maltezos, E. (2021). Stacked autoencoders driven by semi-supervised learning for building extraction from near infrared remote sensing imagery. Remote Sens., 13.
Yang, 2019, A blind stereoscopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route, IEEE Trans. Image Process., 28, 1314, 10.1109/TIP.2018.2878283
Wu, 2018, A hybrid deep learning based traffic flow prediction method and its understanding, Transp. Res. Part C Emerg. Technol., 90, 166, 10.1016/j.trc.2018.03.001
Lin, 2020, Data-driven missing data imputation in cluster monitoring system based on deep neural network, Appl. Intell., 50, 860, 10.1007/s10489-019-01560-y
Zhang, 2021, Deep reinforcement learning assisted federated learning algorithm for data management of IIoT, IEEE Trans. Ind. Inform., 17, 8475, 10.1109/TII.2021.3064351
Guo, 2019, Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data, IEEE Trans. Ind. Electron., 66, 7316, 10.1109/TIE.2018.2877090
Karaca, 2019, Mobile cloud computing based stroke healthcare system, Int. J. Inf. Manag., 45, 250, 10.1016/j.ijinfomgt.2018.09.012
Zhang, 2021, A generative adversarial network for travel times imputation using trajectory data, Comput. Civ. Infrastruct. Eng., 36, 197, 10.1111/mice.12595
Duan, 2016, An efficient realization of deep learning for traffic data imputation, Transp. Res. Part C Emerg. Technol., 72, 168, 10.1016/j.trc.2016.09.015
Shorten, 2019, A survey on image data augmentation for deep learning, J. Big Data, 6, 60, 10.1186/s40537-019-0197-0
Kosasih, 2021, A machine learning approach for predicting hidden links in supply chain with graph neural networks, Int. J. Prod. Res., 60, 5380, 10.1080/00207543.2021.1956697
Oh, 2020, Convolutional neural network–based data recovery method for structural health monitoring, Struct. Health Monit., 19, 1821, 10.1177/1475921719897571
Zhao, 2019, Parallel computing method of deep belief networks and its application to traffic flow prediction, Knowl. Based Syst., 163, 972, 10.1016/j.knosys.2018.10.025
Lillicrap, 2016, Random synaptic feedback weights support error backpropagation for deep learning, Nat. Commun., 7, 13276, 10.1038/ncomms13276
Nissen, 2017, Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques, Inf. Syst. Front., 19, 1085, 10.1007/s10796-016-9680-8
Wen, 2018, A new convolutional neural network-based data-driven fault diagnosis method, IEEE Trans. Ind. Electron., 65, 5990, 10.1109/TIE.2017.2774777
Waheed, 2020, CovidGAN: Data augmentation using auxiliary classifier GAN for improved COVID-19 detection, IEEE Access, 8, 91916, 10.1109/ACCESS.2020.2994762
Uddin, 2017, Facial expression recognition utilizing local direction-based robust features and deep belief network, IEEE Access, 5, 4525, 10.1109/ACCESS.2017.2676238
Wu, 2020, Recent advances in deep learning for object detection, Neurocomputing, 396, 39, 10.1016/j.neucom.2020.01.085
Wang, 2020, Deep high-resolution representation learning for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 43, 3349, 10.1109/TPAMI.2020.2983686
Cortez, 2018, An architecture for emergency event prediction using LSTM recurrent neural networks, Expert Syst. Appl., 97, 315, 10.1016/j.eswa.2017.12.037
Ruiz, 2021, Graph neural networks: Architectures, stability, and transferability, Proc. IEEE, 109, 660, 10.1109/JPROC.2021.3055400
Ordóñez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.
Yu, 2019, Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition, IEEE Trans. Neural Netw. Learn. Syst., 31, 661, 10.1109/TNNLS.2019.2908982
Ren, 2017, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031
Alzubaidi, 2021, Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8, 53, 10.1186/s40537-021-00444-8
Demir, 2021, DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images, Appl. Soft Comput., 103, 107160, 10.1016/j.asoc.2021.107160
Shi, 2018, Deep learning for household load forecasting-a novel pooling deep RNN, IEEE Trans. Smart Grid, 9, 5271, 10.1109/TSG.2017.2686012
Perol, 2018, Convolutional neural network for earthquake detection and location, Sci. Adv., 4, e1700578, 10.1126/sciadv.1700578
Fombellida, 2018, Tackling business intelligence with bioinspired deep learning, Neural Comput. Appl., 32, 13195, 10.1007/s00521-018-3377-5
Li, 2021, Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network, Energy, 227, 120478, 10.1016/j.energy.2021.120478
Lee, 2021, Detecting handcrafted facial image manipulations and GAN-generated facial images using Shallow-FakeFaceNet, Appl. Soft Comput., 105, 107256, 10.1016/j.asoc.2021.107256
Araujo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polonia, A., and Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS ONE, 12.
Arbabzadah, 2017, Quantum-chemical insights from deep tensor neural networks, Nat. Commun., 8, 13890, 10.1038/ncomms13890
Mehdiyev, 2020, A novel business process prediction model using a deep learning method, Bus. Inf. Syst. Eng., 62, 143, 10.1007/s12599-018-0551-3
Kim, 2021, A systematic analysis and guidelines of graph neural networks for practical applications, Expert Syst. Appl., 184, 115466, 10.1016/j.eswa.2021.115466
Diamant, 2018, GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321, 10.1016/j.neucom.2018.09.013
Guo, 2017, A recurrent neural network based health indicator for remaining useful life prediction of bearings, Neurocomputing, 240, 98, 10.1016/j.neucom.2017.02.045
Chowdhury, 2020, Can AI help in screening viral and COVID-19 Pneumonia?, IEEE Access, 8, 132665, 10.1109/ACCESS.2020.3010287
Lu, 2021, Evolutionary deep belief network for cyber-attack detection in industrial automation and control system, IEEE Trans. Ind. Inform., 17, 7618, 10.1109/TII.2021.3053304
Polson, 2017, Deep learning for short-term traffic flow prediction, Transp. Res. Part C Emerg. Technol., 79, 1, 10.1016/j.trc.2017.02.024
Gama, 2020, Stability properties of graph neural networks, IEEE Trans. Signal Process., 68, 5680, 10.1109/TSP.2020.3026980
Liu, 2020, Growth scale prediction of big data for information systems based on a deep learning SAEP method, IEEE Access, 8, 62883, 10.1109/ACCESS.2020.2966770
Sun, 2020, Automatically designing CNN architectures using the genetic algorithm for image classification, IEEE Trans. Cybern., 50, 3840, 10.1109/TCYB.2020.2983860
Pham, 2021, A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing, Nat. Mach. Intell., 3, 247, 10.1038/s42256-020-00285-9
Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386
Simonyan, K., and Zisserman, A. (2015, January 7–9). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA.
He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las vegas, NV, USA.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the inception architecture for computer vision. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las vegas, NV, USA.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.
Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4–9). Dynamic routing between capsules. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
Serey, J., Quezada, L., Alfaro, M., Fuertes, G., Ternero, R., Gatica, G., Gutierrez, S., and Vargas, M. (2020). Methodological proposals for the development of services in a smart city: A literature review. Sustainability, 12.
Fuertes, 2016, Intelligent packaging Systems: Sensors and Nanosensors to Monitor Food Quality and Safety, J. Sens., 2016, 4046061
Keele, S. (2007). Technical Report, Version 2.3, EBSE.
Banguera, 2017, Reverse and inverse logistic models for solid waste management, South Afr. J. Ind. Eng., 28, 120
Fuertes, 2020, Conceptual framework for the strategic management: A literature review—Descriptive, J. Eng., 2020, 6253013
Horkoff, 2019, Goal-oriented requirements engineering: An extended systematic mapping study, Requir. Eng., 24, 133, 10.1007/s00766-017-0280-z
Vargas, 2021, Reverse Logistics for Solid Waste from the Construction Industry, Adv. Civ. Eng., 2021, 6654718
Valenzuela, 2021, Reverse logistics models for the collection of plastic waste: A literature review, Waste Manag. Res., 39, 1, 10.1177/0734242X211003948
Hamid, 2021, How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management, Comput. Sci. Rev., 39, 100337, 10.1016/j.cosrev.2020.100337
Saura, 2021, Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy, Technol. Forecast. Soc. Change, 167, 120681, 10.1016/j.techfore.2021.120681
Garg, 2021, Role of machine learning in medical research: A survey, Comput. Sci. Rev., 40, 100370, 10.1016/j.cosrev.2021.100370
Page, 2020, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, Syst. Rev., 10, 89, 10.1186/s13643-021-01626-4
Page, 2020, PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews, MetaArXiv, 372, n160
(2021, November 25). Google Scholar H5-Index. Available online: https://scholar.google.com/citations?view_op=top_venues&hl=es&vq=en.
(2021, November 25). Scimago Journal & Country Rank SJR. Available online: https://www.scimagojr.com/.
Tang, 2021, A review of lane detection methods based on deep learning, Pattern Recognit., 111, 107623, 10.1016/j.patcog.2020.107623
Chen, 2022, CoCNN: Co-occurrence CNN for recommendation, Expert Syst. Appl., 195, 116595, 10.1016/j.eswa.2022.116595
Ramakrishnan, 2022, Cervical cancer diagnosis based on modified uniform local ternary patterns and feed forward multilayer network optimized by genetic algorithm, Comput. Biol. Med., 144, 105392, 10.1016/j.compbiomed.2022.105392
Alsaffar, 2022, DNA repair genes (APE1 and XRCC1) polymorphisms—Cadmium interaction in fuel station workers, J. Pharm. Negat. Results, 13, 32
Pourkaramdel, 2022, Fabric defect detection based on completed local quartet patterns and majority decision algorithm, Expert Syst. Appl., 198, 116827, 10.1016/j.eswa.2022.116827