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DeepThink IoT: The Strength of Deep Learning in Internet of Things

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Abstract

The integration of Deep Learning (DL) and the Internet of Things (IoT) has revolutionized technology in the twenty-first century, enabling humans and machines to perform tasks more efficiently. The combination of DL and the IoT has resulted in significant advancements in technology by improving the efficiency, security, and user experience of IoT devices and systems. The integration of DL and IoT offers several benefits, including improved data processing and analysis capabilities, the ability for IoT devices to learn from data and adapt to changing conditions, and the early detection of system malfunctions and potential security breaches. This survey paper provides a comprehensive overview of the impact of DL on IoT, including an analysis of sensor data to detect patterns and make predictions, and the implications for various industries such as healthcare, manufacturing, agriculture, and smart cities. The survey paper covers topics such as DL models, frameworks, IoT connectivity terminologies, IoT components, IoT service-oriented architecture, IoT applications, the role of DL in IoT, and challenges faced by DL in IoT. The study also presents quantitative achievements that highlight the potential impact of IoT and DL in environmental contexts such as precision farming and energy consumption. Overall, the survey paper provides an excellent resource for researchers interested in exploring the potential of IoT and DL in their field.

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References

  • Achouch M, Dimitrova M, Ziane K, Sattarpanah Karganroudi S, Dhouib R, Ibrahim H, Adda M (2022) On predictive maintenance in industry 4.0: overview, models, and challenges. Appl Sci 12(16):8081

    Google Scholar 

  • Adedoja A, Owolawi PA, Mapayi T (2019) Deep learning based on NASNet for plant disease recognition using leave images. In: 2019 international conference on advances in big data, computing and data communication systems (icABCD). pp 1–5

  • Agostinelli F, Hoffman M, Sadowski P, Baldi P (2014) Learning activation functions to improve deep neural networks. arXiv preprint. https://arxiv.org/abs/1412.6830

  • Ahmad U, Song H, Bilal A, Alazab M, Jolfaei A (2020) Securing smart vehicles from relay attacks using machine learning. J Supercomput 76(4):2665–2682

    Google Scholar 

  • Ahmed I, Jeon G, Piccialli F (2021) A deep-learning-based smart healthcare system for patient’s discomfort detection at the edge of internet of things. IEEE Internet Things J 8(13):10318–10326

    Google Scholar 

  • Albahrani SA, Mahajan D, Kargarrazi S, Schwantuschke D, Gneiting T, Senesky DG, Khandelwal S (2020) Extreme temperature modeling of ALGAN/GAN HEMTS. IEEE Trans Electron Devices 67(2):430–437

    Google Scholar 

  • Alhussein M, Muhammad G (2018) Voice pathology detection using deep learning on mobile healthcare framework. IEEE Access 6:41034–41041

    Google Scholar 

  • Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Van Esesn BC, Awwal AAS, Asari VK (2018) The history began from Alexnet: a comprehensive survey on deep learning approaches. arXiv preprint. https://arxiv.org/abs/1803.01164

  • Alyamkin S, Ardi M, Berg AC, Brighton A, Chen B, Chen Y, Cheng H-P, Fan Z, Feng C, Fu B et al (2019) Low-power computer vision: status, challenges, and opportunities. IEEE J Emerg Sel Top Circuits Syst 9(2):411–421

    Google Scholar 

  • Andics A, McQueen JM, Petersson KM, Gál V, Rudas G, Vidnyánszky Z (2010) Neural mechanisms for voice recognition. NeuroImage 52(4):1528–1540

    Google Scholar 

  • Andrews JG, Ghosh A, Muhamed R (2007) Fundamentals of WiMAX: understanding broadband wireless networking. Pearson Education, London

    Google Scholar 

  • Arasteh H, Hosseinnezhad V, Loia V, Tommasetti A, Troisi O, Shafie-khah M, Siano P (2016) IoT-based smart cities: a survey. In: 2016 IEEE 16th international conference on environment and electrical engineering (EEEIC). IEEE, pp 1–6

  • Ashok S, Kishore G, Rajesh V, Suchitra S, Sophia SGG, Pavithra B (2020) Tomato leaf disease detection using deep learning techniques. In: 2020 5th international conference on communication and electronics systems (ICCES). IEEE, pp 979–983

  • Azzam R, Alkendi Y, Taha T, Huang S, Zweiri Y (2020) A stacked LSTM-based approach for reducing semantic pose estimation error. IEEE Trans Instrum Meas 70:1–14

    Google Scholar 

  • Baker SB, Xiang W, Atkinson I (2017) Internet of things for smart healthcare: technologies, challenges, and opportunities. IEEE Access 5:26521–26544

    Google Scholar 

  • Ballester P, Araujo RM (2016) On the performance of GoogleNet and Alexnet applied to sketches. In: Thirtieth AAAI conference on artificial intelligence

  • Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR (2019) Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn Lett 122:23–30

    Google Scholar 

  • Bandara K, Bergmeir C, Hewamalage H (2020) LSTM-MSNet: leveraging forecasts on sets of related time series with multiple seasonal patterns. IEEE Trans Neural Netw Learn Syst 32(4):1586–1599

    Google Scholar 

  • Bello O, Zeadally S, Badra M (2017) Network layer inter-operation of device-to-device communication technologies in internet of things (IoT). Ad Hoc Netw 57:52–62

    Google Scholar 

  • Bianchi V, Bassoli M, Lombardo G, Fornacciari P, Mordonini M, De Munari I (2019) IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Things J 6(5):8553–8562

    Google Scholar 

  • Bisdikian C (2001) An overview of the bluetooth wireless technology. IEEE Commun Mag 39(12):86–94

    Google Scholar 

  • Brown RE, Milner PM (2003) The legacy of Donald O. Hebb: more than the Hebb synapse. Nat Rev Neurosci 4(12):1013–1019

    Google Scholar 

  • Caffe. https://caffe.berkeleyvision.org

  • Cambria E, White B (2014) Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag 9(2):48–57

    Google Scholar 

  • Canli H, Toklu S (2021) Deep learning-based mobile application design for smart parking. IEEE Access 9:61171–61183

    Google Scholar 

  • Caro F, Sadr R (2019) The Internet of Things (IoT) in retail: bridging supply and demand. Bus Horiz 62(1):47–54

    Google Scholar 

  • Celebi ME, Aydin K (eds) (2016) Unsupervised learning algorithms, vol 9. Springer, Cham, p 103

    Google Scholar 

  • Cerchecci M, Luti F, Mecocci A, Parrino S, Peruzzi G, Pozzebon A (2018) A low power IoT sensor node architecture for waste management within smart cities context. Sensors 18(4):1282

    Google Scholar 

  • Chadebec C, Thibeau-Sutre E, Burgos N, Allassonnière S (2022) Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2022.3185773

    Article  Google Scholar 

  • Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans Big Data 7(4):750–758

    Google Scholar 

  • Chen L, Zhou M, Su W, Wu M, She J, Hirota K (2018) Softmax regression based deep sparse autoencoder network for facial emotion recognition in human–robot interaction. Inf Sci 428:49–61

    MathSciNet  Google Scholar 

  • Chen X, Chen W, Hou L, Hu H, Bu X, Zhu Q (2020a) A novel data-driven rollover risk assessment for articulated steering vehicles using RNN. J Mech Sci Technol 34(5):2161–2170

    Google Scholar 

  • Chen J, Du L, Liao L (2020b) Discriminative mixture variational autoencoder for semisupervised classification. IEEE Trans Cybern 52(5):3032–3046

    Google Scholar 

  • Chen P, Fu X, Wang X (2021a) A graph convolutional stacked bidirectional unidirectional-LSTM neural network for metro ridership prediction. IEEE Trans Intell Transport Syst. https://doi.org/10.1109/TITS.2021.3065404

    Article  Google Scholar 

  • Chen B, Liu X, Zheng Y, Zhao G, Shi Y-Q (2021b) A robust GAN-generated face detection method based on dual-color spaces and an improved Xception. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2021.3116679

    Article  Google Scholar 

  • Choi S, Kim E, Oh S (2013) Human behavior prediction for smart homes using deep learning. In: 2013 IEEE RO-MAN. IEEE, pp 173–179

  • Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1251–1258

  • Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A (2016) Comparison of deep neural networks to spatiotemporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci Rep 6(1):1–13

    Google Scholar 

  • Cunningham P, Cord M, Delany SJ (2008) Supervised learning. In: Machine learning techniques for multimedia: case studies on organization and retrieval. pp 21–49

  • Danaei Mehr H, Polat H (2019) Human activity recognition in smart home with deep learning approach. In: 2019 7th international Istanbul smart grids and cities congress and fair (ICSG). IEEE, pp 149–153

  • De Santo A, Galli A, Gravina M, Moscato V, Sperlì G (2020) Deep learning for HDD health assessment: an application based on LSTM. IEEE Trans Comput 71(1):69–80

    MATH  Google Scholar 

  • Deeplearning4j. https://deeplearning4j.konduit.ai

  • Deng L (2016) Deep learning: from speech recognition to language and multimodal processing. APSIPA Trans Signal Inf Process. https://doi.org/10.1017/ATSIP.2015.22

    Article  Google Scholar 

  • Dewangan G, Maurya S (2021) Fault diagnosis of machines using deep convolutional beta-variational autoencoder. IEEE Trans Artif Intell 3(2):287–296

    Google Scholar 

  • Dey N, Fong S, Song W, Cho K (2018) Forecasting energy consumption from smart home sensor network by deep learning. In: Smart trends in information technology and computer communications: second international conference, SmartCom 2017, Pune, India, August 18–19, 2017, revised selected papers 2. Springer, pp 255–265

  • Du X, Ma C, Zhang G, Li J, Lai Y-K, Zhao G, Deng X, Liu Y-J, Wang H (2020) An efficient LSTM network for emotion recognition from multichannel EEG signals. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2020.3013711

    Article  Google Scholar 

  • Elkholy MM, Mostafa M, Ebied HM, Tolba MF (2020) Hyperspectral unmixing using deep convolutional autoencoder. Int J Remote Sens 41(12):4799–4819

    Google Scholar 

  • Emami H, Aliabadi MM, Dong M, Chinnam RB (2020) SPA-GAN: spatial attention GAN for image-to-image translation. IEEE Trans Multimed 23:391–401

    Google Scholar 

  • Essien A, Giannetti C (2020) A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders. IEEE Trans Ind Inform 16(9):6069–6078

    Google Scholar 

  • Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    Google Scholar 

  • Fan H, Zhang F, Wei Y, Li Z, Zou C, Gao Y, Dai Q (2021) Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans Pattern Anal Mach Intell 44(8):4125–4138

    Google Scholar 

  • Fang H, Hu C (2014) Recognizing human activity in smart home using deep learning algorithm. In: Proceedings of the 33rd Chinese control conference. pp 4716–4720

  • Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K (2018) Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Future Gener Comput Syst 78:659–676

    Google Scholar 

  • Feigl T, Kram S, Woller P, Siddiqui RH, Philippsen M, Mutschler C (2020) RNN-aided human velocity estimation from a single IMU. Sensors 20(13):3656

    Google Scholar 

  • Francis M, Deisy C (2019) Disease detection and classification in agricultural plants using convolutional neural networks—a visual understanding. In: 2019 6th international conference on signal processing and integrated networks (SPIN). IEEE, pp 1063–1068

  • Gao Y, Xiang X, Xiong N, Huang B, Lee HJ, Alrifai R, Jiang X, Fang Z (2018) Human action monitoring for healthcare based on deep learning. IEEE Access 6:52277–52285

    Google Scholar 

  • Gao S, Huang Y, Zhang S, Han J, Wang G, Zhang M, Lin Q (2020) Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J Hydrol 589:125188

    Google Scholar 

  • Gayathri S, Wise DCJW, Shamini PB, Muthukumaran N (2020) Image analysis and detection of tea leaf disease using deep learning. In: 2020 international conference on electronics and sustainable communication systems (ICESC). IEEE, pp 398–403

  • Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323–338

    Google Scholar 

  • Gharibzahedi SMT, Barba FJ, Zhou J, Wang M, Altintas Z (2022) Electronic sensor technologies in monitoring quality of tea: a review. Biosensors 12(5):356

    Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  • Gorostiza EM, Galilea JLL, Meca FJM, Monzú DS, Zapata FE, Puerto LP (2011) Infrared sensor system for mobile-robot positioning in intelligent spaces. Sensors 11:5416–5438

    Google Scholar 

  • Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    MathSciNet  Google Scholar 

  • Habi HV, Messer H (2020) Recurrent neural network for rain estimation using commercial microwave links. IEEE Trans Geosci Remote Sens 59(5):3672–3681

    Google Scholar 

  • Hadjeres G, Nielsen F (2020) Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation. Neural Comput Appl 32(4):995–1005

    Google Scholar 

  • Han F, Yao J, Zhu H, Wang C (2020) Underwater image processing and object detection based on deep cnn method. J Sens. https://doi.org/10.1155/2020/6707328

    Article  Google Scholar 

  • Hashida H, Kawamoto Y, Kato N (2019) Efficient delay-based internet-wide scanning method for IoT devices in wireless LAN. IEEE Internet Things J 7(2):1364–1374

    Google Scholar 

  • Hayman S (1999) The McCulloch–Pitts model. In: IJCNN’99. International joint conference on neural networks. proceedings (Cat. No. 99CH36339), vol 6. IEEE, pp 4438–4439

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  • Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670

    MathSciNet  MATH  Google Scholar 

  • Hou R, Shen Y, Zhao H, Hu H, Lu J, Long T (2020) Power loss characterization and modeling for GAN-based hardswitching half-bridges considering dynamic on-state resistance. IEEE Trans Transport Electrif 6(2):540–553

    Google Scholar 

  • IEEE 802.11. https://www.ieee802.org/11/

  • IEEE 802.15. https://standards.ieee.org/ieee/802.15.4/7029/

  • IEEE 802.15. https://www.ieee802.org/16/tge/

  • IEEE 802.15. https://www.ieee802.org/15/

  • Ilsvrc-2015. https://scholar.google.com/citations?user=mG4imMEAAAAJ&hl=en&oi=ao

  • Ilsvrc-2015. https://news.cornell.edu/stories/2019/09/professors-perceptron-paved-way-ai-60-years-too-soon

  • Ilsvrc-2015. https://scholar.google.com/citations?user=WLN3QrAAAAAJ&hl=en&oi=ao

  • Iqbal T, Ali H (2018) Generative adversarial network for medical images (MI-GAN). J Med Syst 42(11):1–11

    Google Scholar 

  • Iqbal MA, Talukder KH (2020) Detection of potato disease using image segmentation and machine learning. In: 2020 international conference on wireless communications signal processing and networking (WiSPNET). pp 43–47

  • Irsoy O, Alpaydın E (2017) Unsupervised feature extraction with autoencoder trees. Neurocomputing 258:63–73

    Google Scholar 

  • Jahangir H, Tayarani H, Gougheri SS, Golkar MA, Ahmadian A, Elkamel A (2020) Deep learning-based forecasting approach in smart grids with microclustering and bidirectional LSTM network. IEEE Trans Ind Electron 68(9):8298–8309

    Google Scholar 

  • Jasim MA, Al-Tuwaijari JM (2020) Plant leaf diseases detection and classification using image processing and deep learning techniques. In: 2020 international conference on computer science and software engineering (CSASE). IEEE, pp 259–265

  • Ji Z, Li S, Pang Y (2018) Fusion-attention network for person search with free-form natural language. Pattern Recogn Lett 116:205–211

    Google Scholar 

  • Jia L, Gu Y, Cheng K, Yan H, Ren F (2020) BeAware: convolutional neural network (CNN) based user behavior understanding through WiFi channel state information. Neurocomputing 397:457–463

    Google Scholar 

  • Jia Y, Liu B, Dou W, Xu X, Zhou X, Qi L, Yan Z (2022) CroAPP: a CNN-based resource optimization approach in edge computing environment. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2022.3154473

    Article  Google Scholar 

  • Jiang K, Wang Z, Yi P, Wang G, Lu T, Jiang J (2019) Edge-enhanced GAN for remote sensing image superresolution. IEEE Trans Geosci Remote Sens 57(8):5799–5812

    Google Scholar 

  • Jiang F, Lu Y, Chen Y, Cai D, Li G (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179:105824

    Google Scholar 

  • Jiao R, Peng K, Dong J (2020) Remaining useful life prediction of lithium-ion batteries based on conditional variational autoencodersparticle filter. IEEE Trans Instrum Meas 69(11):8831–8843

    Google Scholar 

  • Jin W, Kim D (2018) Development of virtual resource based IoT proxy for bridging heterogeneous web services in IoT networks. Sensors 18(6):1721

    Google Scholar 

  • Jin L, Yan J, Du X, Xiao X, Fu D (2020) RNN for solving time-variant generalized Sylvester equation with applications to robots and acoustic source localization. IEEE Trans Ind Inform 16(10):6359–6369

    Google Scholar 

  • Joyce JM (2011) Kullback–Leibler divergence. In: International encyclopedia of statistical science. Springer, Berlin, Heidelberg, pp 720–722

  • Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90

    Google Scholar 

  • Kang B, Choo H (2018) An experimental study of a reliable IoT gateway. ICT Express 4(3):130–133

    Google Scholar 

  • Karadağ K, Tenekeci ME, Taşaltın R, Bilgili A (2020) Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance. Sustain Comput Inform Syst 28:100299

    Google Scholar 

  • Karagiannis V, Chatzimisios P, Vazquez-Gallego F, Alonso-Zarate J (2015) A survey on application layer protocols for the internet of things. Trans IoT Cloud Comput 3(1):11–17

    Google Scholar 

  • Karim F, Majumdar S, Darabi H, Chen S (2017) LSTM fully convolutional networks for time series classification. IEEE Access 6:1662–1669

    Google Scholar 

  • Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  • Ke Z, Vikalo H (2021) Real-time radio technology and modulation classification via an LSTM auto-encoder. IEEE Trans Wirel Commun 21(1):370–382

    Google Scholar 

  • Keras. https://keras.io

  • Keras. https://onnx.ai

  • Khairdoost N, Shirpour M, Bauer MA, Beauchemin SS (2020) Real-time driver maneuver prediction using LSTM. IEEE Trans Intell Veh 5(4):714–724

    Google Scholar 

  • Khalil K, Eldash O, Kumar A, Bayoumi M (2019) Economic LSTM approach for recurrent neural networks. IEEE Trans Circuits Syst II Express Briefs 66(11):1885–1889

    Google Scholar 

  • Khan RU, Zhang X, Kumar R (2019) Analysis of ResNet and GoogleNet models for malware detection. J Comput Virol Hacking Tech 15(1):29–37

    Google Scholar 

  • Khan AH, Li S, Chen D, Liao L (2020a) Tracking control of redundant mobile manipulator: an RNN based metaheuristic approach. Neurocomputing 400:272–284

    Google Scholar 

  • Khan MZ, Khan MUG, Irshad O, Iqbal R (2020b) Deep learning and blockchain fusion for detecting driver’s behavior in smart vehicles. Internet Technol Lett 3(6):e119

    Google Scholar 

  • Khanh QV, Hoai NV, Manh LD, Le AN, Jeon G (2022) Wireless communication technologies for IoT in 5G: vision, applications, and challenges. Wirel Commun Mob Comput 2022:1–12

    Google Scholar 

  • Khazeiynasab SR, Zhao J, Batarseh I, Tan B (2021) Power plant model parameter calibration using conditional variational autoencoder. IEEE Trans Power Syst 37(2):1642–1652

    Google Scholar 

  • Khurana D, Koli A, Khatter K, Singh S (2023) Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl 82(3):3713–3744

    Google Scholar 

  • Kim S, Kim S (2018) User preference for an IoT healthcare application for lifestyle disease management. Telecommun Policy 42(4):304–314

    Google Scholar 

  • Kim S, Lee J, Kang S, Lee J, Yoo H-J (2020) A power-efficient CNN accelerator with similar feature skipping for face recognition in mobile devices. IEEE Trans Circuits Syst I 67(4):1181–1193

    Google Scholar 

  • Kim K, Kim C, Jang C, Sunwoo M, Jo K (2021) Deep learning-based dynamic object classification using LiDAR point cloud augmented by layer-based accumulation for intelligent vehicles. Expert Syst Appl 167:113861

    Google Scholar 

  • Kingma DP, Welling M (2019) An introduction to variational autoencoders. Found Trends® Mach Learn 12(4):307–392

    MATH  Google Scholar 

  • Kollias D, Zafeiriou S (2020) Exploiting multi-CNN features in CNN-RNN based dimensional emotion recognition on the OMG in-the-wild dataset. IEEE Trans Affect Comput 12(3):595–606

    Google Scholar 

  • Kong L, Tan J, Huang J, Chen G, Wang S, Jin X, Zeng P, Khan M, Das SK (2022) Edge-computing-driven internet of things: a survey. ACM Comput Surv 55(8):1–41

    Google Scholar 

  • Kuutti S, Bowden R, Jin Y, Barber P, Fallah S (2020) A survey of deep learning applications to autonomous vehicle control. IEEE Trans Intell Transport Syst 22(2):712–733

    Google Scholar 

  • Lakhwani K, Gianey H, Agarwal N, Gupta S (2019) Development of IoT for smart agriculture a review. In: Emerging trends in expert applications and security: proceedings of ICETEAS 2018. Springer, pp 425–432

  • Langer S (2021) Approximating smooth functions by deep neural networks with sigmoid activation function. J Multivar Anal 182:104696

    MathSciNet  MATH  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015a) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  • LeCun Y et al (2015b) LeNet-5, convolutional neural networks, vol 20, no 5, p 14. http://yann.lecun.com/exdb/lenet

  • Lee S-J, Chen T, Yu L, Lai C-H (2018) Image classification based on the boost convolutional neural network. IEEE Access 6:12755–12768

    Google Scholar 

  • Li J (2022) Recent advances in end-to-end automatic speech recognition. APSIPA Trans Signal Inf Process 11(1):1–64

  • Li J, Huang X, Gamba P, Bioucas-Dias JM, Zhang L, Benediktsson JA, Plaza A (2014) Multiple feature learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(3):1592–1606

    Google Scholar 

  • Li S, Xu LD, Zhao S (2015) The internet of things: a survey. Inf Syst Front 17:243–259

    Google Scholar 

  • Li W, Fu H, Yu L, Gong P, Feng D, Li C, Clinton N (2016) Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. Int J Remote Sens 37(23):5632–5646

    Google Scholar 

  • Li S, Liu X, Wang Y, Wang X (2019) A cubic quality loss function and its applications. Qual Reliab Eng Int 35(4):1161–1179

    Google Scholar 

  • Li X, Tang J, Zhang Q, Gao B, Yang JJ, Song S, Wu W, Zhang W, Yao P, Deng N et al (2020a) Power-efficient neural network with artificial dendrites. Nat Nanotechnol 15(9):776–782

    Google Scholar 

  • Li L, Zou C, Zheng Y, Su Q, Fu H, Tai C-L (2020b) Sketch-R2CNN: an RNN-rasterization-CNN architecture for vector sketch recognition. IEEE Trans Vis Comput Graph 27(9):3745–3754

    Google Scholar 

  • Li Q, Cheng M, Wang J, Sun B (2020c) LSTM based phishing detection for big email data. IEEE Trans Big Data. https://doi.org/10.1109/TBDATA.2020.2978915

    Article  Google Scholar 

  • Li R, Hu Y, Liang Q (2020d) T2F-LSTM method for long-term traffic volume prediction. IEEE Trans Fuzzy Syst 28(12):3256–3264

    Google Scholar 

  • Li L, Yan J, Wang H, Jin Y (2020e) Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE Trans Neural Netw Learn Syst 32(3):1177–1191

    Google Scholar 

  • Li C, Zhang Z, Song R, Cheng J, Liu Y, Chen X (2021a) EEG-based emotion recognition via neural architecture search. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2021.3130387

    Article  Google Scholar 

  • Li W, Liang Z, Ma P, Wang R, Cui X, Chen P (2021b) Hausdorff GAN: improving GAN generation quality with Hausdorff metric. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3062396

    Article  Google Scholar 

  • Li L, Yan J, Zhang Y, Zhang J, Bao J, Jin Y, Yang X (2022) Learning generative RNN-ODE for collaborative time-series and event sequence forecasting. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2022.3185115

    Article  Google Scholar 

  • Liciotti D, Bernardini M, Romeo L, Frontoni E (2020) A sequential deep learning application for recognising human activities in smart homes. Neurocomputing 396:501–513

    Google Scholar 

  • Lin L, Li M, Ma L, Nazari M, Mahdavi S, Yunianta A (2020) Using fuzzy uncertainty quantization and hybrid RNN-LSTM deep learning model for wind turbine power. IEEE Trans Ind Appl. https://doi.org/10.1109/TIA.2020.2999436

    Article  Google Scholar 

  • Lina López K, Gagné C, Gardner M-A (2018) Demand-side management using deep learning for smart charging of electric vehicles. IEEE Trans Smart Grid 10(3):2683–2691

    Google Scholar 

  • Liu Q, Wang J (2008) A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming. IEEE Trans Neural Netw 19(4):558–570

    Google Scholar 

  • Liu L, Shen C, van den Hengel A (2016) Cross-convolutional-layer pooling for image recognition. IEEE Trans Pattern Anal Mach Intell 39(11):2305–2313

    Google Scholar 

  • Liu H, Zhou J, Zheng Y, Jiang W, Zhang Y (2018) Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans 77:167–178

    Google Scholar 

  • Liu Z, Wu J, Fu L, Majeed Y, Feng Y, Li R, Cui Y (2019a) Improved kiwifruit detection using pre-trained VGG16 with RGB and NIR information fusion. IEEE Access 8:2327–2336

    Google Scholar 

  • Liu H, Lang B, Liu M, Yan H (2019b) CNN and RNN based payload classification methods for attack detection. Knowl Based Syst 163:332–341

    Google Scholar 

  • Lopez-Alvis J, Laloy E, Nguyen F, Hermans T (2021) Deep generative models in inversion: the impact of the generator’s nonlinearity and development of a new approach based on a variational autoencoder. Comput Geosci 152:104762

    Google Scholar 

  • Lore KG, Akintayo A, Sarkar S (2017) LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662

    Google Scholar 

  • Lu C, Wang Z-Y, Qin W-L, Ma J (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130:377–388

    Google Scholar 

  • Lu S, Lu Z, Zhang Y-D (2019) Pathological brain detection based on Alexnet and transfer learning. J Comput Sci 30:41–47

    Google Scholar 

  • Lv N, Chen C, Qiu T, Sangaiah AK (2018) Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Trans Ind Inform 14(12):5530–5538

    Google Scholar 

  • Ma M, Mao Z (2020) Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Trans Ind Inform 17(3):1658–1667

    Google Scholar 

  • Ma Z, Chang D, Xie J, Ding Y, Wen S, Li X, Si Z, Guo J (2019a) Fine-grained vehicle classification with channel max pooling modified CNNs. IEEE Trans Veh Technol 68(4):3224–3233

    Google Scholar 

  • Ma Y, Xu X, Yu Q, Zhang Y, Li Y, Zhao J, Wang G (2019b) LungBRN: a smart digital stethoscope for detecting respiratory disease using Bi-ResNet deep learning algorithm. In: 2019b IEEE biomedical circuits and systems conference (BioCAS). IEEE, pp 1–4

  • Ma Y, Zhou G, Wang S (2019c) WiFi sensing with channel state information: a survey. ACM Comput Surv (CSUR) 52(3):1–36

    Google Scholar 

  • Ma J, Liu H, Peng C, Qiu T (2020) Unauthorized broadcasting identification: a deep LSTM recurrent learning approach. IEEE Trans Instrum Meas 69(9):5981–5983

    Google Scholar 

  • Ma F, Sun B, Li S (2021) Facial expression recognition with visual transformers and attentional selective fusion. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2021.3122146

    Article  Google Scholar 

  • Mao L, Yan Y, Xue J-H, Wang H (2020) Deep multi-task multi-label CNN for effective facial attribute classification. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2020.2969189

    Article  Google Scholar 

  • Mct. https://learn.microsoft.com/en-us/cognitive-toolkit/

  • Mehmood F, Ullah I, Ahmad S, Kim D (2019) Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01272-8

    Article  Google Scholar 

  • Meneghello F, Calore M, Zucchetto D, Polese M, Zanella A (2019) IoT: Internet of Threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet Things J 6(5):8182–8201

    Google Scholar 

  • Meng Z, Zhan X, Li J, Pan Z (2018) An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement 130:448–454

    Google Scholar 

  • Miglani A, Kumar N (2019) Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges. Veh Commun 20:100184

    Google Scholar 

  • Militante SV, Gerardo BD, Dionisio NV (2019) Plant leaf detection and disease recognition using deep learning. In: 2019 IEEE Eurasia conference on IoT, communication and engineering (ECICE). IEEE, pp 579–582

  • Minsky M, Papert SA (2017) Perceptrons, reissue of the 1988 expanded edition with a new foreword by Léon Bottou: an introduction to computational geometry. MIT Press, Cambridge

    Google Scholar 

  • Mishra SK, Sarkar A (2022) Service-oriented architecture for internet of things: a semantic approach. J King Saud Univ Comput Inf Sci 34(10):8765–8776

    Google Scholar 

  • Mishra S, Sachan R, Rajpal D (2020) Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Comput Sci 167:2003–2010

    Google Scholar 

  • Misra D (2019) Mish: a self regularized non-monotonic activation function. arXiv preprint. https://arxiv.org/abs/1908.08681

  • Mitchell TM (2007) Machine learning, vol 1. McGraw-Hill, New York

    MATH  Google Scholar 

  • Muangprathub J, Boonnam N, Kajornkasirat S, Lekbangpong N, Wanichsombat A, Nillaor P (2019) IoT and agriculture data analysis for smart farm. Comput Electron Agric 156:467–474

    Google Scholar 

  • Mukherjee D, Mondal R, Singh PK, Sarkar R, Bhattacharjee D (2020) EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications. Multimed Tools Appl 79(41):31663–31690

    Google Scholar 

  • Mulligan G (2007) The 6LoWPAN architecture. In: Proceedings of the 4th workshop on embedded networked sensors. pp 78–82

  • Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32(15):11419–11441

    Google Scholar 

  • Muthu Ramya C, Shanmugaraj M, Prabakaran R (2011) Study on ZigBee technology. In: 2011 3rd international conference on electronics computer technology, vol 6. IEEE, pp 297–301

  • MXNet. https://mxnet.apache.org/versions/1.9.1/

  • Natani A, Sharma A, Peruma T, Sukhavasi S (2019) Deep learning for multi-resident activity recognition in ambient sensing smart homes. In: 2019 IEEE 8th global conference on consumer electronics (GCCE). IEEE, pp 340–341

  • Niu S, Li B, Wang X, Lin H (2020) Defect image sample generation with GAN for improving defect recognition. IEEE Trans Autom Sci Eng 17(3):1611–1622

    Google Scholar 

  • Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737

    Google Scholar 

  • Othman E, Bazi Y, Alajlan N, Alhichri H, Melgani F (2016) Using convolutional features and a sparse autoencoder for land-use scene classification. Int J Remote Sens 37(10):2149–2167

    Google Scholar 

  • Otter DW, Medina JR, Kalita JK (2020) A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst 32(2):604–624

    MathSciNet  Google Scholar 

  • Pantic I, Paunovic J, Cumic J, Valjarevic S, Petroianu GA, Corridon PR (2022) Artificial neural networks in contemporary toxicology research. Chemico-Biol Interact 369:110269

    Google Scholar 

  • Park SH, Park JK (2016) IoT industry & security technology trends. Int J Adv Smart Converg 5(3):27–31

    Google Scholar 

  • Park K, Kim J, Lee J (2019) Visual field prediction using recurrent neural network. Sci Rep 9(1):1–12

    Google Scholar 

  • Parthasarathy P, Vivekanandan S (2020) A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int J Comput Appl 42(3):222–232

    Google Scholar 

  • Phasinam K, Kassanuk T, Shinde PP, Thakar CM, Sharma DK, Mohiddin MK, Rahmani AW (2022) Application of IoT and cloud computing in automation of agriculture irrigation. J Food Qual 2022:1–8

    Google Scholar 

  • Popa D, Pop F, Serbanescu C, Castiglione A (2019) Deep learning model for home automation and energy reduction in a smart home environment platform. Neural Comput Appl 31(5):1317–1337

    Google Scholar 

  • Popović T, Latinović N, Pešić A, Zečević Ž, Krstajić B, Djukanović S (2017) Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: a case study. Comput Electron Agric 140:255–265

    Google Scholar 

  • Prakash CD, Karam LJ (2021) It GAN do better: GAN-based detection of objects on images with varying quality. IEEE Trans Image Process 30:9220–9230

    Google Scholar 

  • pytorch. https://pytorch.org

  • Qi M, Wang Y, Qin J, Li A, Luo J, Van Gool L (2019) StagNet: an attentive semantic RNN for group activity and individual action recognition. IEEE Trans Circuits Syst Video Technol 30(2):549–565

    Google Scholar 

  • Qiang N, Dong Q, Ge F, Liang H, Ge B, Zhang S, Sun Y, Gao J, Liu T (2020) Deep variational autoencoder for mapping functional brain networks. IEEE Trans Cogn Dev Syst 13(4):841–852

    Google Scholar 

  • Qu Y, Yu S, Zhou W, Tian Y (2020) GAN-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Trans Netw Sci Eng 7(4):2576–2586

    MathSciNet  Google Scholar 

  • Quispe R, Pedrini H (2019) Improved person re-identification based on saliency and semantic parsing with deep neural network models. Image Vis Comput 92:103809

    Google Scholar 

  • Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. arXiv preprint. https://arxiv.org/abs/1710.05941

  • Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49–57

    Google Scholar 

  • Rebennack S, Krasko V (2020) Piecewise linear function fitting via mixed-integer linear programming. INFORMS J Comput 32(2):507–530

    MathSciNet  MATH  Google Scholar 

  • Ruan Y-P, Ling Z (2021) Emotion-regularized conditional variational autoencoder for emotional response generation. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2021.3073809

    Article  Google Scholar 

  • Russell SJ (2010) Artificial intelligence a modern approach. Pearson Education Inc., London

    MATH  Google Scholar 

  • Saidi SJ, Matic S, Gasser O, Smaragdakis G, Feldmann A (2022) Deep dive into the IoT backend ecosystem. In: Proceedings of the 22nd ACM internet measurement conference. pp 488–503

  • Salari A, Djavadifar A, Liu XR, Najjaran H (2022) Object recognition datasets and challenges: a review. Neurocomputing

  • Samuel SSI (2016) A review of connectivity challenges in IoT-smart home. In: 2016 3rd MEC international conference on big data and smart city (ICBDSC). IEEE, pp 1–4

  • Sanchez-Iborra R, Cano M-D (2016) State of the art in LP-WAN solutions for industrial IoT services. Sensors 16(5):708

    Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  • Schmidt-Hieber J (2020) Nonparametric regression using deep neural networks with ReLU activation function. Ann Stat 48(4):1875–1897

    MathSciNet  MATH  Google Scholar 

  • Selvaraj S, Sundaravaradhan S (2020) Challenges and opportunities in IoT healthcare systems: a systematic review. SN Appl Sci 2(1):139

    Google Scholar 

  • Shah AM, Yan X, Shah SAA, Mamirkulova G (2020) Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach. J Ambient Intell Human Comput 11(7):2925–2942

    Google Scholar 

  • Shanthi T, Sabeenian RS (2019) Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 76:56–64

    Google Scholar 

  • Shao L, Zhu F, Li X (2014) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034

    MathSciNet  Google Scholar 

  • Sharma A, Liu X, Yang X, Shi D (2017) A patch-based convolutional neural network for remote sensing image classification. Neural Netw 95:19–28

    Google Scholar 

  • Shu Y, Yi R, Xia M, Ye Z, Zhao W, Chen Y, Lai Y-K, Liu Y-J (2021) GAN-based multi-style photo cartoonization. IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2021.3067201

    Article  Google Scholar 

  • Sicari S, Rizzardi A, Coen-Porisini A (2019) Smart transport and logistics: a node-RED implementation. Internet Technol Lett 2(2):e88

    Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint. https://arxiv.org/abs/1409.1556

  • Soui M, Smiti S, Mkaouer MW, Ejbali R (2020) Bankruptcy prediction using stacked auto-encoders. Appl Artif Intell 34(1):80–100

    Google Scholar 

  • Souibgui MA, Kessentini Y (2020) DE-GAN: a conditional generative adversarial network for document enhancement. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.3022406

    Article  Google Scholar 

  • Stiller B, Schiller E, Schmitt C, Ziegler S, James M (2020) An overview of network communication technologies for IoT. Handbook of Internet-of-Things, 12.

  • Su Y, Zhao Y, Sun M, Zhang S, Wen X, Zhang Y, Liu X, Liu X, Tang J, Wu W et al (2021) Detecting outlier machine instances through Gaussian mixture variational autoencoder with one dimensional CNN. IEEE Trans Comput 71(4):892–905

    MATH  Google Scholar 

  • Subetha T, Khilar R, Christo MS (2021) A comparative analysis on plant pathology classification using deep learning architecture—ResNet and VGG19. Mater Today. https://doi.org/10.1016/j.matpr.2020.11.993

    Article  Google Scholar 

  • Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615

    Google Scholar 

  • Sun Q, Liu X, Bourennane S, Liu B (2021) Multiscale denoising autoencoder for improvement of target detection. Int J Remote Sens 42(8):3002–3016

    Google Scholar 

  • Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT Press, Cambridge

    MATH  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1–9

  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818–2826

  • Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

  • Tan K, Xu B, Kumar A, Nachmani E, Adi Y (2020) SAGRNN: self-attentive gated RNN for binaural speaker separation with interaural cue preservation. IEEE Signal Process Lett 28:26–30

    Google Scholar 

  • Tang J, Deng C, Huang G-B (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821

    MathSciNet  Google Scholar 

  • Tao W, Li C, Song R, Cheng J, Liu Y, Wan F, Chen X (2020) EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2020.3025777

    Article  Google Scholar 

  • Tasyurek M, Celik M (2020) RNN-GWR: a geographically weighted regression approach for frequently updated data. Neurocomputing 399:258–270

    Google Scholar 

  • tesnsorflow. https://www.tensorflow.org

  • Thakur D, Kumar Y, Kumar A, Singh PK (2019) Applicability of wireless sensor networks in precision agriculture: a review. Wirel Pers Commun 107(1):471–512

    Google Scholar 

  • Thakur D, Kumar Y, Vijendra S (2020) Smart irrigation and intrusions detection in agricultural fields using IoT. Procedia Comput Sci 167:154–162

    Google Scholar 

  • Thies J, Alimohammad A (2019) Compact and low-power neural spike compression using undercomplete autoencoders. IEEE Trans Neural Syst Rehabil Eng 27(8):1529–1538

    Google Scholar 

  • Tigadi A, Gujanatti R, Gonchi A, Klemsscet B (2016) Advanced driver assistance systems. Int J Eng Res Gen Sci 4(3):151–158

    Google Scholar 

  • Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S, Bhardwaj S (2020) Potato leaf diseases detection using deep learning. In: 2020 4th international conference on intelligent computing and control systems (ICICCS). IEEE, pp 461–466

  • Tran N-T, Tran V-H, Nguyen N-B, Nguyen T-K, Cheung N-M (2021) On data augmentation for GAN training. IEEE Trans Image Process 30:1882–1897

    MathSciNet  Google Scholar 

  • Tzounis A, Katsoulas N, Bartzanas T, Kittas C (2017) Internet of things in agriculture, recent advances and future challenges. Biosyst Eng 164:31–48

    Google Scholar 

  • Ullah I, Ahmad S, Mehmood F, Kim D (2019) Cloud based IoT network virtualization for supporting dynamic connectivity among connected devices. Electronics 8(7):742

    Google Scholar 

  • Veeramakali T, Siva R, Sivakumar B, Senthil Mahesh PC, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. J Supercomput 77(9):9576–9596

    Google Scholar 

  • Vincent P (2011) A connection between score matching and denoising autoencoders. Neural Comput 23(7):1661–1674

    MathSciNet  MATH  Google Scholar 

  • Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232–242

    Google Scholar 

  • Wang S, Jiang Y, Hou X, Cheng H, Du S (2017) Cerebral micro-bleed detection based on the convolution neural network with rank based average pooling. IEEE Access 5:16576–16583

    Google Scholar 

  • Wang W, Yang D, Chen F, Pang Y, Huang S, Ge Y (2019) Clustering with orthogonal autoencoder. IEEE Access 7:62421–62432

    Google Scholar 

  • Wang Q, Bu S, He Z (2020a) Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Trans Ind Inform 16(10):6509–6517

    Google Scholar 

  • Wang X, Tan K, Du Q, Chen Y, Du P (2020b) CVA2E: a conditional variational autoencoder with an adversarial training process for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 58(8):5676–5692

    Google Scholar 

  • Wang J, Zhang W, Yang H, Michael Yeh C-C, Wang L (2021a) Visual analytics for RNN-based deep reinforcement learning. IEEE Trans Vis Comput Graph. https://doi.org/10.1109/TVCG.2021.3076749

    Article  Google Scholar 

  • Wang H, Lu B, Li J, Liu T, Xing Y, Lv C, Cao D, Li J, Zhang J, Hashemi E (2021b) Risk assessment and mitigation in local path planning for autonomous vehicles with LSTM based predictive model. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2021.3075773

    Article  Google Scholar 

  • Wang Y, Ma X, Wang J, Hou S, Dai J, Gu D, Wang H (2022) Robust AUV visual loop-closure detection based on variational autoencoder network. IEEE Trans Ind Inform 18(12):8829–8838

    Google Scholar 

  • Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput Appl 32(10):6111–6124

    MathSciNet  Google Scholar 

  • Wirges S, Stiller C, Hartenbach F (2018) Evidential occupancy grid map augmentation using deep learning. In: 2018 IEEE intelligent vehicles symposium (IV). IEEE, pp 668–673

  • Wortmann F, Flüchter K (2015) Internet of things. Bus Inf Syst Eng 57(3):221–224

    Google Scholar 

  • Wu H, Huang Q, Wang D, Gao L (2018) A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyogr Kinesiol 42:136–142

    Google Scholar 

  • Wu J-Y, Wu M, Chen Z, Li X-L, Yan R (2021) Degradation-aware remaining useful life prediction with LSTM autoencoder. IEEE Trans Instrum Meas 70:1–10

    Google Scholar 

  • Wu S, Sun F, Zhang W, Xie X, Cui B (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5):1–37

    Google Scholar 

  • Xia M, Shao H, Ma X, de Silva CW (2021) A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans Ind Inform 17(10):7050–7059

    Google Scholar 

  • Xia X, Pan X, Li N, He X, Ma L, Zhang X, Ding N (2022a) GAN-based anomaly detection: a review. Neurocomputing

  • Xia W, Zhang Y, Yang Y, Xue J-H, Zhou B, Yang M-H (2022b) GAN inversion: a survey. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2022.3181070

    Article  Google Scholar 

  • Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1492–1500

  • Xie Z, Jin L, Luo X, Sun Z, Liu M (2020a) RNN for repetitive motion generation of redundant robot manipulators: an orthogonal projection-based scheme. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3028304

    Article  Google Scholar 

  • Xie M, Li C, Liu X, Wong T-T (2020b) Manga filling style conversion with screentone variational autoencoder. ACM Trans Graph 39(6):1–15

    Google Scholar 

  • Xing Y, Lv C, Mo X, Hu Z, Huang C, Hang P (2021) Toward safe and smart mobility: energy-aware deep learning for driving behavior analysis and prediction of connected vehicles. IEEE Trans Intell Transport Syst 22(7):4267–4280

    Google Scholar 

  • Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2015) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130

    Google Scholar 

  • Xu Y, Chen Z, Xie Z, Wu L (2017) Quality assessment of building footprint data using a deep autoencoder network. Int J Geogr Inf Sci 31(10):1929–1951

    Google Scholar 

  • Xu J, Li Z, Du B, Zhang M, Liu J (2020a) Reluplex made more practical: leaky ReLU. In: 2020a IEEE symposium on computers and communications (ISCC). IEEE, pp 1–7

  • Xu D, Wei C, Peng P, Xuan Q, Guo H (2020b) GE-GAN: a novel deep learning framework for road traffic state estimation. Transport Res C 117:102635

    Google Scholar 

  • Xu L, Zhou X, Tao Y, Liu L, Yu X, Kumar N (2021) Intelligent security performance prediction for IoT-enabled healthcare networks using an improved cnn. IEEE Trans Ind Inform 18(3):2063–2074

    Google Scholar 

  • Yan X, Ai T, Yang M, Tong X (2021) Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. Int J Geogr Inf Sci 35(3):490–512

    Google Scholar 

  • Ye F, Bors AG (2021) Lifelong mixture of variational autoencoders. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3096457

    Article  Google Scholar 

  • Ye L, Liu Z, Wang Y (2020) Dual convolutional LSTM network for referring image segmentation. IEEE Trans Multimed 22(12):3224–3235

    Google Scholar 

  • Yeo Y-J, Shin Y-G, Park S, Ko S-J (2021) Simple yet effective way for improving the performance of GAN. IEEE Trans Neural Netw Learn Syst 33(4):1811–1818

    Google Scholar 

  • Yi J, Zhu Y, Xie J, Chen Z (2021) Cross-modal variational auto-encoder for content-based micro-video background music recommendation. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2021.3128254

    Article  Google Scholar 

  • Yu S, Principe JC (2019) Understanding autoencoders with information theoretic concepts. Neural Netw 117:104–123

    MATH  Google Scholar 

  • Yu X-M, Feng W-Z, Wang H, Chu Q, Chen Q (2020) An attention mechanism and multi-granularity-based Bi-LSTM model for chinese Q&A system. Soft Comput 24(8):5831–5845

    Google Scholar 

  • Yuan X, Li L, Shardt YAW, Wang Y, Yang C (2020) Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development. IEEE Trans Ind Electron 68(5):4404–4414

    Google Scholar 

  • Zabalza J, Ren J, Zheng J, Zhao H, Qing C, Yang Z, Du P, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185:1–10

    Google Scholar 

  • Zaimi A, Wabartha M, Herman V, Antonsanti P-L, Perone CS, Cohen-Adad J (2018) AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Sci Rep 8(1):1–11

    Google Scholar 

  • Zeng N, Zhang H, Song B, Liu W, Li Y, Dobaie AM (2018) Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273:643–649

    Google Scholar 

  • Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017a) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155

    MathSciNet  MATH  Google Scholar 

  • Zhang G, Kou L, Zhang L, Liu C, Da Q, Sun J (2017b) A new digital watermarking method for data integrity protection in the perception layer of IoT. Secur Commun Netw. https://doi.org/10.1155/2017/3126010

    Article  Google Scholar 

  • Zhang L, Wang S, Liu B (2018a) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1253

    Google Scholar 

  • Zhang H, Weng T-W, Chen P-Y, Hsieh C-J, Daniel L (2018b) Efficient neural network robustness certification with general activation functions. Advances in neural information processing systems, vol 31

  • Zhang M, Li W, Tao R, Li H, Du Q (2021a) Information fusion for classification of hyperspectral and LiDAR data using IP-CNN. IEEE Trans Geosci Remote Sens 60:1–12

    Google Scholar 

  • Zhang H, Yuan J, Tian X, Ma J (2021b) GAN-FM: infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators. IEEE Trans Comput Imaging 7:1134–1147

    MathSciNet  Google Scholar 

  • Zhang Q, Zeng F, Xiao Z, Jiang H, Regan AC, Yang K, Zhu Y (2022) Toward predicting stay time for private car users: a RNN-NALU approach. IEEE Trans Veh Technol 71(6):6007–6018

    Google Scholar 

  • Zhao C, Gong J, Lu C, Xiong G, Mei W (2017) Speed and steering angle prediction for intelligent vehicles based on deep belief network. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC). IEEE, pp 301–306

  • Zhao Z-Q, Zheng P, Xu S-T, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Google Scholar 

  • Zhao T, Li F, Tian P (2020) A deep-learning method for device activity detection in MMTC under imperfect CSI based on variationalautoencoder. IEEE Trans Veh Technol 69(7):7981–7986

    Google Scholar 

  • Zheng W, Wang K, Wang F-Y (2020) GAN-based key secret-sharing scheme in blockchain. IEEE Trans Cybern 51(1):393–404

    Google Scholar 

  • Zheng Y, Sui X, Jiang Y, Che T, Zhang S, Yang J, Li H (2021) SymReg-GAN: symmetric image registration with generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 44(9):5631–5646

    Google Scholar 

  • Zhou M (2022) Evolution from AI, IoT and Big Data analytics to metaverse. IEEE/CAA J Autom Sin 9(12):2041–2042

    MathSciNet  Google Scholar 

  • Zhou X, Li Y, Liang W (2020) CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans Comput Biol Bioinform 18(3):912–921

    Google Scholar 

  • Zhu X, Luo Y, Liu A, Tang W, Bhuiyan MZA (2020) A deep learning-based mobile crowdsensing scheme by predicting vehicle mobility. IEEE Trans Intell Transport Syst 22(7):4648–4659

    Google Scholar 

  • Zou J, Han Y, So S-S (2008) Overview of artificial neural networks. In: Artificial neural networks. pp 14–22

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DT wrote the manuscript. JKS and SS provided the relevant information and ideas. All the authors reviewed the manuscript.

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Correspondence to Divyansh Thakur.

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Thakur, D., Saini, J.K. & Srinivasan, S. DeepThink IoT: The Strength of Deep Learning in Internet of Things. Artif Intell Rev 56, 14663–14730 (2023). https://doi.org/10.1007/s10462-023-10513-4

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