Learning Deep Architectures for AI

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Optimization by Simulated Annealing

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Reinforcement Learning: An Introduction

(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

Big Data and supply chain management: A review and bibliometric analysis

Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018) · Annals of Operations Research · doi:10.1007/s10479-016-2236-y · Cited by 382

Opinion Mining with Deep Recurrent Neural Networks

Irsoy, O., & Cardie, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1080 · Cited by 384

Text feature extraction based on deep learning: a review

Liang, H., Sun, X., Sun, Y., & Gao, Y. (2017) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-017-0993-1 · Cited by 373

Droid-Sec: deep learning in android malware detection

Yuan, Z., Lu, Y., Wang, Z., & Xue, Y. (2014) · Proceedings of the 2014 ACM conference on SIGCOMM · doi:10.1145/2619239.2631434 · Cited by 371

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Zhang, Y., Li, P., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2903723 · Cited by 359

Advances and opportunities in machine learning for process data analytics

Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342

A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2662008 · Cited by 333

Deep Structured Energy Based Models for Anomaly Detection

Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334

Deep neural networks for anatomical brain segmentation

de Brebisson, A., & Montana, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301312 · Cited by 330

Detection of Co-salient Objects by Looking Deep and Wide

Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0907-4 · Cited by 328

Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]

Rafique, D., & Velasco, L. (2018) · Journal of Optical Communications and Networking · doi:10.1364/jocn.10.00d126 · Cited by 298

Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges

Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295

Untitled work

(2013) · Cited by 290

Visualizing the Hidden Activity of Artificial Neural Networks

Rauber, P. E., Fadel, S. G., Falcao, A. X., & Telea, A. C. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598838 · Cited by 280

Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network

Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., & de Geus, P. (2017) · 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2017.00-19 · Cited by 285

Mobile sensor data anonymization

Malekzadeh, M., Clegg, R. G., Cavallaro, A., & Haddadi, H. (2019) · Proceedings of the International Conference on Internet of Things Design and Implementation · doi:10.1145/3302505.3310068 · Cited by 298

A discriminative deep model for pedestrian detection with occlusion handling

Ouyang, W., & Wang, X. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248062 · Cited by 281

Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries

Bian, C., He, H., & Yang, S. (2020) · Energy · doi:10.1016/j.energy.2019.116538 · Cited by 284

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274

Maximum Entropy Deep Inverse Reinforcement Learning

Wulfmeier, M., Ondrúška, P., & Posner, I. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.04888 · Cited by 272

fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs

Venieris, S. I., & Bouganis, C.-S. (2016) · 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2016.22 · Cited by 267

A patch-based convolutional neural network for remote sensing image classification

Sharma, A., Liu, X., Yang, X., & Shi, D. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.07.017 · Cited by 260

Deep learning for human affect recognition: Insights and new developments

Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

Glavic, M., Fonteneau, R., & Ernst, D. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1217 · Cited by 241

Multimodal emotion recognition using deep learning architectures

Ranganathan, H., Chakraborty, S., & Panchanathan, S. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477679 · Cited by 249

Studying Very Low Resolution Recognition Using Deep Networks

Wang, Z., Chang, S., Yang, Y., Liu, D., & Huang, T. S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.518 · Cited by 235

A renaissance of neural networks in drug discovery

Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235

Real-time scheduling for a smart factory using a reinforcement learning approach

Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225

The state of the art of deep learning models in medical science and their challenges

Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222

Authorship Attribution for Social Media Forensics

Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222

Disentangling Factors of Variation for Facial Expression Recognition

Rifai, S., Bengio, Y., Courville, A., Vincent, P., & Mirza, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_58 · Cited by 215

eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212

Mapping the Landscape of Human-Level Artificial General Intelligence

Adams, S. S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., Hall, J. S., Samsonovich, A., Scheutz, M., Schlesinger, M., et al. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2322 · Cited by 213

A review of privacy-preserving techniques for deep learning

Boulemtafes, A., Derhab, A., & Challal, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.041 · Cited by 211

Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

Cao, X.-C., Chen, B.-Q., Yao, B., & He, W.-P. (2019) · Computers in Industry · doi:10.1016/j.compind.2018.12.018 · Cited by 208

Exploring Representation Learning With CNNs for Frame-to-Frame Ego-Motion Estimation

Costante, G., Mancini, M., Valigi, P., & Ciarfuglia, T. A. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2505717 · Cited by 203

Disease inference from health-related questions via sparse deep learning

Nie, L., Wang, M., Zhang, L., Yan, S., Zhang, B., & Chua, T.-S. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2399298 · Cited by 195

The successor representation: Its computational logic and neural substrates

Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191

Deep learning for small and big data in psychiatry

Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189

Review on self-supervised image recognition using deep neural networks

Ohri, K., & Kumar, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107090 · Cited by 179

Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach

Yin, J., & Zhao, W. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.10.002 · Cited by 173

Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning

Mocanu, E., Nguyen, P. H., Kling, W. L., & Gibescu, M. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.01.030 · Cited by 171

A deep learning approach for fault diagnosis of induction motors in manufacturing

Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017) · Chinese Journal of Mechanical Engineering · doi:10.1007/s10033-017-0189-y · Cited by 167

Data Evaluation and Enhancement for Quality Improvement of Machine Learning

Chen, H., Chen, J., & Ding, J. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3070863 · Cited by 166

Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data

Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161

Bitwise Neural Networks

Kim, M., & Smaragdis, P. (2016) · arXiv · doi:10.48550/arxiv.1601.06071 · Cited by 166

fpgaConvNet: Mapping regular and irregular convolutional neural networks on FPGAs

Venieris, S. I., & Bouganis, C.-S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2844093 · Cited by 161

Are we predictive engines? Perils, prospects, and the puzzle of the porous perceiver

Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12002440 · Cited by 158

The Consciousness Prior

Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1709.08568 · Cited by 155

Big data analytics and computational intelligence for cyber–physical systems: recent trends and state of the art applications

Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.021 · Cited by 155

A deep auto-encoder based approach for intrusion detection system

Farahnakian, F., & Heikkonen, J. (2018) · 2018 20th International Conference on Advanced Communication Technology (ICACT) · doi:10.23919/icact.2018.8323687 · Cited by 146

Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning

Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., & Bando, T. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2649541 · Cited by 141

Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound

Wu, K., Chen, X., & Ding, M. (2014) · Optik · doi:10.1016/j.ijleo.2014.01.114 · Cited by 139

A bird's-eye view of deep learning in bioimage analysis

Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126

A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities

Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2015.7299406 · Cited by 136

Promise and pitfalls of g-ratio estimation with MRI

Campbell, J. S. W., Leppert, I. R., Narayanan, S., Boudreau, M., Duval, T., Cohen-Adad, J., Pike, G. B., & Stikov, N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.038 · Cited by 129

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

Lee, H., Lee, S. H., & Quek, T. Q. S. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933890 · Cited by 129

A Critical Review of Recent Trends, and a Future Perspective of Optical Spectroscopy as PAT in Biopharmaceutical Downstream Processing

Rolinger, L., Rüdt, M., & Hubbuch, J. (2020) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-020-02407-z · Cited by 126

A Survey on Differentially Private Machine Learning [Review Article]

Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.2976185 · Cited by 115

A computational framework for understanding antecedents of guests’ perceived trust towards hosts on Airbnb

Zhang, L., Yan, Q., & Zhang, L. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.10.002 · Cited by 114

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110

Soft errors in DNN accelerators: A comprehensive review

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

Towards real-time Speech Emotion Recognition using deep neural networks

Fayek, H. M., Lech, M., & Cavedon, L. (2015) · 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS) · doi:10.1109/icspcs.2015.7391796 · Cited by 103

A Deep and Tractable Density Estimator

Uria, B., Murray, I., & Larochelle, H. (2013) · arXiv · doi:10.48550/arxiv.1310.1757 · Cited by 100

Decision trees do not generalize to new variations

Bengio, Y., Delalleau, O., & Simard, C. (2010) · Computational Intelligence · doi:10.1111/j.1467-8640.2010.00366.x · Cited by 89

Quantum deep learning

Wiebe, N., Kapoor, A., & Svore, K. M. (2016) · Quantum Information and Computation · doi:10.26421/qic16.7-8-1 · Cited by 85

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

Sharma, P., Sarma, K. K., & Mastorakis, N. E. (2020) · IEEE Access · doi:10.1109/access.2020.3044453 · Cited by 83

Growing random forest on deep convolutional neural networks for scene categorization

Bai, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.038 · Cited by 81

Detecting predatory conversations in social media by deep convolutional neural networks

Ebrahimi, M., Suen, C. Y., & Ormandjieva, O. (2016) · Digital Investigation · doi:10.1016/j.diin.2016.07.001 · Cited by 78

Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

Wu, D., Lawhern, V. J., Hairston, W. D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2544108 · Cited by 74

Integrating Learning and Reasoning with Deep Logic Models

Marra, G., Giannini, F., Diligenti, M., & Gori, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-46147-8_31 · Cited by 71

Enhancing the prediction of disease-gene associations with multimodal deep learning

Luo, P., Li, Y., Tian, L.-P., & Wu, F.-X. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz155 · Cited by 67

Meta Cancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Albaradei, S., Napolitano, F., Thafar, M. A., Gojobori, T., Essack, M., & Gao, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.006 · Cited by 61

Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning

Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116113 · Cited by 61

iHear Food: Eating Detection Using Commodity Bluetooth Headsets

Gao, Y., Zhang, N., Wang, H., Ding, X., Ye, X., Chen, G., & Cao, Y. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.14 · Cited by 63

The cascading neural network: building the Internet of Smart Things

Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59

Deep learning algorithms for traffic forecasting: a comprehensive review and comparison with classical ones

Afandizadeh, S., Abdolahi, S., & Mirzahossein, H. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/9981657 · Cited by 64

Ensemble Deep Learning for Biomedical Time Series Classification

Jin, L.-P., & Dong, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6212684 · Cited by 60

Security and Privacy Issues in Deep Learning

Bae, H., Jang, J., Jung, D., Jang, H., Ha, H., Lee, H., & Yoon, S. (2018) · arXiv · doi:10.48550/arxiv.1807.11655 · Cited by 56

Arabic Cyberbullying Detection: Using Deep Learning

Haidar, B., Chamoun, M., & Serhrouchni, A. (2018) · 2018 7th International Conference on Computer and Communication Engineering (ICCCE) · doi:10.1109/iccce.2018.8539303 · Cited by 58

A deep learning framework for hybrid heterogeneous transfer learning

Zhou, J. T., Pan, S. J., & Tsang, I. W. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2019.06.001 · Cited by 53

Predicting Seizures from Electroencephalography Recordings: A Knowledge Transfer Strategy

Liang, J., Lu, R., Zhang, C., & Wang, F. (2016) · 2016 IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2016.27 · Cited by 49

Event-based media processing and analysis: A survey of the literature

Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49

Regularizing deep networks with prior knowledge: A constraint-based approach

Roychowdhury, S., Diligenti, M., & Gori, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106989 · Cited by 46

A novel feature extraction method for scene recognition based on Centered Convolutional Restricted Boltzmann Machines

Gao, J., Yang, J., Wang, G., & Li, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.055 · Cited by 44

Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data

Nie, S., Yu, J., Chen, P., Wang, Y., & Zhang, J. Q. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2016.08.034 · Cited by 42

EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model

Yin, J., Qiao, Z., Han, L., & Zhang, X. (2026) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2025.2477801 · Cited by 41

Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units

Montúfar, G. F. (2014) · Neural Computation · doi:10.1162/neco_a_00601 · Cited by 38

Predicting online shopping cart abandonment with machine learning approaches

Rausch, T. M., Derra, N. D., & Wolf, L. (2022) · International Journal of Market Research · doi:10.1177/1470785320972526 · Cited by 36

A Multitask Grocery Assist System for the Visually Impaired: Smart glasses, gloves, and shopping carts provide auditory and tactile feedback

Advani, S., Zientara, P., Shukla, N., Okafor, I., Irick, K., Sampson, J., Datta, S., & Narayanan, V. (2017) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2016.2614422 · Cited by 32

Cross-dataset deep transfer learning for activity recognition

Gjoreski, M., Kalabakov, S., Luštrek, M., Gams, M., & Gjoreski, H. (2019) · Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers · doi:10.1145/3341162.3344865 · Cited by 28

Towards large-scale multimedia retrieval enriched by knowledge about human interpretation: Retrospective survey

Shirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24

Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait

Fernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., Ferreira, C., Gama, J., Erlhagen, W., & Bicho, E. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621466 · Cited by 17

Potential for standardization and automation for pathology and endoscopy in inflammatory bowel disease

Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16

Multi-modal panoramic 3D outdoor datasets for place categorization

Jung, H., Oto, Y., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759669 · Cited by 16

Connected and Automated Vehicles

Peng, H. (2016) · Mechanical Engineering · doi:10.1115/1.2016-dec-2 · Cited by 15

The changing fortunes of pattern recognition and computer vision

Chellappa, R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.04.005 · Cited by 8

Improving sEMG-Based Hand Gesture Recognition Using Maximal Overlap Discrete Wavelet Transform and an Autoencoder Neural Network

de Andrade, F. H. C., Pereira, F. G., Resende, C. Z., & Cavalieri, D. C. (2019) · IFMBE Proceedings · doi:10.1007/978-981-13-2517-5_42 · Cited by 8

Estimation of Distribution using Population Queue based Variational Autoencoders

Bhattacharjee, S., & Gras, R. (2019) · 2019 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790077 · Cited by 7