Brain intelligence: go beyond artificial intelligence

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

Bidirectional recurrent neural networks

Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542 · see citing works

Large-Scale Video Classification with Convolutional Neural Networks

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,882 · see citing works

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works

A million spiking-neuron integrated circuit with a scalable communication network and interface

Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Chen, G., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,137 · see citing works

A Convolutional Neural Network for Modelling Sentences

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · see citing works

Evolving Neural Networks through Augmenting Topologies

Stanley, K. O., & Miikkulainen, R. P. (2002) · Evolutionary Computation · doi:10.1162/106365602320169811 · Cited by 3,269 · see citing works

Large Scale Distributed Deep Networks

Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works

A Fast and Accurate Dependency Parser using Neural Networks

Chen, D., & Manning, C. D. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1082 · Cited by 1,810 · see citing works

Hybrid speech recognition with Deep Bidirectional LSTM

Graves, A., Jaitly, N., & Mohamed, A. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707742 · Cited by 1,790 · see citing works

cuDNN: Efficient Primitives for Deep LearningPreprint

Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J. J., Catanzaro, B., & Shelhamer, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0759 · Cited by 1,394 · see citing works

Connectionist Speech Recognition: A Hybrid Approach

Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115 · see citing works

Multi-column deep neural network for traffic sign classification

Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057 · see citing works

A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

Stanley, K. O., D’Ambrosio, D. B., & Gauci, J. (2009) · Artificial Life · doi:10.1162/artl.2009.15.2.15202 · Cited by 781 · see citing works

Large-scale deep unsupervised learning using graphics processors

Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742 · see citing works

Lessons from the Netflix prize challenge

Bell, R. M., & Koren, Y. (2007) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1345448.1345465 · Cited by 727 · see citing works

Untitled work

(2013) · Cited by 651 · see citing works

Project Adam: building an efficient and scalable deep learning training system

Chilimbi, T. M., Suzue, Y., Apacible, J., & Kalyanaraman, K. (2014) · Operating Systems Design and Implementation · doi:10.5555/2685048.2685094 · Cited by 614 · see citing works

Multi-task Sequence to Sequence LearningPreprint

Luong, M.-T., Le, Q. V., Sutskever, I., Vinyals, O., & Kaiser, Ł. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06114 · Cited by 606 · see citing works

Connectionist Speech Recognition

Bourlard, H. A., & Morgan, N. H. (1994) · Springer eBooks · doi:10.1007/978-1-4615-3210-1 · Cited by 587 · see citing works

Three new graphical models for statistical language modelling

Mnih, A., & Hinton, G. E. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273577 · Cited by 569 · see citing works

Classifying Relations by Ranking with Convolutional Neural Networks

dos Santos, C., Xiang, B., & Zhou, B. (2015) · doi:10.3115/v1/p15-1061 · Cited by 504 · see citing works

Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems

Chen, M., Ma, Y., Li, Y., et al. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600410cm · Cited by 379 · see citing works

Drought forecasting using feed-forward recursive neural network

Mishra, A. K., & Desai, V. R. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.04.017 · Cited by 374 · see citing works

SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor

Khan, M. M., Lester, D., Plana, L. A., Rast, A. D., Jin, X., Painkras, E., & Furber, S. B. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634199 · Cited by 282 · see citing works

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282 · see citing works

Provable Bounds for Learning Some Deep RepresentationsPreprint

Arora, S., Bhaskara, A., Ge, R., & Ma, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.6343 · Cited by 251 · see citing works

Muscle satellite cell heterogeneity and self-renewal

Motohashi, N., & Asakura, A. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00001 · Cited by 251 · see citing works

What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

Mei, H., Bansal, M., & Walter, M. R. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1086 · Cited by 251 · see citing works

Contrast enhancement for images in turbid water

Lu, H., Li, Y., Zhang, L., et al. (2015) · Journal of the Optical Society of America A · doi:10.1364/josaa.32.000886 · Cited by 231 · see citing works

Wound intensity correction and segmentation with convolutional neural networks

Lu, H., Li, B., Zhu, J., Li, Y., Li, Y., Xu, X., He, L., Li, X. S., Li, J., & Serikawa, S. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3927 · Cited by 214 · see citing works

Underwater image de-scattering and classification by deep neural network

Li, Y., Li, Y., Lu, H., et al. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.08.008 · Cited by 177 · see citing works

Deep Learning on FPGAs: Past, Present, and FuturePreprint

Lacey, G., Taylor, G. W., & Areibi, S. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04283 · Cited by 157 · see citing works

Big-Data Analytics for Cloud, IoT and Cognitive Computing

Hwang, K., & Chen, M. (2017) · Cited by 137 · see citing works

A New Approach to Automating Services

Lacity, M. C., & Willcocks, L. P. (2018) · The MIT Press eBooks · doi:10.7551/mitpress/11633.003.0015 · Cited by 121 · see citing works

Green data center with IoT sensing and cloud-assisted smart temperature control system

Liu, Q., Ma, Y., Alhussein, M., Zhang, Y., & Peng, L. (2016) · Computer Networks · doi:10.1016/j.comnet.2015.11.024 · Cited by 118 · see citing works

GroRec: A Group-Centric Intelligent Recommender System Integrating Social, Mobile and Big Data Technologies

Zhang⋆, Y. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2592520 · Cited by 111 · see citing works

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder

Bartsch, G. C., Mitra, A. P., Mitra, S. A., et al. (2015) · The Journal of Urology · doi:10.1016/j.juro.2015.09.090 · Cited by 58 · see citing works

Evolving multimodal behavior with modular neural networks in Ms. Pac-Man

Schrum, J., & Miikkulainen, R. P. (2014) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2576768.2598234 · Cited by 42 · see citing works

The New Dawn of Virtual Reality in Health Care: Medical Simulation and Experiential Interface

Riva, G., & Wiederhold, B. K. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-595-1-3 · Cited by 40 · see citing works

TOLA: Topic-oriented learning assistance based on cyber-physical system and big data

Song, J., Zhang⋆, Y., Duan, K., Hossain, M. S., & Rahman, S. M. M. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.05.040 · Cited by 37 · see citing works

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

Labonnote, N., & Høyland, K. (2015) · Intelligent Buildings International · doi:10.1080/17508975.2015.1048767 · Cited by 34 · see citing works

An Effective Method for Evolving Reaction Networks in Synthetic Biochemical Systems

Dinh, H. Q., Aubert-Kato, N., Noman, N., Fujii, T., Rondelez, Y., & Iba, H. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2326863 · Cited by 32 · see citing works

Integrated Business Prestige and Artificial Intelligence for Corporate Decision Making in Dynamic Environments

Lin, W.-K., Lin, S., & Yang, T.-N. (2017) · Cybernetics & Systems · doi:10.1080/01969722.2017.1284533 · Cited by 21 · see citing works

Context-Dependent Translation Selection Using Convolutional Neural Network

Hu, B., Tu, Z., Lu, Z., Li, H., & Chen, Q. (2015) · doi:10.3115/v1/p15-2088 · Cited by 18 · see citing works

Proposal of a System of Function-Discovery Using a Bug Type of Artificial Life

Serikawa, S., & Shimomura, T. (1998) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss1987.118.2_170 · Cited by 10 · see citing works

Survey of scaling platforms for Deep Neural Networks

Ratnaparkhi, A. A., Pilli, E. S., & Joshi, R. C. (2016) · International Conference on Emerging Trends in Communication Technologies (ETCT) · doi:10.1109/etct.2016.7882969 · Cited by 8 · see citing works

The internet of things and the connected world [The President's Page]

Freeman, H., & Drobot, A. T. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7452256 · Cited by 4 · see citing works

Deep learning modelling techniques: Current progress, applications, advantages, and challenges

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works

Blockchain for AI: review and open research challenges

Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019) · IEEE Access · doi:10.1109/access.2018.2890507 · Cited by 1,001 · see citing works

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536 · see citing works

NAS-Unet: Neural Architecture Search for Medical Image Segmentation

Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487 · see citing works

Reliability of analog resistive switching memory for neuromorphic computing

Zhao, M., Gao, B., Tang, J., Qian, H., & Wu, H. (2020) · Applied Physics Reviews · doi:10.1063/1.5124915 · Cited by 349 · see citing works

The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities

Yigitcanlar, T., & Cugurullo, F. (2020) · Sustainability · doi:10.3390/su12208548 · Cited by 337 · see citing works

Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices

Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245 · see citing works

Artificial intelligence and social media on academic performance and mental well-being: student perceptions of positive impact in the age of smart learning

Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29523 · Cited by 205 · see citing works

Applications of AI in classical software engineering

Barenkamp, M., Rebstadt, J., & Thomas, O. (2020) · AI Perspectives · doi:10.1186/s42467-020-00005-4 · Cited by 123 · see citing works

Trends in artificial intelligence in nursing: Impacts on nursing management

Chang, C., Jen, H., & Su, W. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13770 · Cited by 76 · see citing works

Construction and Exploration of an Intelligent Evaluation System for Educational APP through Artificial Intelligence Technology

Tang, J., & Hai, L. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i05.20293 · Cited by 20 · see citing works

Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks

Teng, Y., Lu, H., Li, Y., Kamiya, T., Nakatoh, Y., Serikawa, S., & Gao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3168303 · Cited by 15 · see citing works

Study on application of information and communication technology in special education

Han, D., & Kang, M. (2012) · Journal of the Korean Data and Information Science Society · doi:10.7465/jkdi.2012.23.5.927 · Cited by 5 · see citing works

Effect of Information and Communication Technology(ICT) Utilization on Manufacturing Cost Structure

Lim, K.-T., & Koo, J.-H. (2020) · KOREAN JOURNAL OF MANAGEMENT ACCOUNTING RESEARCH · doi:10.31507/kjmar.2020.8.20.2.27

The role of information and communication technologies (ICT) has been specified as a new industry in many parts of the world. Although ICT industry has spread to some extent in Iran, its employment generation effects is still undrestimated. In this articl

میرزایی, م., & اربابیان, ا. (2007) · پژوهش های اقتصاد پولی، مالی · doi:10.22067/pm.v14i20.237

Resource-Constrained Social Evidence Based Cognitive Model for Empathy-Driven Artificial Intelligence

Kolonin, A. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97676-1_10 · Cited by 2 · see citing works

Supporting Artificial Social Intelligence With Theory of Mind

Williams, J., Fiore, S. M., & Jentsch, F. G. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.750763 · Cited by 109 · see citing works