如何利用Web of Science和EndNote创建文献列表

使用Web of Science可以按主题、标题、出版物、机构、...或它们的组合，检索到相关文献。以AI and Arts作为主题词组合为例，可以检索到9 450篇文献。

可以针对文献进行筛选过滤。例如，选择高被引论文过滤，得到59篇。可以进一步对这59篇论文进行分析。点击分析检索结果，可以按作者、机构、方向、国家、语种等等等排序。

如果选择了所属机构，那么得到按机构排名的清单。针对本主题，麻省理工学院、清华大学和中国电子科技大学并列第一。

现在不去介绍更多结果分析，而是看看如何把检索到的文献放到文献列表中。还是以刚才59篇论文为例。在当前文献列表窗口上方有导出功能。可以选择不同的导出格式。

如果选择excel格式，那么导出的是excel格子。这样的文献列表显然不具备可读性，也很难放到文章的参考文献列表中。

正确的方式是，导出为EndNote格式。这时，会生成一个可以用EndNote工具打开的文件（如savedrecs.ciw）。

使用EndNote打开刚才生成的文件（如savedrecs.ciw），在EndNote里就自动出现了刚才的59篇文献列表。

现在从文件（File）中选择导出（Export）。

导出后，得到一个文本文件（文件名缺省为My EndNote Library）。打开文本文件，就得到文献列表。

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2.Wang, S.-H., et al., COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion, 2021. 68: p. 131-148.

3.Ivanov, D., et al., Researchers' perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research, 2021. 59(7): p. 2055-2078.

4.Fan, C., et al., Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management. International Journal of Information Management, 2021. 56.

5.Dong, B., et al., Technology evolution from self-powered sensors to AIoT enabled smart homes. Nano Energy, 2021. 79.

6.Bhattacharya, S., et al., Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable Cities and Society, 2021. 65.

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8.Wan, S., Z. Gu, and Q. Ni, Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 2020. 149: p. 99-106.

9.Udrescu, S.-M. and M. Tegmark, Al Feynman: A physics-inspired method for symbolic regression. Science Advances, 2020. 6(16).

10.Tiyasha, T. Tran Minh, and Z.M. Yaseen, A survey on river water quality modelling using artificial intelligence models: 2000-2020. Journal of Hydrology, 2020. 585.

11.Sun, L., et al., Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection. Journal of Structural Engineering, 2020. 146(5).

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14.Ko, H., et al., COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation. Journal of Medical Internet Research, 2020. 22(6).

15.Kato, N., et al., Ten Challenges in Advancing Machine Learning Technologies toward 6G. Ieee Wireless Communications, 2020. 27(3): p. 96-103.

16.Grigorescu, S., et al., A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 2020. 37(3): p. 362-386.

17.Duan, J., et al., Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations. Ieee Transactions on Power Systems, 2020. 35(1): p. 814-817.

18.Deng, L., et al., Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey. Proceedings of the Ieee, 2020. 108(4): p. 485-532.

19.Darko, A., et al., Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 2020. 112.

20.Bhagat, S.K., T. Tran Minh, and Z.M. Yaseen, Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. Journal of Cleaner Production, 2020. 250.

21.Albahri, A.S., et al., Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review. Journal of Medical Systems, 2020. 44(7).

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23.Ting, D.S.W., et al., Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 2019. 103(2): p. 167-175.

24.Tao, F., et al., Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, 2019. 5(4): p. 653-661.

25.Stephenson, N., et al., Survey of Machine Learning Techniques in Drug Discovery. Current Drug Metabolism, 2019. 20(3): p. 185-193.

26.Nawaz, S.J., et al., Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future. Ieee Access, 2019. 7: p. 46317-46350.

27.Long, M., et al., Transferable Representation Learning with Deep Adaptation Networks. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2019. 41(12): p. 3071-3085.

28.Dey, D., et al., Artificial Intelligence in Cardiovascular Imaging JACC State-of-the-Art Review. Journal of the American College of Cardiology, 2019. 73(11): p. 1317-1335.

29.Cao, B., et al., Intelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework. Ieee Communications Magazine, 2019. 57(3): p. 56-62.

30.Baryannis, G., et al., Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 2019. 57(7): p. 2179-2202.

31.Pan, X. and A.F.d.C. Hamilton, Why and how to use virtual reality to study human social interaction: The challenges of exploring a new research landscape. British Journal of Psychology, 2018. 109(3): p. 395-417.

32.Sze, V., et al., Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proceedings of the Ieee, 2017. 105(12): p. 2295-2329.

33.Poria, S., et al., A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 2017. 37: p. 98-125.

34.Chen, Y.-H., et al., Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks. Ieee Journal of Solid-State Circuits, 2017. 52(1): p. 127-138.

35.Chapi, K., et al., A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 2017. 95: p. 229-245.

36.Abar, S., et al., Agent Based Modelling and Simulation tools: A review of the state-of-art software. Computer Science Review, 2017. 24: p. 13-33.

37.Seyedmahmoudian, M., et al., State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - A review. Renewable & Sustainable Energy Reviews, 2016. 64: p. 435-455.

38.Yu, J.J.Q. and V.O.K. Li, A social spider algorithm for global optimization. Applied Soft Computing, 2015. 30: p. 614-627.

39.Yaseen, Z.M., et al., Artificial intelligence based models for stream-flow forecasting: 2000-2015. Journal of Hydrology, 2015. 530: p. 829-844.

40.Lu, J., et al., Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 2015. 80: p. 14-23.

41.Zhang, M.-L. and Z.-H. Zhou, A Review on Multi-Label Learning Algorithms. Ieee Transactions on Knowledge and Data Engineering, 2014. 26(8): p. 1819-1837.

42.Long, M., et al., Adaptation Regularization: A General Framework for Transfer Learning. Ieee Transactions on Knowledge and Data Engineering, 2014. 26(5): p. 1076-1089.

43.Bengler, K., et al., Three Decades of Driver Assistance Systems Review and Future Perspectives. Ieee Intelligent Transportation Systems Magazine, 2014. 6(4): p. 6-22.

44.Lara, O.D. and M.A. Labrador, A Survey on Human Activity Recognition using Wearable Sensors. Ieee Communications Surveys and Tutorials, 2013. 15(3): p. 1192-1209.

45.Gao, W.-f., S.-y. Liu, and L.-l. Huang, A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning. Ieee Transactions on Cybernetics, 2013. 43(3): p. 1011-1024.

46.Acampora, G., et al., A Survey on Ambient Intelligence in Healthcare. Proceedings of the Ieee, 2013. 101(12): p. 2470-2494.

47.Grondman, I., et al., A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 2012. 42(6): p. 1291-1307.

48.Chen, L., et al., Sensor-Based Activity Recognition. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 2012. 42(6): p. 790-808.

49.Browne, C.B., et al., A Survey of Monte Carlo Tree Search Methods. Ieee Transactions on Computational Intelligence and Ai in Games, 2012. 4(1): p. 1-43.

50.Monmasson, E., et al., FPGAs in Industrial Control Applications. Ieee Transactions on Industrial Informatics, 2011. 7(2): p. 224-243.