{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T19:00:43Z","timestamp":1787338843578,"version":"build-2736575974"},"reference-count":73,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the relational structure between authors to identify key contributors, and extracts major themes from extensive texts to highlight essential research content. To achieve these objectives, trend analysis, network analysis, and topic modeling were conducted on 352 research papers collected from the Web of Science between 2004 and 2023. To ensure the validity of the topic modeling results, a topic correlation matrix was also performed. The results revealed three key findings. First, SDL research has surged since 2019, driven by advancements in AI technologies, reflecting heightened activity in this field. Second, modern scientific research is advancing with a focus on data-driven approaches, artificial intelligence applications, and experimental optimization through the utilization of SDLs. Third, SDL research exhibits interdisciplinary convergence, encompassing material optimization, biological processes, and AI predictive algorithms. This study underscores the growing importance of SDLs as a research tool across diverse academic disciplines and provides practical insights into sustainable future scientific research directions and strategic approaches.<\/jats:p>","DOI":"10.3390\/systems13040253","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T06:52:56Z","timestamp":1743749576000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1495-0167","authenticated-orcid":false,"given":"Woojun","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1110-2440","authenticated-orcid":false,"given":"Insung","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keuntae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Systems Management Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Garcia Martin, H., Radivojevic, T., Zucker, J., Bouchard, K.E., Sustarich, J., Peisert, S., Arnold, D., Hillson, N.J., Babnigg, G., and Mart\u00ed, J.M. (2022). Perspectives for Self-Driving Labs in Synthetic Biology. Curr. Opin. Biotechnol., 79.","DOI":"10.1016\/j.copbio.2022.102881"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1039\/D4DD00040D","article-title":"The Future of Self-Driving Laboratories: From Human in the Loop Interactive AI to Gamification","volume":"3","author":"Hysmith","year":"2024","journal-title":"Digit. Discov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/s44160-022-00231-0","article-title":"The Rise of Self-Driving Labs in Chemical and Materials Sciences","volume":"2","author":"Abolhasani","year":"2023","journal-title":"Nat. Synth."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1021\/acs.accounts.2c00220","article-title":"Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab","volume":"55","author":"Seifrid","year":"2022","journal-title":"Acc. Chem. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12992-024-01049-5","article-title":"The advancement of artificial intelligence in biomedical research and health innovation: Challenges and opportunities in emerging economies","volume":"20","year":"2024","journal-title":"Glob. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1039\/D3DD00223C","article-title":"Review of Low-Cost Self-Driving Laboratories in Chemistry and Materials Science: The \u201cFrugal Twin\u201d Concept","volume":"3","author":"Lo","year":"2024","journal-title":"Digit. Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"031406","DOI":"10.1063\/5.0048164","article-title":"Gryffin: An Algorithm for Bayesian Optimization of Categorical Variables Informed by Expert Knowledge","volume":"8","author":"Aldeghi","year":"2021","journal-title":"Appl. Phys. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2533\/chimia.2023.7","article-title":"How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science","volume":"77","author":"Gutierrez","year":"2023","journal-title":"Chimia"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s44286-023-00002-4","article-title":"Self-Driving Laboratories to Autonomously Navigate the Protein Fitness Landscape","volume":"1","author":"Rapp","year":"2024","journal-title":"Nat. Chem. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1021\/acs.jpca.0c09316","article-title":"Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network","volume":"125","author":"Ji","year":"2021","journal-title":"J. Phys. Chem. A"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jneumeth.2005.04.017","article-title":"An Autonomous Implantable Computer for Neural Recording and Stimulation in Unrestrained Primates","volume":"148","author":"Mavoori","year":"2005","journal-title":"J. Neurosci. Methods"},{"key":"ref_12","first-page":"2200331","article-title":"Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery","volume":"5","author":"Abolhasani","year":"2022","journal-title":"Adv. Intell. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10592","DOI":"10.1021\/acs.inorgchem.9b00109","article-title":"Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry","volume":"58","author":"Janet","year":"2019","journal-title":"Inorg. Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1038\/s41467-023-37139-y","article-title":"AlphaFlow: Autonomous Discovery and Optimization of Multi-Step Chemistry Using a Self-Driven Fluidic Lab Guided by Reinforcement Learning","volume":"14","author":"Volk","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1039\/D3DD00244F","article-title":"A Multiobjective Closed-Loop Approach Towards Autonomous Discovery of Electrocatalysts for Nitrogen Reduction","volume":"3","author":"Kavalsky","year":"2024","journal-title":"Digit. Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1038\/s41467-022-28580-6","article-title":"A self-driving laboratory advances the Pareto front for material properties","volume":"13","author":"MacLeod","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8867","DOI":"10.1126\/sciadv.aaz8867","article-title":"Self-Driving Laboratory for Accelerated Discovery of Thin-Film Materials","volume":"6","author":"MacLeod","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2102698","DOI":"10.1002\/aenm.202102698","article-title":"Implications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries","volume":"12","author":"Bhowmik","year":"2021","journal-title":"Adv. Energy Mater."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100264","DOI":"10.1016\/j.xcrp.2020.100264","article-title":"Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning","volume":"1","author":"Dave","year":"2020","journal-title":"Cell Rep. Phys. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7866","DOI":"10.1021\/acs.jpclett.1c01961","article-title":"Machine Learning Roadmap for Perovskite Photovoltaics","volume":"12","author":"Srivastava","year":"2021","journal-title":"J. Phys. Chem. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109995","DOI":"10.1016\/j.cie.2024.109995","article-title":"Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning","volume":"189","author":"Lin","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1126\/sciadv.aaz1708","article-title":"A Bayesian Experimental Autonomous Researcher for Mechanical Design","volume":"6","author":"Gongora","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1371\/journal.pone.0229862","article-title":"ChemOS: An Orchestration Software to Democratize Autonomous Discovery","volume":"15","author":"Roch","year":"2020","journal-title":"PLoS ONE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1021\/acsmaterialslett.1c00390","article-title":"Deep Reinforcement Learning for Digital Materials Design","volume":"3","author":"Sui","year":"2021","journal-title":"ACS Mater. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1126\/science.aaa8685","article-title":"Advances in Natural Language Processing","volume":"349","author":"Hirschberg","year":"2015","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Basu, S., Ahmed, M., Acharjee, P., and Saifuddin, M. (2024, January 2\u20134). Machine Learning-Driven Analysis of Suicidal Language on Social Media: A Dive into Natural Language Processing (NLP) Techniques. Proceedings of the 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh.","DOI":"10.1109\/ICEEICT62016.2024.10534569"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"29633","DOI":"10.1109\/ACCESS.2024.3368382","article-title":"Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis","volume":"12","author":"Attigeri","year":"2024","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"122263","DOI":"10.1016\/j.jclepro.2020.122263","article-title":"Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia","volume":"269","author":"Fatimah","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Oh, M., Ahn, C., Nam, H., and Choi, S. (2023). New Trends in Smart Cities: The Evolutionary Directions Using Topic Modeling and Network Analysis. Systems, 11.","DOI":"10.3390\/systems11080410"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nam, H., and Nam, T. (2021). Exploring strategic directions of pandemic crisis management: A text analysis of world economic forum COVID-19 reports. Sustainability, 13.","DOI":"10.20944\/preprints202103.0380.v1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Elmezain, M., Othman, E.A., and Ibrahim, H.M. (2021). Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis. Mathematics, 9.","DOI":"10.3390\/math9222850"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"\u017dalik, K.R., and \u017dalik, M. (2023). Density-Based Entropy Centrality for Community Detection in Complex Networks. Entropy, 25.","DOI":"10.3390\/e25081196"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, J., Tian, Y., Yao, Q., and Shi, Y. (2023). Structural Characteristics of Intergovernmental Water Pollution Control Cooperation Networks Using Social Network Analysis and GIS in Yangtze River Delta Urban Agglomeration, China. Sustainability, 15.","DOI":"10.3390\/su151813655"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101582","DOI":"10.1016\/j.is.2020.101582","article-title":"A Review of Topic Modeling Methods","volume":"94","author":"Vayansky","year":"2020","journal-title":"Inf. Syst"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.3390\/app13042250","article-title":"Sentiment Analysis and Topic Modeling Regarding Online Classes on the Reddit Platform: Educators versus Learners","volume":"13","author":"Li","year":"2023","journal-title":"Appl. Sci"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, H.-L., Chang, T.-W., and Choi, Y. (2018). Exploring the Research Trend of Smart Factory with Topic Modeling. Sustainability, 10.","DOI":"10.3390\/su10082779"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Choi, D., and Song, B. (2018). Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis. Sustainability, 10.","DOI":"10.3390\/su10082810"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yi, H., Qu, T., Zhang, K., Li, M., Huang, G.Q., and Chen, Z. (2023). Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions. Systems, 11.","DOI":"10.3390\/systems11070371"},{"key":"ref_39","first-page":"25","article-title":"Text mining: Use of TF-IDF to examine the relevance of words to documents","volume":"181","author":"Qaiser","year":"2018","journal-title":"Int. J. Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, S., Lu, S., Yin, Z., Li, X., Yin, L., Tian, J., and Zheng, W. (2023). Adapting Feature Selection Algorithms for the Classification of Chinese Texts. Systems, 11.","DOI":"10.3390\/systems11090483"},{"key":"ref_41","first-page":"285","article-title":"Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF)","volume":"7","author":"Christian","year":"2016","journal-title":"ComTech Comput. Math. Eng. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dogra, V., Alharithi, F.S., \u00c1lvarez, R.M., Singh, A., and Qahtani, A.M. (2022). NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange. Systems, 10.","DOI":"10.3390\/systems10060233"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"87873","DOI":"10.1109\/ACCESS.2024.3417180","article-title":"Novel Curriculum Learning Strategy Using Class-Based TF-IDF for Enhancing Personality Detection in Text","volume":"12","author":"Kwon","year":"2024","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/03081079.2017.1291635","article-title":"A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)","volume":"46","author":"Havrlant","year":"2017","journal-title":"Int. J. Gen. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"189636","DOI":"10.1109\/ACCESS.2024.3515087","article-title":"Quantitative Evaluation of Digital Economy Policies in the Tourism Industry Using the TF-IDF and PMC Index Model","volume":"12","author":"Xia","year":"2024","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, J.G., Lin, J.H., Guo, Q., and Zhou, T. (2016). Locating influential nodes via dynamics-sensitive centrality. Sci. Rep., 6.","DOI":"10.1038\/srep21380"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MIS.2009.36","article-title":"The unreasonable effectiveness of data","volume":"24","author":"Halevy","year":"2009","journal-title":"IEEE Intell. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/amiajnl-2011-000464","article-title":"Natural language processing: An introduction","volume":"18","author":"Nadkarni","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1109\/TSA.2005.858551","article-title":"Association pattern language modeling","volume":"14","author":"Chien","year":"2006","journal-title":"IEEE Trans. Audio Speech Lang. Process"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.poetic.2013.10.001","article-title":"Introduction\u2014Topic Models: What They Are and Why They Matter","volume":"41","author":"Mohr","year":"2013","journal-title":"Poetics"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1109\/TIFS.2020.3032021","article-title":"Latent Dirichlet Allocation Model Training With Differential Privacy","volume":"16","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.joi.2014.07.005","article-title":"Time Gap Analysis by the Topic Model-Based Temporal Technique","volume":"8","author":"Jeong","year":"2014","journal-title":"J. Informetr."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Vahedi, T., Ampel, B., Samtani, S., and Chen, H. (2021, January 2\u20133). Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach. Proceedings of the 2021 IEEE International Conference on Intelligence and Security Informatics (ISI), San Antonio, TX, USA.","DOI":"10.1109\/ISI53945.2021.9624765"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, L., Dai, D., Liu, H., Yuan, Y., Ding, L., and Xu, Y. (2022). Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM. Appl. Sci., 12.","DOI":"10.3390\/app122311902"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, J., Fan, Y., Zhang, H., and Feng, L. (2021). Technology Hotspot Tracking: Topic Discovery and Evolution of China\u2019s Blockchain Patents Based on a Dynamic LDA Model. Symmetry, 13.","DOI":"10.3390\/sym13030415"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"578","DOI":"10.3390\/ai2040035","article-title":"Emerging Research Topic Detection Using Filtered-LDA","volume":"2","author":"Alattar","year":"2021","journal-title":"AI"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1016\/j.giq.2018.07.005","article-title":"Dynamic capabilities of a smart city: An innovative approach to discovering urban problems and solutions","volume":"35","author":"Chong","year":"2018","journal-title":"Gov. Inf. Q."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Nam, H., Kim, S., and Nam, T. (2022). Identifying the directions of technology-driven government innovation. Information, 13.","DOI":"10.3390\/info13050208"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.3724\/SP.J.1004.2009.01586","article-title":"Topic Analysis Based on LDA Model: Topic Analysis Based on LDA Model","volume":"35","author":"Shi","year":"2010","journal-title":"Acta Autom. Sin."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1145\/2133806.2133826","article-title":"Probabilistic topic models","volume":"55","author":"Blei","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.9728\/dcs.2019.20.9.1839","article-title":"Research trend analysis on smart city based on structural topic modeling (STM)","volume":"20","author":"Park","year":"2019","journal-title":"J. Digit. Contents Soc."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Gan, J., and Qi, Y. (2021). Selection of the Optimal Number of Topics for LDA Topic Model-Taking Patent Policy Analysis as an Example. Entropy, 23.","DOI":"10.3390\/e23101301"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s11135-020-00976-w","article-title":"Topic modeling, long texts and the best number of topics. Some Problems and solutions","volume":"54","author":"Sbalchiero","year":"2020","journal-title":"Qual. Quant."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1186\/s40064-016-3252-8","article-title":"An overview of topic modeling and its current applications in bioinformatics","volume":"5","author":"Liu","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5228","DOI":"10.1073\/pnas.0307752101","article-title":"Finding scientific topics","volume":"101","author":"Griffiths","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_66","first-page":"e2","article-title":"Topic modeling: A comprehensive review","volume":"20","author":"Kherwa","year":"2019","journal-title":"EAI Endorsed Trans. Scalable Inf. Syst."},{"key":"ref_67","first-page":"288","article-title":"Reading tea leaves: How humans interpret topic models","volume":"22","author":"Chang","year":"2009","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"101419","DOI":"10.1016\/j.compenvurbsys.2019.101419","article-title":"Tracking urban geo-topics based on dynamic topic model","volume":"79","author":"Yao","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1017\/pan.2016.7","article-title":"Exploring the political agenda of the European Parliament using a dynamic topic modeling approach","volume":"25","author":"Greene","year":"2017","journal-title":"Polit. Anal."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sievert, C., and Shirley, K. (2014, January 27). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-3110"},{"key":"ref_71","first-page":"1506","article-title":"A Comprehensive Review of Feature Selection and Feature Selection Stability in Machine Learning","volume":"36","author":"Okur","year":"2022","journal-title":"Gazi Univ. J. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v091.i02","article-title":"Stm: An R Package for Structural Topic Models","volume":"91","author":"Roberts","year":"2019","journal-title":"J. Stat. Softw."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sun, J., Ye, X., Yan, X., Wang, T., and Chen, J. (2025). Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model. Systems, 13.","DOI":"10.3390\/systems13020096"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/4\/253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:09:42Z","timestamp":1760029782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/4\/253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,3]]},"references-count":73,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["systems13040253"],"URL":"https:\/\/doi.org\/10.3390\/systems13040253","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,3]]}}}