{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:37:58Z","timestamp":1793482678748,"version":"4.5.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-22972-6","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:47:24Z","timestamp":1762786044000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Machine learning prediction model for medical environment comfort based on SHAP and LIME interpretability analysis"],"prefix":"10.1038","volume":"15","author":[{"given":"Changsheng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"22972_CR1","doi-asserted-by":"publisher","first-page":"37370","DOI":"10.1109\/ACCESS.2024.3422319","volume":"13","author":"S Ahmed","year":"2024","unstructured":"Ahmed, S., Kaiser, M. S., Hossain, M. S. & Andersson, K. A comparative analysis of LIME and SHAP interpreters with explainable ML-based diabetes predictions. IEEE Access 13, 37370\u201337388 (2024).","journal-title":"IEEE Access"},{"key":"22972_CR2","unstructured":"Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019). What clinicians want: Contextualizing explainable machine learning for clinical end use. In Proceeding Machine Learning Healthcare Conference (pp. 359\u2013380)."},{"key":"22972_CR3","unstructured":"Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608."},{"issue":"1","key":"22972_CR4","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s40200-020-00520-5","volume":"19","author":"H Naz","year":"2020","unstructured":"Naz, H. & Ahuja, S. Deep learning approach for diabetes prediction using PIMA Indian dataset. J. Diabetes Metabolic Disorders 19(1), 391\u2013403 (2020).","journal-title":"J. Diabetes Metabolic Disorders"},{"issue":"10","key":"22972_CR5","doi-asserted-by":"publisher","first-page":"869","DOI":"10.17485\/IJST\/v14i10.2187","volume":"14","author":"VC Bavkar","year":"2021","unstructured":"Bavkar, V. C. & Shinde, A. A. Machine learning algorithms for diabetes prediction and neural network method for blood glucose measurement. Indian J. Sci. Technol. 14(10), 869\u2013880 (2021).","journal-title":"Indian J. Sci. Technol."},{"key":"22972_CR6","doi-asserted-by":"crossref","unstructured":"Rout, M., & Kaur, A. (2020). Prediction of diabetes risk based on machine learning techniques. In Proceeding on International Conference on Intelligent Engineering and Management (ICIEM) (pp. 246\u2013251).","DOI":"10.1109\/ICIEM48762.2020.9160276"},{"key":"22972_CR7","doi-asserted-by":"crossref","unstructured":"Van Steenkiste, T., Deschrijver, D., & Dhaene, T. (2019). Interpretable ECG beat embedding using disentangled variational auto-encoders. In Proc. IEEE 32nd Int. Symp. Comput.-Based Med. Syst. (CBMS) (pp. 373\u2013378).","DOI":"10.1109\/CBMS.2019.00081"},{"issue":"7553","key":"22972_CR8","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521(7553), 436\u2013444 (2015).","journal-title":"Nature"},{"key":"22972_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceeding 22nd ACM SIGKDD International Conference Knowledge Discovery Data Mining (pp. 785\u2013794).","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"22972_CR10","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2(3), 18\u201322 (2002).","journal-title":"R News"},{"key":"22972_CR11","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble learning. In Ensemble Machine Learning (pp. 1\u201334). Springer","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"22972_CR12","doi-asserted-by":"crossref","unstructured":"Weisberg, S. (2005). Applied Linear Regression (Vol. 528). Wiley.","DOI":"10.1002\/0471704091"},{"issue":"3","key":"22972_CR13","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian, S. R. & Landgrebe, D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660\u2013674 (1991).","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"22972_CR14","doi-asserted-by":"crossref","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? In Proceeding on 22nd ACM SIGKDD International Conference in Knowledge Discovery Data Mining (pp. 1135\u20131144).","DOI":"10.1145\/2939672.2939778"},{"key":"22972_CR15","unstructured":"Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. In Proceeding on Advances in Neural Information Processing Systems 30, 4768\u20134777 (2017)."},{"issue":"7","key":"22972_CR16","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1111\/1753-0407.12913","volume":"11","author":"A Misra","year":"2019","unstructured":"Misra, A. et al. Diabetes in developing countries. J. Diabetes 11(7), 522\u2013539 (2019).","journal-title":"J. Diabetes"},{"key":"22972_CR17","doi-asserted-by":"crossref","unstructured":"Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., & Nalluri, S. (2017). Genetic algorithm based feature selection and MOE fuzzy classification algorithm on pima Indians diabetes dataset. In Proceedings on International Conference on Computing Network and Informatics (ICCNI) (pp. 1\u20135).","DOI":"10.1109\/ICCNI.2017.8123815"},{"key":"22972_CR18","first-page":"100516","volume":"15","author":"U Orji","year":"2024","unstructured":"Orji, U. & Ukwandu, E. Machine learning for an explainable cost prediction of medical insurance. Mach. Learn. Appl. 15, 100516 (2024).","journal-title":"Mach. Learn. Appl."},{"issue":"5","key":"22972_CR19","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1515\/cclm-2023-1037","volume":"62","author":"HC \u00c7ubuk\u00e7u","year":"2023","unstructured":"\u00c7ubuk\u00e7u, H. C., Topcu, D. \u0130 & Yenice, S. Machine learning-based clinical decision support using laboratory data. Clin. Chem. Lab. Med. (CCLM) 62(5), 793\u2013823 (2023).","journal-title":"Clin. Chem. Lab. Med. (CCLM)"},{"issue":"1","key":"22972_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-023-10192-x","volume":"16","author":"V Viswan","year":"2024","unstructured":"Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K. & Hajamohideen, F. Explainable artificial intelligence in Alzheimer\u2019s disease classification: A systematic review. Cognit. Comput. 16(1), 1\u201344 (2024).","journal-title":"Cognit. Comput."},{"issue":"1","key":"22972_CR21","doi-asserted-by":"publisher","first-page":"6263","DOI":"10.1038\/s41598-023-33525-0","volume":"13","author":"MJ Raihan","year":"2023","unstructured":"Raihan, M. J., Khan, M. A. M., Kee, S. H. & Nahid, A. A. Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP. Sci. Rep. 13(1), 6263 (2023).","journal-title":"Sci. Rep."},{"issue":"1","key":"22972_CR22","doi-asserted-by":"publisher","first-page":"3687","DOI":"10.1038\/s41598-024-54375-4","volume":"14","author":"SK Ghosh","year":"2024","unstructured":"Ghosh, S. K. & Khandoker, A. H. Investigation on explainable machine learning models to predict chronic kidney diseases. Sci. Rep. 14(1), 3687 (2024).","journal-title":"Sci. Rep."},{"issue":"5","key":"22972_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-0940-7","volume":"42","author":"M Maniruzzaman","year":"2018","unstructured":"Maniruzzaman, M. et al. Accurate diabetes risk stratification using machine learning: Role of missing value and outliers. J. Med. Syst. 42(5), 1\u201317 (2018).","journal-title":"J. Med. Syst."},{"issue":"9","key":"22972_CR24","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1093\/nar\/26.9.2230","volume":"26","author":"A Reinhardt","year":"1998","unstructured":"Reinhardt, A. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res. 26(9), 2230\u20132236 (1998).","journal-title":"Nucleic Acids Res."},{"key":"22972_CR25","unstructured":"K\u00e9gl, B. (2013). The return of AdaBoost. MH: Multi-class Hamming trees. arXiv:1312.6086."},{"issue":"11","key":"22972_CR26","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.2337\/diacare.25.11.1999","volume":"25","author":"BP Tabaei","year":"2002","unstructured":"Tabaei, B. P. & Herman, W. H. A multivariate logistic regression equation to screen for diabetes: Development and validation. Diabetes Care 25(11), 1999\u20132003 (2002).","journal-title":"Diabetes Care"},{"issue":"3","key":"22972_CR27","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1016\/j.ijar.2007.12.002","volume":"48","author":"I Jenhani","year":"2008","unstructured":"Jenhani, I., Amor, N. B. & Elouedi, Z. Decision trees as possibilistic classifiers. Int. J. Approx. Reasoning 48(3), 784\u2013807 (2008).","journal-title":"Int. J. Approx. Reasoning"},{"key":"22972_CR28","doi-asserted-by":"publisher","first-page":"76516","DOI":"10.1109\/ACCESS.2020.2989857","volume":"8","author":"MK Hasan","year":"2020","unstructured":"Hasan, M. K., Alam, M. A., Das, D., Hossain, E. & Hasan, M. Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access 8, 76516\u201376531 (2020).","journal-title":"IEEE Access"},{"issue":"3","key":"22972_CR29","first-page":"1","volume":"3","author":"MN Imtiaz","year":"2020","unstructured":"Imtiaz, M. N. & Haque, M. A. Predicting type 2 diabetes using machine learning and feature selection techniques. Adv. Comput. Technol. Appl. 3(3), 1\u20138 (2020).","journal-title":"Adv. Comput. Technol. Appl."},{"key":"22972_CR30","doi-asserted-by":"crossref","unstructured":"Priyadarshini, A., & Aravinth, J. (2021). Correlation based breast cancer detection using machine learning. In Proceeding on International Conference on Recent Trends Electronics, Information, Communication & Technology (RTEICT) (pp. 499\u2013504).","DOI":"10.1109\/RTEICT52294.2021.9573818"},{"key":"22972_CR31","doi-asserted-by":"crossref","unstructured":"Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019). A decision support system for diabetes prediction using machine learning and deep learning techniques. In Proceeding on 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1\u20134).","DOI":"10.1109\/UBMYK48245.2019.8965556"},{"key":"22972_CR32","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.procs.2020.01.047","volume":"165","author":"A Mujumdar","year":"2019","unstructured":"Mujumdar, A. & Vaidehi, V. Diabetes prediction using machine learning algorithms. Proc. Comput. Sci. 165, 292\u2013299 (2019).","journal-title":"Proc. Comput. Sci."},{"key":"22972_CR33","doi-asserted-by":"publisher","first-page":"103737","DOI":"10.1109\/ACCESS.2021.3098691","volume":"9","author":"N Fazakis","year":"2021","unstructured":"Fazakis, N. et al. Machine learning tools for long-term type 2 diabetes risk prediction. IEEE Access 9, 103737\u2013103757 (2021).","journal-title":"IEEE Access"},{"key":"22972_CR34","doi-asserted-by":"crossref","unstructured":"Sarwar, M. A., Kamal, N., Hamid, W., & Shah, M. A. (2018). Prediction of diabetes using machine learning algorithms in healthcare. In Proceeding on 24th International Conference on Automation Computing (ICAC) (pp. 1\u20136).","DOI":"10.23919\/IConAC.2018.8748992"},{"key":"22972_CR35","doi-asserted-by":"crossref","unstructured":"Emon, M. U., Keya, M. S., Kaiser, M. S., Islam, M. A., Tanha, T., & Zulfiker, M. S. (2021). Primary stage of diabetes prediction using machine learning approaches. In Proceeding on International Conference Artifical Intelligence and Smart Systems (ICAIS) (pp. 364\u2013367).","DOI":"10.1109\/ICAIS50930.2021.9395968"},{"key":"22972_CR36","unstructured":"Lin, Z. Q., Shafiee, M. J., Bochkarev, S., Jules, M. S., Wang, X. Y., & Wong, A. (2019). Do explanations reflect decisions? A machine-centric strategy to quantify the performance of explainability algorithms. arXiv:1910.07387."},{"key":"22972_CR37","doi-asserted-by":"crossref","unstructured":"Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020). Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscipl. Rev., Data Mining Knowl. Discovery, 10(5), e1379.","DOI":"10.1002\/widm.1379"},{"key":"22972_CR38","unstructured":"Visani, G., Bagli, E., & Chesani, F. (2020). OptiLIME: Optimized LIME explanations for diagnostic computer algorithms. arXiv:2006.05714."},{"key":"22972_CR39","doi-asserted-by":"publisher","first-page":"113561","DOI":"10.1016\/j.dss.2021.113561","volume":"150","author":"C Moreira","year":"2021","unstructured":"Moreira, C. et al. LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models. Decis. Support Syst. 150, 113561 (2021).","journal-title":"Decis. Support Syst."},{"key":"22972_CR40","doi-asserted-by":"crossref","unstructured":"Duell, J. A. (2021). A comparative approach to explainable artificial intelligence methods in application to high-dimensional electronic health records: Examining the usability of XAI. arXiv:2103.04951.","DOI":"10.1109\/BHI50953.2021.9508618"},{"key":"22972_CR41","unstructured":"Gohel, P., Singh, P., & Mohanty, M. (2021). Explainable AI: Current status and future directions. arXiv:2107.07045."},{"key":"22972_CR42","doi-asserted-by":"crossref","unstructured":"Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In Proceeding on AAAI\/ACM Conference on AI, Ethics, Society (pp. 180\u2013186).","DOI":"10.1145\/3375627.3375830"},{"key":"22972_CR43","doi-asserted-by":"crossref","unstructured":"Duell, J., Fan, X., Burnett, B., Aarts, G., & Zhou, S. M. (2021). A comparison of explanations given by explainable artificial intelligence methods on analysing electronic health records. In Proceeding IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1\u20134).","DOI":"10.1109\/BHI50953.2021.9508618"},{"key":"22972_CR44","doi-asserted-by":"crossref","unstructured":"Ong, J. H., Goh, K. M., & Lim, L. L. (2021). Comparative analysis of explainable artificial intelligence for COVID-19 diagnosis on CXR image. In Proceeding on IEEE International Conference on Signal Image Processing Application (ICSIPA) (pp. 185\u2013190).","DOI":"10.1109\/ICSIPA52582.2021.9576766"},{"issue":"2","key":"22972_CR45","doi-asserted-by":"publisher","first-page":"e033898","DOI":"10.1136\/bmjopen-2019-033898","volume":"10","author":"CA Hu","year":"2020","unstructured":"Hu, C. A. et al. Using a machine learning approach to predict mortality in critically ill influenza patients: A cross-sectional retrospective multicentre study in Taiwan. BMJ Open 10(2), e033898 (2020).","journal-title":"BMJ Open"},{"key":"22972_CR46","doi-asserted-by":"crossref","unstructured":"Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI tool for experimenting explainable AI techniques on medical data analytics. In Proceeding on IEEE 33rd International Conference on Tools, Artificial Intelligence (ICTAI) (pp. 841\u2013845).","DOI":"10.1109\/ICTAI52525.2021.00134"},{"key":"22972_CR47","doi-asserted-by":"crossref","unstructured":"Gandhi, N., & Mishra, S. (2022). Explainable AI for healthcare: A study for interpreting diabetes prediction. In Proceeding on International Conference on Machine Learning and Big Data Analytics (ICMLBDA) (pp. 95\u2013105).","DOI":"10.1007\/978-3-030-82469-3_9"},{"key":"22972_CR48","doi-asserted-by":"crossref","unstructured":"Ye, Q., Xia, J., & Yang, G. (2021). Explainable AI for COVID-19 CT classifiers: An initial comparison study. In Proceeding on IEEE 34th international symposium on computer-based medical systems (CBMS) (pp. 521\u2013526).","DOI":"10.1109\/CBMS52027.2021.00103"},{"key":"22972_CR49","unstructured":"Garreau, D., & Mardaoui, D. (2021). What does lime really see in images? In Proceeding on International Conference of Machine Learning (pp. 3620\u20133629)."},{"issue":"4","key":"22972_CR50","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1109\/TAI.2021.3092698","volume":"4","author":"H Wu","year":"2021","unstructured":"Wu, H. et al. Interpretable machine learning for COVID-19: An empirical study on severity prediction task. IEEE Trans. Artif. Intell. 4(4), 764\u2013777 (2021).","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"2","key":"22972_CR51","doi-asserted-by":"crossref","first-page":"4015","DOI":"10.32604\/cmc.2023.027078","volume":"74","author":"R Alkanhel","year":"2023","unstructured":"Alkanhel, R. et al. Network intrusion detection based on feature selection and hybrid metaheuristic optimization. Comput., Mater. Continua 74(2), 4015\u20134035 (2023).","journal-title":"Comput., Mater. Continua"},{"key":"22972_CR52","doi-asserted-by":"publisher","first-page":"79750","DOI":"10.1109\/ACCESS.2023.3298955","volume":"11","author":"AA Abdelhamid","year":"2023","unstructured":"Abdelhamid, A. A. et al. Innovative feature selection method based on hybrid sine cosine and dipper throated optimization algorithms. IEEE Access 11, 79750\u201379776 (2023).","journal-title":"IEEE Access"},{"issue":"23","key":"22972_CR53","doi-asserted-by":"publisher","first-page":"4421","DOI":"10.3390\/math10234421","volume":"10","author":"ESM El-Kenawy","year":"2022","unstructured":"El-Kenawy, E. S. M. et al. Metaheuristic optimization for improving weed detection in wheat images captured by drones. Mathematics 10(23), 4421 (2022).","journal-title":"Mathematics"},{"issue":"1","key":"22972_CR54","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.32604\/cmc.2023.031723","volume":"75","author":"G Atteia","year":"2023","unstructured":"Atteia, G. et al. Adaptive dynamic dipper throated optimization for feature selection in medical data. Comput., Mater. & Continua 75(1), 1883\u20131900 (2023).","journal-title":"Comput., Mater. & Continua"},{"key":"22972_CR55","doi-asserted-by":"publisher","first-page":"23681","DOI":"10.1109\/ACCESS.2023.3253430","volume":"11","author":"H Myriam","year":"2023","unstructured":"Myriam, H. et al. Advanced meta-heuristic algorithm based on Particle Swarm and Al-biruni Earth Radius optimization methods for oral cancer detection. IEEE Access 11, 23681\u201323700 (2023).","journal-title":"IEEE Access"},{"issue":"1","key":"22972_CR56","doi-asserted-by":"publisher","first-page":"25033","DOI":"10.1038\/s41598-025-09522-w","volume":"15","author":"Z Liu","year":"2025","unstructured":"Liu, Z. et al. A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images. Sci. Rep. 15(1), 25033 (2025).","journal-title":"Sci. Rep."},{"key":"22972_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.129518","author":"J Xu","year":"2025","unstructured":"Xu, J. et al. DFGNet: A dual-pathway graph neural network via frequency decomposition for spatiotemporal forecasting. Expert Syst. Appl. https:\/\/doi.org\/10.1016\/j.eswa.2025.129518 (2025).","journal-title":"Expert Syst. Appl."},{"key":"22972_CR58","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2025.3603330","author":"Z Song","year":"2025","unstructured":"Song, Z. et al. Smart-city spatiotemporal data-driven trajectory prediction for autonomous vehicles via attention mechanisms and self-supervised learning. IEEE Trans. Cons. Electr. https:\/\/doi.org\/10.1109\/TCE.2025.3603330 (2025).","journal-title":"IEEE Trans. Cons. Electr."},{"key":"22972_CR59","doi-asserted-by":"publisher","first-page":"122074","DOI":"10.1016\/j.ins.2025.122074","volume":"710","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y. et al. An off-policy deep reinforcement learning-based active learning for crime scene investigation image classification. Inf. Sci. 710, 122074 (2025).","journal-title":"Inf. Sci."},{"issue":"10","key":"22972_CR60","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s10586-025-05334-9","volume":"28","author":"Z Wang","year":"2025","unstructured":"Wang, Z. et al. An adaptive equilibrium optimizer with information enhancement for global optimization. Clust. Comput. 28(10), 673 (2025).","journal-title":"Clust. Comput."},{"issue":"5","key":"22972_CR61","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1049\/cit2.12316","volume":"9","author":"ZS Wang","year":"2024","unstructured":"Wang, Z. S. et al. Elite-guided equilibrium optimiser based on information enhancement: Algorithm and mobile edge computing applications. CAAI Trans. Intell. Technol. 9(5), 1126\u20131171 (2024).","journal-title":"CAAI Trans. Intell. Technol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22972-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22972-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22972-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T02:04:21Z","timestamp":1763172261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-22972-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["22972"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-22972-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"16 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39269"}}