{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:35:19Z","timestamp":1787027719103,"version":"build-2736575974"},"reference-count":110,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Fund of China","award":["19BJY246"],"award-info":[{"award-number":["19BJY246"]}]},{"name":"National Social Science Fund of China","award":["20240101361JC"],"award-info":[{"award-number":["20240101361JC"]}]},{"name":"National Social Science Fund of China","award":["2024LH009"],"award-info":[{"award-number":["2024LH009"]}]},{"name":"Natural Science Fund Project of the Science and Technology Department of Jilin Province","award":["19BJY246"],"award-info":[{"award-number":["19BJY246"]}]},{"name":"Natural Science Fund Project of the Science and Technology Department of Jilin Province","award":["20240101361JC"],"award-info":[{"award-number":["20240101361JC"]}]},{"name":"Natural Science Fund Project of the Science and Technology Department of Jilin Province","award":["2024LH009"],"award-info":[{"award-number":["2024LH009"]}]},{"name":"Think Tank Fund Project of the Jilin Science and Technology Association","award":["19BJY246"],"award-info":[{"award-number":["19BJY246"]}]},{"name":"Think Tank Fund Project of the Jilin Science and Technology Association","award":["20240101361JC"],"award-info":[{"award-number":["20240101361JC"]}]},{"name":"Think Tank Fund Project of the Jilin Science and Technology Association","award":["2024LH009"],"award-info":[{"award-number":["2024LH009"]}]},{"name":"Pioneering Project of Jilin University of Finance and Economics","award":["19BJY246"],"award-info":[{"award-number":["19BJY246"]}]},{"name":"Pioneering Project of Jilin University of Finance and Economics","award":["20240101361JC"],"award-info":[{"award-number":["20240101361JC"]}]},{"name":"Pioneering Project of Jilin University of Finance and Economics","award":["2024LH009"],"award-info":[{"award-number":["2024LH009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method\u2019s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder\u2013Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk.<\/jats:p>","DOI":"10.3390\/biomimetics10010041","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T06:25:54Z","timestamp":1736490354000},"page":"41","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-3978","authenticated-orcid":false,"given":"Guangyu","family":"Mu","sequence":"first","affiliation":[{"name":"School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China"},{"name":"Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0884-8415","authenticated-orcid":false,"given":"Jiaxue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3718-143X","authenticated-orcid":false,"given":"Zhanhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Community Official Staff College of Jilin Province, Changchun 130052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2318-682X","authenticated-orcid":false,"given":"Jiaxiu","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2506-2136","authenticated-orcid":false,"given":"Jiayi","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8508-2185","authenticated-orcid":false,"given":"Xiurong","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mu, G., Li, J., Li, X., Chen, C., Ju, X., and Dai, J. (2024). An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets. Biomimetics, 9.","DOI":"10.3390\/biomimetics9090533"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102145","DOI":"10.1016\/j.ipm.2019.102145","article-title":"Identifying Crisis-Related Informative Tweets Using Learning on Distributions","volume":"57","author":"Ghafarian","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102753","DOI":"10.1016\/j.ijdrr.2021.102753","article-title":"Text Mining Hurricane Harvey Tweet Data: Lessons Learned and Policy Recommendations","volume":"70","author":"Ngamassi","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e1859","DOI":"10.7717\/peerj-cs.1859","article-title":"Categorization of Tweets for Damages: Infrastructure and Human Damage Assessment Using Fine-Tuned BERT Model","volume":"10","author":"Malik","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moussa, A.M., Abdou, S., Elsayed, K.M., Rashwan, M., Asif, A., Khatoon, S., and Alshamari, M.A. (2024). Enhanced Arabic Disaster Data Classification Using Domain Adaptation. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0301255"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., and Imran, M. (2018, January 25\u201328). CrisisMMD: Multimodal Twitter Datasets from Natural Disasters. Proceedings of the International AAAI Conference on Web and Social Media, Palo Alto, CA, USA.","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Olteanu, A., Castillo, C., Diaz, F., and Vieweg, S. (2014, January 1\u20134). CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14538"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cobo, A., Parra, D., and Nav\u00f3n, J. (2015, January 18). Identifying Relevant Messages in a Twitter-Based Citizen Channel for Natural Disaster Situations. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.","DOI":"10.1145\/2740908.2741719"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"165525","DOI":"10.1109\/ACCESS.2024.3493933","article-title":"Multimodal Sentiment Analysis of Government Information Comments Based on Contrastive Learning and Cross-Attention Fusion Networks","volume":"12","author":"Mu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102977","DOI":"10.1016\/j.ipm.2022.102977","article-title":"Identifying Humanitarian Information for Emergency Response by Modeling the Correlation and Independence between Text and Images","volume":"59","author":"Wu","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116626","DOI":"10.1016\/j.eswa.2022.116626","article-title":"Multi-Modality Helps in Crisis Management: An Attention-Based Deep Learning Approach of Leveraging Text for Image Classification","volume":"195","author":"Ahmad","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"46538","DOI":"10.1109\/ACCESS.2022.3170897","article-title":"A Deep Attentive Multimodal Learning Approach for Disaster Identification from Social Media Posts","volume":"10","author":"Hossain","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116562","DOI":"10.1016\/j.eswa.2022.116562","article-title":"Topic2Labels: A Framework to Annotate and Classify the Social Media Data through LDA Topics and Deep Learning Models for Crisis Response","volume":"195","author":"Wahid","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s11042-018-6083-5","article-title":"Feature Selection for Text Classification: A Review","volume":"78","author":"Deng","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.engappai.2016.02.002","article-title":"Deep Feature Weighting for Naive Bayes and Its Application to Text Classification","volume":"52","author":"Jiang","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1177\/0165551516677946","article-title":"Bayesian Na\u00efve Bayes Classifiers to Text Classification","volume":"44","author":"Xu","year":"2016","journal-title":"J. Inf. Sci."},{"key":"ref_17","first-page":"2733","article-title":"A Novel Improved Random Forest for Text Classification Using Feature Ranking and Optimal Number of Trees","volume":"34","author":"Jalal","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102798","DOI":"10.1016\/j.ipm.2021.102798","article-title":"A Comparative Study of Automated Legal Text Classification Using Random Forests and Deep Learning","volume":"59","author":"Chen","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1016\/j.aej.2021.02.009","article-title":"Efficient English Text Classification Using Selected Machine Learning Techniques","volume":"60","author":"Luo","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6149","DOI":"10.1007\/s10462-021-09970-6","article-title":"Feature Selection Methods for Text Classification: A Systematic Literature Review","volume":"54","author":"Pintas","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15091","DOI":"10.1007\/s00521-021-06406-8","article-title":"A Systematic Review of Emerging Feature Selection Optimization Methods for Optimal Text Classification: The Present State and Prospective Opportunities","volume":"33","author":"Abiodun","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101661","DOI":"10.1016\/j.swevo.2024.101661","article-title":"Evolutionary Computation for Feature Selection in Classification: A Comprehensive Survey of Solutions, Applications and Challenges","volume":"90","author":"Song","year":"2024","journal-title":"Swarm Evol. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e961","DOI":"10.7717\/peerj-cs.961","article-title":"Feature Selection by Integrating Document Frequency with Genetic Algorithm for Amharic News Document Classification","volume":"8","author":"Endalie","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54923","DOI":"10.1109\/ACCESS.2021.3071169","article-title":"An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm","volume":"9","author":"Ansari","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2004345","DOI":"10.1080\/08839514.2021.2004345","article-title":"Feature Selection Empowered by Self-Inertia Weight Adaptive Particle Swarm Optimization for Text Classification","volume":"36","author":"Asif","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.asoc.2015.07.005","article-title":"Improved Particle Swarm Optimization Algorithm and Its Application in Text Feature Selection","volume":"35","author":"Lu","year":"2015","journal-title":"Appl. Soft. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1007\/s13042-023-01788-8","article-title":"BSSFS: Binary Sparrow Search Algorithm for Feature Selection","volume":"14","author":"Sun","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111546","DOI":"10.1016\/j.asoc.2024.111546","article-title":"A Binary Sparrow Search Algorithm for Feature Selection on Classification of X-Ray Security Images","volume":"158","author":"Babalik","year":"2024","journal-title":"Appl. Soft. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s10462-024-10723-4","article-title":"Black-Winged Kite Algorithm: A Nature-Inspired Meta-Heuristic for Solving Benchmark Functions and Engineering Problems","volume":"57","author":"Wang","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109836","DOI":"10.1016\/j.jobe.2024.109836","article-title":"Inversion of Image-Only Intrinsic Parameters for Steel Fibre Concrete under Combined Rate-Temperature Conditions: An Adaptively Enhanced Machine Learning Approach","volume":"94","author":"Zhao","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, X., and Yue, Y. (2024). Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application. Biomimetics, 9.","DOI":"10.3390\/biomimetics9100595"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111028","DOI":"10.1016\/j.jobe.2024.111028","article-title":"An Effective PID Control Method of Air Conditioning System for Electric Drive Workshop Based on IBK-IFNN Two-Stage Optimization","volume":"98","author":"Gong","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No Free Lunch Theorems for Optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107865","DOI":"10.1016\/j.engappai.2024.107865","article-title":"A New Filter Feature Selection Algorithm for Classification Task by Ensembling Pearson Correlation Coefficient and Mutual Information","volume":"131","author":"Gong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1007\/s11063-019-10185-8","article-title":"Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient","volume":"51","author":"Liu","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhao, L., Chen, X., Li, Y., and Wang, J. (2023). Identification of Cashmere and Wool Based on LBP and GLCM Texture Feature Selection. J. Eng. Fiber Fabr., 18.","DOI":"10.1177\/15589250221146548"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1007\/s13369-018-3507-5","article-title":"Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers","volume":"44","author":"Thaseen","year":"2018","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A Review of Feature Selection Methods Based on Mutual Information","volume":"24","author":"Vergara","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1007\/s10489-021-02524-x","article-title":"Feature Selection Based on Mutual Information with Correlation Coefficient","volume":"52","author":"Zhou","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for Filter Methods for Feature Selection in High-Dimensional Classification Data","volume":"143","author":"Bommert","year":"2020","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1007\/s11042-023-15675-5","article-title":"Filter Feature Selection Methods for Text Classification: A Review","volume":"83","author":"Ming","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_43","first-page":"817","article-title":"Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges","volume":"74","author":"ElDahshan","year":"2023","journal-title":"CMC-Comput. Mat. Contin."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TKDE.2019.2893266","article-title":"Feature Selection for Neural Networks Using Group Lasso Regularization","volume":"32","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5555","DOI":"10.1109\/TSP.2021.3115943","article-title":"On the Adversarial Robustness of LASSO Based Feature Selection","volume":"69","author":"Li","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119728","DOI":"10.1016\/j.neuroimage.2022.119728","article-title":"Feature-Space Selection with Banded Ridge Regression","volume":"264","author":"Eickenberg","year":"2022","journal-title":"Neuroimage"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1162\/NECO_a_00816","article-title":"Feature Selection for Ridge Regression with Provable Guarantees","volume":"28","author":"Paul","year":"2016","journal-title":"Neural Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"152333","DOI":"10.1109\/ACCESS.2021.3126429","article-title":"RENT\u2014Repeated Elastic Net Technique for Feature Selection","volume":"9","author":"Jenul","year":"2021","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"114072","DOI":"10.1016\/j.eswa.2020.114072","article-title":"A Two-Layer Feature Selection Method Using Genetic Algorithm and Elastic Net","volume":"166","author":"Amini","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"113842","DOI":"10.1016\/j.eswa.2020.113842","article-title":"A Feature Selection Algorithm of Decision Tree Based on Feature Weight","volume":"164","author":"Zhou","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"S1019","DOI":"10.3233\/BME-151397","article-title":"EEG Feature Selection Method Based on Decision Tree","volume":"26","author":"Duan","year":"2015","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P.J., Ma, Q., and Zhang, Y. (2020). Improving Protein-Protein Interactions Prediction Accuracy Using XGBoost Feature Selection and Stacked Ensemble Classifier. Comput. Biol. Med., 123.","DOI":"10.1016\/j.compbiomed.2020.103899"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, C., Shi, H., Jiang, Z., Salhi, A., Chen, R., Cui, X., and Yu, B. (2021). DNN-DTIs: Improved Drug-Target Interactions Prediction Using XGBoost Feature Selection and Deep Neural Network. Comput. Biol. Med., 136.","DOI":"10.1016\/j.compbiomed.2021.104676"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s12539-021-00488-7","article-title":"DeepStack-DTIs: Predicting Drug\u2013Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier","volume":"14","author":"Zhang","year":"2021","journal-title":"Interdiscip. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e12658","DOI":"10.1111\/exsy.12658","article-title":"HFS-LightGBM: A Machine Learning Model Based on Hybrid Feature Selection for Classifying ICU Patient Readmissions","volume":"38","author":"Qiu","year":"2020","journal-title":"Expert Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"19235","DOI":"10.1007\/s00521-023-08763-y","article-title":"Ensemble Feature Selection for Single-Label Text Classification: A Comprehensive Analytical Study","volume":"35","author":"Parlak","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11128-018-1924-8","article-title":"Quantum-Enhanced Feature Selection with Forward Selection and Backward Elimination","volume":"17","author":"He","year":"2018","journal-title":"Quantum Inf. Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1016\/j.patcog.2013.10.009","article-title":"Efficient Feature Size Reduction via Predictive Forward Selection","volume":"47","author":"Reif","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1007\/s12665-021-09998-5","article-title":"Performance Assessment of Artificial Neural Network Using Chi-Square and Backward Elimination Feature Selection Methods for Landslide Susceptibility Analysis","volume":"80","author":"Pham","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compbiomed.2013.11.016","article-title":"Investigating the Performance Improvement of HRV Indices in CHF Using Feature Selection Methods Based on Backward Elimination and Statistical Significance","volume":"45","author":"Narin","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"785","DOI":"10.23919\/cje.2021.00.347","article-title":"Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems","volume":"32","author":"Zhou","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jeon, H., and Oh, S. (2020). Hybrid-Recursive Feature Elimination for Efficient Feature Selection. Appl. Sci., 10.","DOI":"10.3390\/app10093211"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111578","DOI":"10.1016\/j.knosys.2024.111578","article-title":"Search Space Division Method for Wrapper Feature Selection on High-Dimensional Data Classification","volume":"291","author":"Chaudhuri","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"39833","DOI":"10.1109\/ACCESS.2022.3165814","article-title":"Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review","volume":"10","author":"Alyasiri","year":"2022","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"43733","DOI":"10.1109\/ACCESS.2023.3272556","article-title":"Bio-Inspired Feature Selection Algorithms with Their Applications: A Systematic Literature Review","volume":"11","author":"Pham","year":"2023","journal-title":"IEEE Access"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e2084","DOI":"10.7717\/peerj-cs.2084","article-title":"A Systematic Literature Review on Meta-Heuristic Based Feature Selection Techniques for Text Classification","volume":"10","author":"Senan","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Alyasiri, O.M., Cheah, Y.-N., Zhang, H., Al-Janabi, O.M., and Abasi, A.K. (2024). Text Classification Based on Optimization Feature Selection Methods: A Review and Future Directions. Multimed. Tools Appl.","DOI":"10.1007\/s11042-024-19769-6"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s00607-022-01098-x","article-title":"AMAE: Adversarial Multimodal Auto-Encoder for Crisis-Related Tweet Analysis","volume":"105","author":"Lv","year":"2022","journal-title":"Computing"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Mu, G., Li, J., Liao, Z., and Yang, Z. (2024). An Enhanced IHHO-LSTM Model for Predicting Online Public Opinion Trends in Public Health Emergencies. SAGE Open, 14.","DOI":"10.1177\/21582440241257681"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1007\/s11042-020-09873-8","article-title":"A Stacked Convolutional Neural Network for Detecting the Resource Tweets during a Disaster","volume":"80","author":"Madichetty","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"62257","DOI":"10.1007\/s11042-023-16840-6","article-title":"A Semantic Ontology Infused Deep Learning Model for Disaster Tweet Classification","volume":"83","author":"Giri","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"28901","DOI":"10.1007\/s11042-020-09343-1","article-title":"Classifying Informative and Non-Informative Tweets from the Twitter by Adapting Image Features during Disaster","volume":"79","author":"Madichetty","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101824","DOI":"10.1016\/j.compenvurbsys.2022.101824","article-title":"VictimFinder: Harvesting Rescue Requests in Disaster Response from Social Media with BERT","volume":"95","author":"Zhou","year":"2022","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"109398","DOI":"10.1016\/j.compeleceng.2024.109398","article-title":"Knowledge-Injected Prompt Learning for Actionable Information Extraction from Crisis-Related Tweets","volume":"118","author":"Gao","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101757","DOI":"10.1016\/j.ijdrr.2020.101757","article-title":"Machine-Learning Methods for Identifying Social Media-Based Requests for Urgent Help during Hurricanes","volume":"51","author":"Devaraj","year":"2020","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s10479-017-2522-3","article-title":"Event Classification and Location Prediction from Tweets during Disasters","volume":"283","author":"Singh","year":"2017","journal-title":"Ann. Oper. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/TCSS.2019.2937899","article-title":"Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach","volume":"6","author":"Rudra","year":"2019","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"101913","DOI":"10.1016\/j.datak.2021.101913","article-title":"A Novel Domain and Event Adaptive Tweet Augmentation Approach for Enhancing the Classification of Crisis Related Tweets","volume":"135","author":"Ramachandran","year":"2021","journal-title":"Data Knowl. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"103865","DOI":"10.1016\/j.ijdrr.2023.103865","article-title":"Social Response and Disaster Management: Insights from Twitter Data Assimilation on Hurricane Ian","volume":"95","author":"Karimiziarani","year":"2023","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, N., Yaya Hamadou, K., Su, Y., Guo, X., and Song, W. (2024). Multi-Level Emotion Propagation in Natural Disaster Events: Diverse Leadership of Super-Spreaders in Different Levels of Hierarchy. Online Inf. Rev., 49.","DOI":"10.1108\/OIR-03-2024-0192"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Mu, G., Liao, Z., Li, J., Qin, N., and Yang, Z. (2023). IPSO-LSTM Hybrid Model for Predicting Online Public Opinion Trends in Emergencies. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0292677"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s10796-021-10107-x","article-title":"A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters","volume":"23","author":"Mendon","year":"2021","journal-title":"Inf. Syst. Front."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"103695","DOI":"10.1016\/j.ipm.2024.103695","article-title":"Unveiling the Dynamics of Crisis Events: Sentiment and Emotion Analysis via Multi-Task Learning with Attention Mechanism and Subject-Based Intent Prediction","volume":"61","author":"Lo","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1007\/s11069-021-04527-w","article-title":"Content Analyses of the International Federation of Red Cross and Red Crescent Societies (Ifrc) Based on Machine Learning Techniques through Twitter","volume":"106","author":"Dereli","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"106136","DOI":"10.1016\/j.engappai.2023.106136","article-title":"An ALBERT-Based TextCNN-Hatt Hybrid Model Enhanced with Topic Knowledge for Sentiment Analysis of Sudden-Onset Disasters","volume":"123","author":"Zhang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e1417","DOI":"10.7717\/peerj-cs.1417","article-title":"Application of Public Emotion Feature Extraction Algorithm Based on Social Media Communication in Public Opinion Analysis of Natural Disasters","volume":"9","author":"Li","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Minocha, S., and Singh, B. (2023). A Novel Sentimental Analysis for Response to Natural Disaster on Twitter Data. Int. J. Pattern Recognit. Artif. Intell., 37.","DOI":"10.1142\/S0218001423570070"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"100480","DOI":"10.1016\/j.crm.2023.100480","article-title":"Toward Reduction of Detrimental Effects of Hurricanes Using a Social Media Data Analytic Approach: How Climate Change Is Perceived?","volume":"39","author":"Karimiziarani","year":"2023","journal-title":"Clim. Risk Manag."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s11069-023-06033-7","article-title":"Analysis of Public Emotion on Flood Disasters in Southern China in 2020 Based on Social Media Data","volume":"118","author":"Ma","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"121274","DOI":"10.1016\/j.foreco.2023.121274","article-title":"Trends in Bushfire Related Tweets during the Australian \u2018Black Summer\u2019 of 2019\/20","volume":"545","author":"Zander","year":"2023","journal-title":"For. Ecol. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.5194\/nhess-21-1825-2021","article-title":"Review Article: Detection of Actionable Tweets in Crisis Events","volume":"21","author":"Kruspe","year":"2021","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.3390\/ijgi4031549","article-title":"Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery","volume":"4","author":"Huang","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"2348668","DOI":"10.1080\/17538947.2024.2348668","article-title":"Multi-Class Multi-Label Classification of Social Media Texts for Typhoon Damage Assessment: A Two-Stage Model Fully Integrating the Outputs of the Hidden Layers of BERT","volume":"17","author":"Zou","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1111\/1468-5973.12321","article-title":"What Happens Where during Disasters? A Workflow for the Multifaceted Characterization of Crisis Events Based on Twitter Data","volume":"28","author":"Kersten","year":"2020","journal-title":"J. Cont. Crisis Manag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s10796-018-9830-2","article-title":"Weakly Supervised and Online Learning of Word Models for Classification to Detect Disaster Reporting Tweets","volume":"20","author":"Palshikar","year":"2018","journal-title":"Inf. Syst. Front."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1080\/17538947.2018.1563219","article-title":"Identifying Disaster-Related Tweets and Their Semantic, Spatial and Temporal Context Using Deep Learning, Natural Language Processing and Spatial Analysis: A Case Study of Hurricane Irma","volume":"12","author":"Sit","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"108280","DOI":"10.1016\/j.cie.2022.108280","article-title":"Mining Social Media Text for Disaster Resource Management Using a Feature Selection Based on Forest Optimization","volume":"169","author":"Bhoi","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Mujahid, M., Lee, E., Rustam, F., Washington, P.B., Ullah, S., Reshi, A.A., and Ashraf, I. (2021). Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19. Appl. Sci., 11.","DOI":"10.3390\/app11188438"},{"key":"ref_99","first-page":"3189","article-title":"Data Analytics for the Identification of Fake Reviews Using Supervised Learning","volume":"70","author":"Alsubari","year":"2022","journal-title":"CMC-Comput. Mat. Contin."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.3934\/era.2024093","article-title":"IOOA: A Multi-Strategy Fusion Improved Osprey Optimization Algorithm for Global Optimization","volume":"32","author":"Wen","year":"2024","journal-title":"Electron. Res. Arch."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"16673","DOI":"10.1109\/JSEN.2022.3190469","article-title":"Mayfly Sparrow Search Hybrid Algorithm for RFID Network Planning","volume":"22","author":"Zhang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"3204","DOI":"10.1002\/oca.3034","article-title":"A Modified Reptile Search Algorithm for Parametric Estimation of Fractional Order Model of Lithium Battery","volume":"44","author":"Ding","year":"2023","journal-title":"Optim. Control. Appl. Methods"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1007\/s44196-024-00578-6","article-title":"A Multi-Strategy Improved Grasshopper Optimization Algorithm for Solving Global Optimization and Engineering Problems","volume":"17","author":"Liu","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Fan, Z., Xiao, Z., Li, X., Huang, Z., and Zhang, C. (2024). MSBWO: A Multi-Strategies Improved Beluga Whale Optimization Algorithm for Feature Selection. Biomimetics, 9.","DOI":"10.3390\/biomimetics9090572"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"104966","DOI":"10.1016\/j.knosys.2019.104966","article-title":"Dynamic Opposite Learning Enhanced Teaching\u2013Learning-Based Optimization","volume":"188","author":"Xu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.aej.2023.06.048","article-title":"Enhanced Opposition-Based Grey Wolf Optimizer for Global Optimization and Engineering Design Problems","volume":"76","author":"Chandran","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A Simplex Method for Function Minimization","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TVT.2021.3051655","article-title":"Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications","volume":"70","author":"Shrivastava","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_109","unstructured":"Nguyen, D.T., Joty, S., Imran, M., Sajjad, H., and Mitra, P. (2024, September 01). Applications of Online Deep Learning for Crisis Response Using Social Media Information. Available online: https:\/\/arxiv.org\/abs\/1610.01030."},{"key":"ref_110","unstructured":"Ofli, F., Alam, F., and Imran, M. (2024, September 01). Analysis of Social Media Data Using Multimodal Deep Learning for Disaster Response. Available online: https:\/\/arxiv.org\/abs\/2004.11838."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/1\/41\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:26:30Z","timestamp":1759919190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/1\/41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":110,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["biomimetics10010041"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10010041","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]}}}