{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T06:38:43Z","timestamp":1776494323402,"version":"3.51.2"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s42001-026-00475-2","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:40:12Z","timestamp":1776490812000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A double-stacked ensemble classification approach for crime against women in India"],"prefix":"10.1007","volume":"9","author":[{"given":"Poonam K.","family":"Saravag","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8391-098X","authenticated-orcid":false,"given":"B. Rushi","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"Jitendra","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"475_CR1","doi-asserted-by":"publisher","first-page":"109215","DOI":"10.1016\/j.asoc.2022.109215","volume":"125","author":"AK Das","year":"2022","unstructured":"Das, A. K., & Das, P. (2022). Graph based ensemble classification for crime report prediction. Applied Soft Computing, 125, 109215.","journal-title":"Applied Soft Computing"},{"issue":"9","key":"475_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.37398\/JSR.2021.650902","volume":"65","author":"K Anjali","year":"2021","unstructured":"Anjali, K., B, R., & Kumar, J. (2021). Spatio-temporal aspect of suicide and suicidal ideation: An application of satscan to detect hotspots in four major cities of tamil nadu. Journal of Scientific Research, 65(9), 7\u201318.","journal-title":"Journal of Scientific Research"},{"key":"475_CR3","doi-asserted-by":"crossref","unstructured":"Saravag, P. K., & Kumar, B. R. (2024). An application of scan statistics in identification and analysis of hotspot of crime against women in rajasthan, india. Applied Spatial Analysis and Policy, 1\u201320.","DOI":"10.1007\/s12061-024-09572-z"},{"key":"475_CR4","doi-asserted-by":"crossref","unstructured":"Sukhija, K., Singh, S.N., & Kumar, J. (2017). Spatial visualization approach for detecting criminal hotspots: An analysis of total cognizable crimes in the state of haryana. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), (pp. 1060\u20131066). IEEE","DOI":"10.1109\/RTEICT.2017.8256761"},{"key":"475_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jadr.2024.100741","volume":"16","author":"K Anjali","year":"2024","unstructured":"Anjali, K., & B, R. (2024). Spatial analysis of multivariate factors influencing suicide hotspots in urban tamil nadu. Journal of Affective Disorders Reports, 16, Article 100741.","journal-title":"Journal of Affective Disorders Reports"},{"key":"475_CR6","doi-asserted-by":"publisher","first-page":"100339","DOI":"10.1016\/j.array.2024.100339","volume":"21","author":"A Mahmud","year":"2024","unstructured":"Mahmud, A., Sarower, A. H., Sohel, A., Assaduzzaman, M., & Bhuiyan, T. (2024). Adoption of chatgpt by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach. Array, 21, 100339.","journal-title":"Array"},{"key":"475_CR7","first-page":"318","volume":"1","author":"V Mi\u0161kovic","year":"2014","unstructured":"Mi\u0161kovic, V. (2014). Machine learning of hybrid classification models for decision support. Sinteza 2014-Impact of the Internet on Business Activities in Serbia and Worldwide, 1, 318\u2013323.","journal-title":"Sinteza 2014-Impact of the Internet on Business Activities in Serbia and Worldwide"},{"issue":"3","key":"475_CR8","doi-asserted-by":"publisher","first-page":"4219","DOI":"10.17485\/ijst\/2013\/v6i3.6","volume":"6","author":"R Iqbal","year":"2013","unstructured":"Iqbal, R., Murad, M. A. A., Mustapha, A., Panahy, P. H. S., & Khanahmadliravi, N. (2013). An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology, 6(3), 4219\u20134225.","journal-title":"Indian Journal of Science and Technology"},{"key":"475_CR9","unstructured":"Women Peace and Security Index (2021\/22), Report, Georgetown Institute for Women, Peace and Security and Peace Research Institute Oslo, Washington DC"},{"issue":"2","key":"475_CR10","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s10940-020-09457-7","volume":"37","author":"AP Wheeler","year":"2021","unstructured":"Wheeler, A. P., & Steenbeek, W. (2021). Mapping the risk terrain for crime using machine learning. Journal of Quantitative Criminology, 37(2), 445\u2013480.","journal-title":"Journal of Quantitative Criminology"},{"key":"475_CR11","doi-asserted-by":"publisher","first-page":"100195","DOI":"10.1016\/j.array.2022.100195","volume":"15","author":"R Prykhodchenko","year":"2022","unstructured":"Prykhodchenko, R., & Skruch, P. (2022). Road scene classification based on street-level images and spatial data. Array, 15, 100195.","journal-title":"Array"},{"issue":"1","key":"475_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40064-016-2941-7","volume":"5","author":"L-Y Hu","year":"2016","unstructured":"Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 5(1), 1\u20139.","journal-title":"SpringerPlus"},{"key":"475_CR13","doi-asserted-by":"publisher","first-page":"829519","DOI":"10.3389\/fpubh.2022.829519","volume":"10","author":"MO Edeh","year":"2022","unstructured":"Edeh, M. O., Khalaf, O. I., Tavera, C. A., Tayeb, S., Ghouali, S., Abdulsahib, G. M., Richard-Nnabu, N. E., & Louni, A. (2022). A classification algorithm-based hybrid diabetes prediction model. Frontiers in Public Health, 10, 829519.","journal-title":"Frontiers in Public Health"},{"key":"475_CR14","doi-asserted-by":"crossref","unstructured":"Ali, Z. H., & Burhan, A. M. (2023). Hybrid machine learning approach for construction cost estimation: An evaluation of extreme gradient boosting model. Asian Journal of Civil Engineering, 1\u201316.","DOI":"10.1007\/s42107-023-00651-z"},{"issue":"1","key":"475_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1177\/1461355719864367","volume":"22","author":"Y Lee","year":"2020","unstructured":"Lee, Y., & O, S. (2020). Flag and boost theories for hot spot forecasting: An application of nij\u2019s real-time crime forecasting algorithm using colorado springs crime data. International Journal of Police Science & Management, 22(1), 4\u201315.","journal-title":"International Journal of Police Science & Management"},{"issue":"2","key":"475_CR16","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1177\/1098611119887809","volume":"23","author":"Y Lee","year":"2020","unstructured":"Lee, Y., SooHyun, O., & Eck, J. E. (2020). A theory-driven algorithm for real-time crime hot spot forecasting. Police Quarterly, 23(2), 174\u2013201.","journal-title":"Police Quarterly"},{"key":"475_CR17","doi-asserted-by":"crossref","unstructured":"Zaidi, N.A.S., Mustapha, A., Mostafa, S.A., & Razali, M.N. (2019). A classification approach for crime prediction. International conference on applied computing to support industry: innovation and technology, (pp. 68\u201378). Springer","DOI":"10.1007\/978-3-030-38752-5_6"},{"key":"475_CR18","doi-asserted-by":"crossref","unstructured":"Das, P.,& Das, A.K. (2019). Application of classification techniques for prediction and analysis of crime in india. Computational intelligence in data mining: Proceedings of the international conference on CIDM 2017, (pp. 191\u2013201). Springer","DOI":"10.1007\/978-981-10-8055-5_18"},{"issue":"5","key":"475_CR19","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1080\/08839514.2019.1582861","volume":"33","author":"P Sornsuwit","year":"2019","unstructured":"Sornsuwit, P., & Jaiyen, S. (2019). A new hybrid machine learning for cybersecurity threat detection based on adaptive boosting. Applied Artificial Intelligence, 33(5), 462\u2013482.","journal-title":"Applied Artificial Intelligence"},{"key":"475_CR20","doi-asserted-by":"publisher","first-page":"166553","DOI":"10.1109\/ACCESS.2020.3022808","volume":"8","author":"UM Butt","year":"2020","unstructured":"Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553\u2013166574.","journal-title":"IEEE Access"},{"issue":"9","key":"475_CR21","doi-asserted-by":"publisher","first-page":"0274172","DOI":"10.1371\/journal.pone.0274172","volume":"17","author":"UM Butt","year":"2022","unstructured":"Butt, U. M., Letchmunan, S., Hassan, F. H., & Koh, T. W. (2022). Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy. Plos One, 17(9), 0274172.","journal-title":"Plos One"},{"key":"475_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/4830411","volume":"2022","author":"M Khan","year":"2022","unstructured":"Khan, M., Ali, A., & Alharbi, Y. (2022). Predicting and preventing crime: A crime prediction model using san francisco crime data by classification techniques. Complexity, 2022, 1\u201313.","journal-title":"Complexity"},{"key":"475_CR23","doi-asserted-by":"publisher","first-page":"598","DOI":"10.11591\/ijeecs.v29.i1.pp598-608","volume":"29","author":"NHA Malek","year":"2023","unstructured":"Malek, N. H. A., Yaacob, W. F. W., Wah, Y. B., Nasir, S. A. M., Shaadan, N., & Indratno, S. W. (2023). Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data. Indonesian Journal of Electrical Engineering and Computer Science, 29, 598\u2013608.","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"issue":"6","key":"475_CR24","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.18280\/ijsse.140603","volume":"14","author":"AJ Adeyiga","year":"2024","unstructured":"Adeyiga, A. J., Adedotun, A. F., Adebisi, O. E., & Agboola, O. O. (2024). Comparative analysis of svm classifiers in criminal profiling using a hybridized algorithm. International Journal of Safety & Security Engineering, 14(6), 1679\u20131687.","journal-title":"International Journal of Safety & Security Engineering"},{"key":"475_CR25","doi-asserted-by":"crossref","unstructured":"Thongsatapornwatana, U. (2016). A survey of data mining techniques for analyzing crime patterns. 2016 Second asian conference on defence technology (ACDT), (pp. 123\u2013128). IEEE","DOI":"10.1109\/ACDT.2016.7437655"},{"key":"475_CR26","doi-asserted-by":"crossref","unstructured":"Rawat, K.S., & Malhan, I. (2019). A hybrid classification method based on machine learning classifiers to predict performance in educational data mining. In: Proceedings of 2nd international conference on communication, computing and networking: ICCCN 2018, NITTTR Chandigarh, India, (pp. 677\u2013684). Springer","DOI":"10.1007\/978-981-13-1217-5_67"},{"key":"475_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2023.103871","volume":"92","author":"K Anjali","year":"2023","unstructured":"Anjali, K., & B, R. (2023). Exploring cause-specific strategies for suicide prevention in india: A multivariate varma approach. Asian Journal of Psychiatry, 92, Article 103871.","journal-title":"Asian Journal of Psychiatry"},{"issue":"1","key":"475_CR28","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1140\/epjds\/s13688-022-00327-9","volume":"11","author":"G Zhang","year":"2022","unstructured":"Zhang, G., Merrill, M. A., Liu, Y., Heer, J., & Althoff, T. (2022). Coral: Code representation learning with weakly-supervised transformers for analyzing data analysis. EPJ Data Science, 11(1), 14.","journal-title":"EPJ Data Science"},{"key":"475_CR29","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018054314350","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123\u2013140.","journal-title":"Machine Learning"},{"key":"475_CR30","doi-asserted-by":"crossref","unstructured":"Saravag, P. K., & Kumar, B. R. (2024). A hybrid machine learning and regression approach for validating a multi-dimensional crime index in the context of crime against women. IEEE Access.","DOI":"10.1109\/ACCESS.2024.3439721"},{"issue":"2","key":"475_CR31","first-page":"3","volume":"1","author":"H Zhang","year":"2004","unstructured":"Zhang, H. (2004). The optimality of naive bayes. Aa, 1(2), 3.","journal-title":"Aa"},{"issue":"1","key":"475_CR32","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1140\/epjds\/s13688-020-00253-8","volume":"9","author":"F Pierri","year":"2020","unstructured":"Pierri, F., Piccardi, C., & Ceri, S. (2020). A multi-layer approach to disinformation detection in us and italian news spreading on twitter. EPJ Data Science, 9(1), 35.","journal-title":"EPJ Data Science"},{"issue":"18","key":"475_CR33","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","volume":"117","author":"MP LaValley","year":"2008","unstructured":"LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395\u20132399.","journal-title":"Circulation"},{"key":"475_CR34","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.measurement.2019.06.039","volume":"146","author":"MA Costa","year":"2019","unstructured":"Costa, M. A., Wullt, B., Norrl\u00f6f, M., & Gunnarsson, S. (2019). Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting. Measurement, 146, 425\u2013436.","journal-title":"Measurement"},{"issue":"1","key":"475_CR35","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3847\/1538-4357\/aaa23c","volume":"853","author":"F Benvenuto","year":"2018","unstructured":"Benvenuto, F., Piana, M., Campi, C., & Massone, A. M. (2018). A hybrid supervised\/unsupervised machine learning approach to solar flare prediction. The Astrophysical Journal, 853(1), 90.","journal-title":"The Astrophysical Journal"},{"key":"475_CR36","first-page":"139","volume":"2017","author":"GN Obuandike","year":"2017","unstructured":"Obuandike, G. N., Alhassan, J. K., & Abdullahi, M. B. (2017). Classification of crime data for crime control using c4. 5 and na\u00efve bayes techniques. International Journal of Mathematical Analysis and Optimization: Theory and Applications, 2017, 139\u2013153.","journal-title":"International Journal of Mathematical Analysis and Optimization: Theory and Applications"},{"key":"475_CR37","doi-asserted-by":"crossref","unstructured":"Sundhara\u00a0Kumar, K., & Bhalaji, N. (2016). A study on classification algorithms for crime records. In: Smart trends in information technology and computer communications: First international conference, SmartCom 2016, Jaipur, India, August 6\u20137, 2016, Revised Selected Papers 1, (pp. 873\u2013880). Springer","DOI":"10.1007\/978-981-10-3433-6_104"},{"key":"475_CR38","doi-asserted-by":"publisher","first-page":"107778","DOI":"10.1016\/j.compeleceng.2022.107778","volume":"99","author":"C Chakraborty","year":"2022","unstructured":"Chakraborty, C., Kishor, A., & Rodrigues, J. J. (2022). Novel enhanced-grey wolf optimization hybrid machine learning technique for biomedical data computation. Computers and Electrical Engineering, 99, 107778.","journal-title":"Computers and Electrical Engineering"},{"key":"475_CR39","doi-asserted-by":"publisher","first-page":"110986","DOI":"10.1016\/j.asoc.2023.110986","volume":"150","author":"J Guo","year":"2024","unstructured":"Guo, J., Wu, H., Chen, X., & Lin, W. (2024). Adaptive sv-borderline smote-svm algorithm for imbalanced data classification. Applied Soft Computing, 150, 110986.","journal-title":"Applied Soft Computing"},{"key":"475_CR40","doi-asserted-by":"publisher","first-page":"104856","DOI":"10.1016\/j.ijrmms.2021.104856","volume":"145","author":"J Zhou","year":"2021","unstructured":"Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S., & Zhang, X. (2021). Developing a hybrid model of jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 145, 104856.","journal-title":"International Journal of Rock Mechanics and Mining Sciences"},{"key":"475_CR41","doi-asserted-by":"publisher","first-page":"109327","DOI":"10.1016\/j.petrol.2021.109327","volume":"208","author":"MR Delavar","year":"2022","unstructured":"Delavar, M. R. (2022). Hybrid machine learning approaches for classification and detection of fractures in carbonate reservoir. Journal of Petroleum Science and Engineering, 208, 109327.","journal-title":"Journal of Petroleum Science and Engineering"},{"issue":"5","key":"475_CR42","first-page":"321","volume":"12","author":"C-C Sun","year":"2014","unstructured":"Sun, C.-C., Yao, C., Li, X., & Lee, K. (2014). Detecting crime types using classification algorithms. Journal of Digital Information Management, 12(5), 321\u2013327.","journal-title":"Journal of Digital Information Management"},{"key":"475_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9996737","volume":"2021","author":"TS Qaid","year":"2021","unstructured":"Qaid, T. S., Mazaar, H., Al-Shamri, M. Y. H., Alqahtani, M. S., Raweh, A. A., & Alakwaa, W. (2021). Hybrid deep-learning and machine-learning models for predicting covid-19. Computational Intelligence and Neuroscience, 2021, 1\u201311.","journal-title":"Computational Intelligence and Neuroscience"},{"key":"475_CR44","doi-asserted-by":"crossref","unstructured":"Babakura, A., Sulaiman, M.N., & Yusuf, M.A. (2014). Improved method of classification algorithms for crime prediction. 2014 international symposium on biometrics and security technologies (ISBAST), (pp. 250\u2013255). IEEE","DOI":"10.1109\/ISBAST.2014.7013130"},{"issue":"12","key":"475_CR45","first-page":"44","volume":"4","author":"GN Obuandike","year":"2015","unstructured":"Obuandike, G. N., Audu, I., & John, A. (2015). Analytical study of some selected classification algorithms in weka using real crime data. International Journal of Advanced Research in Artificial Intelligence, 4(12), 44\u201348.","journal-title":"International Journal of Advanced Research in Artificial Intelligence"},{"key":"475_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00254-x","volume":"14","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Chu, Y., Thaljaoui, A., Khan, Y. A., Chammam, W., & Abbas, S. Z. (2021). A multi-feature hybrid classification data mining technique for human-emotion. BioData Mining, 14, 1\u201320.","journal-title":"BioData Mining"},{"key":"475_CR47","doi-asserted-by":"crossref","unstructured":"Yu, C.-H., Ward, M.W., Morabito, M., & Ding, W. (2011). Crime forecasting using data mining techniques. In: 2011 IEEE 11th international conference on data mining workshops, (pp. 779\u2013786). IEEE","DOI":"10.1109\/ICDMW.2011.56"},{"issue":"15","key":"475_CR48","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1080\/08839514.2021.1982475","volume":"35","author":"C-H Weng","year":"2021","unstructured":"Weng, C.-H., & Huang, C.-K. (2021). A hybrid machine learning model for credit approval. Applied Artificial Intelligence, 35(15), 1439\u20131465.","journal-title":"Applied Artificial Intelligence"}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-026-00475-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42001-026-00475-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-026-00475-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:40:18Z","timestamp":1776490818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42001-026-00475-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["475"],"URL":"https:\/\/doi.org\/10.1007\/s42001-026-00475-2","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"11 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2026","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 have stated that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"All authors have given their consent for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"42"}}