{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:10:43Z","timestamp":1760609443107,"version":"3.40.3"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031024610"},{"type":"electronic","value":"9783031024627"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-02462-7_6","type":"book-chapter","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T23:02:49Z","timestamp":1649977369000},"page":"77-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python"],"prefix":"10.1007","author":[{"given":"Anh T.","family":"Dang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raneem","family":"Qaddoura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ala\u2019 M.","family":"Al-Zoubi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossam","family":"Faris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro A.","family":"Castillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M.: Tensorflow: learning functions at scale. In: Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, p. 1 (2016)","DOI":"10.1145\/2951913.2976746"},{"key":"6_CR2","unstructured":"Aljarah, I., et al.: Intelligent detection of hate speech in Arabic social network: a machine learning approach. J. Inf. Sci., 0165551520917651 (2020)"},{"key":"6_CR3","unstructured":"Bradski, G.: The opencv library. Dr. Dobb\u2019s J. Softw. Tools Prof. Programm. 25(11), 120\u2013123 (2000)"},{"issue":"3","key":"6_CR4","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1023\/B:HEUR.0000026900.92269.ec","volume":"10","author":"S Cahon","year":"2004","unstructured":"Cahon, S., Melab, N., Talbi, E.-G.: Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10(3), 357\u2013380 (2004)","journal-title":"J. Heuristics"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","volume":"408","author":"J Cervantes","year":"2020","unstructured":"Cervantes, J., Garcia-Lamont, F., Rodr\u00edguez-Mazahua, L., Lopez, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189\u2013215 (2020)","journal-title":"Neurocomputing"},{"key":"6_CR6","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1\u20134 (2015)"},{"issue":"3","key":"6_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2480741.2480752","volume":"45","author":"M \u010crepin\u0161ek","year":"2013","unstructured":"\u010crepin\u0161ek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 1\u201333 (2013)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Faris, H., Aljarah, I., Mirjalili, S., Castillo, P.A., Guerv\u00f3s, J.J.M.: Evolopy: an open-source nature-inspired optimization framework in python. In: IJCCI (ECTA), pp. 171\u2013177 (2016)","DOI":"10.5220\/0006048201710177"},{"key":"6_CR9","unstructured":"Fortin, F.-A., De Rainville, F.-M., Gardner Gardner, M.-A., Parizeau, M., Gagn\u00e9, C.: Deap: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(1), 2171\u20132175 (2012)"},{"issue":"7","key":"6_CR10","first-page":"98","volume":"1","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Machine learning basics. Deep Learn. 1(7), 98\u2013164 (2016)","journal-title":"Deep Learn."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Expl. Newsletter, 11(1), 10\u201318 (2009)","DOI":"10.1145\/1656274.1656278"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/3-540-46033-0_19","volume-title":"Artificial Evolution","author":"M Keijzer","year":"2002","unstructured":"Keijzer, M., Merelo, J.J., Romero, G., Schoenauer, M.: Evolving objects: a general purpose evolutionary computation library. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 231\u2013242. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-46033-0_19"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Ketkar, N.: Introduction to keras. In: Deep learning with Python, pp. 97\u2013111. Springer (2017)","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"6_CR14","series-title":"Algorithms for Intelligent Systems","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-981-32-9990-0_8","volume-title":"Evolutionary Machine Learning Techniques","author":"RA Khurma","year":"2020","unstructured":"Khurma, R.A., Aljarah, I., Sharieh, A., Mirjalili, S.: EvoloPy-FS: an open-source nature-inspired optimization framework in python for feature selection. In: Mirjalili, S., Faris, H., Aljarah, I. (eds.) Evolutionary Machine Learning Techniques. AIS, pp. 131\u2013173. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-32-9990-0_8"},{"key":"6_CR15","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755\u20131758 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"6_CR16","first-page":"3","volume":"160","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis, S.B., Zaharakis, I., Pintelas, P., et al.: Supervised machine learning: a review of classification techniques. Emerging Artif. Intell. Appl. Comput. Eng. 160(1), 3\u201324 (2007)","journal-title":"Emerging Artif. Intell. Appl. Comput. Eng."},{"key":"6_CR17","doi-asserted-by":"publisher","unstructured":"Porcu, V.: Scikit-learn. In: Python for Data Mining Quick Syntax Reference, pp. 235\u2013253. Apress, Berkeley, CA (2018). https:\/\/doi.org\/10.1007\/978-1-4842-4113-4_11","DOI":"10.1007\/978-1-4842-4113-4_11"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, S., Wang, Y., Lombardi, F., Han, J.: A survey of stochastic computing neural networks for machine learning applications. IEEE Trans. Neural Networks Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.3009047"},{"issue":"3","key":"6_CR19","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1093\/bioinformatics\/btm605","volume":"24","author":"P Magni","year":"2008","unstructured":"Magni, P., Ferrazzi, F., Sacchi, L., Bellazzi, R.: Timeclust: a clustering tool for gene expression time series. Bioinformatics 24(3), 430\u2013432 (2008)","journal-title":"Bioinformatics"},{"key":"6_CR20","doi-asserted-by":"crossref","first-page":"381","DOI":"10.21275\/ART20203995","volume":"9","author":"B Mahesh","year":"2020","unstructured":"Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR) 9, 381\u2013386 (2020)","journal-title":"Int. J. Sci. Res. (IJSR)"},{"issue":"9","key":"6_CR21","first-page":"1","volume":"14","author":"W McKinney","year":"2011","unstructured":"McKinney, W., et al.: pandas: a foundational python library for data analysis and statistics. Python High Performance Sci. Comput. 14(9), 1\u20139 (2011)","journal-title":"Python High Performance Sci. Comput."},{"issue":"1","key":"6_CR22","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng, X., et al.: Mllib: machine learning in apache spark. J. Mach. Learn. Res. 17(1), 1235\u20131241 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR23","unstructured":"Mhembere, D., Zheng, D., Priebe, C.E., Vogelstein, J.T., Burns, R.: Clusternor: a numa-optimized clustering framework. arXiv preprint arXiv:1902.09527 (2019)"},{"key":"6_CR24","unstructured":"NN Open. An open source neural networks c++ library. http:\/\/opennn.cimne.com\/: 04(10), pp. 2008 (2016)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Palop, J.J., Mucke, L., Roberson, E.D.: Quantifying biomarkers of cognitive dysfunction and neuronal network hyperexcitability in mouse models of alzheimer\u2019s disease: depletion of calcium-dependent proteins and inhibitory hippocampal remodeling. In: Alzheimer\u2019s Disease and Frontotemporal Dementia, pp. 245\u2013262. Springer (2010)","DOI":"10.1007\/978-1-60761-744-0_17"},{"key":"6_CR26","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR27","unstructured":"Phyu, T.N.: Survey of classification techniques in data mining. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1 (2009)"},{"key":"6_CR28","unstructured":"Pohlheim, H.: Geatbx\u00ae-the genetic and evolutionary algorithm toolbox for matlab\u00ae (2007). http:\/\/www.geatbx.com\/. Accessed 24 June 2012"},{"issue":"7","key":"6_CR29","doi-asserted-by":"publisher","first-page":"3022","DOI":"10.3390\/app11073022","volume":"11","author":"R Qaddoura","year":"2021","unstructured":"Qaddoura, R., Al-Zoubi, A.M., Almomani, I., Faris, H.: A multi-stage classification approach for iot intrusion detection based on clustering with oversampling. Appl. Sci. 11(7), 3022 (2021)","journal-title":"Appl. Sci."},{"issue":"9","key":"6_CR30","doi-asserted-by":"publisher","first-page":"2987","DOI":"10.3390\/s21092987","volume":"21","author":"R Qaddoura","year":"2021","unstructured":"Qaddoura, R., Al-Zoubi, M., Faris, H., Almomani, I., et al.: A multi-layer classification approach for intrusion detection in iot networks based on deep learning. Sensors 21(9), 2987 (2021)","journal-title":"Sensors"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., Aljarah, I., Faris, H., Almomani, I.: A classification approach based on evolutionary clustering and its application for ransomware detection. In: Evolutionary Data Clustering: Algorithms and Applications, p. 237 (2021)","DOI":"10.1007\/978-981-33-4191-3_11"},{"issue":"3","key":"6_CR32","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s13042-019-01027-z","volume":"11","author":"R Qaddoura","year":"2020","unstructured":"Qaddoura, R., Faris, H., Aljarah, I.: An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. Int. J. Mach. Learn. Cybern. 11(3), 675\u2013714 (2020)","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"8","key":"6_CR33","doi-asserted-by":"publisher","first-page":"8387","DOI":"10.1007\/s12652-020-02570-2","volume":"12","author":"R Qaddoura","year":"2020","unstructured":"Qaddoura, R., Faris, H., Aljarah, I.: An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis. J. Ambient. Intell. Humaniz. Comput. 12(8), 8387\u20138412 (2020). https:\/\/doi.org\/10.1007\/s12652-020-02570-2","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"6_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-43722-0_2","volume-title":"Applications of Evolutionary Computation","author":"R Qaddoura","year":"2020","unstructured":"Qaddoura, R., Faris, H., Aljarah, I., Castillo, P.A.: EvoCluster: an open-source nature-inspired optimization clustering framework in python. In: Castillo, P.A., Jim\u00e9nez Laredo, J.L., Fern\u00e1ndez de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 20\u201336. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43722-0_2"},{"issue":"3","key":"6_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00511-0","volume":"2","author":"R Qaddoura","year":"2021","unstructured":"Qaddoura, R., Faris, H., Aljarah, I., Castillo, P.A.: Evocluster: an open-source nature-inspired optimization clustering framework. SN Comput. Sci. 2(3), 1\u201312 (2021)","journal-title":"SN Comput. Sci."},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Qaddoura, R., Faris, H., Aljarah, I., Guerv\u00f3s, J.J.M., Castillo, P.A.: Empirical evaluation of distance measures for nearest point with indexing ratio clustering algorithm. In: IJCCI, pp. 430\u2013438 (2020)","DOI":"10.5220\/0010121504300438"},{"key":"6_CR37","unstructured":"Rehurek, R., Sojka, P., et al.: Gensim-statistical semantics in python. Retrieved from genism. org (2011)"},{"issue":"9","key":"6_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006378","volume":"14","author":"D Risso","year":"2018","unstructured":"Risso, D., et al.: Clusterexperiment and rsec: a bioconductor package and framework for clustering of single-cell and other large gene expression datasets. PLoS Comput. Biology 14(9), e1006378 (2018)","journal-title":"PLoS Comput. Biology"},{"key":"6_CR39","first-page":"1799","volume":"11","author":"S Sonnenburg","year":"2010","unstructured":"Sonnenburg, S., et al.: The shogun machine learning toolbox. J. Mach. Learn. Res. 11, 1799\u20131802 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR40","doi-asserted-by":"crossref","unstructured":"Virtanen, P., et al.: SciPy 1. 0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261\u2013272 (2020)","DOI":"10.1038\/s41592-019-0686-2"},{"issue":"23","key":"6_CR41","doi-asserted-by":"publisher","first-page":"613","DOI":"10.21105\/joss.00613","volume":"3","author":"G Vrban\u010di\u010d","year":"2018","unstructured":"Vrban\u010di\u010d, G., Brezo\u010dnik, L., Mlakar, U., Fister, D., Fister, I.: Niapy: python microframework for building nature-inspired algorithms. J. Open Source Softw. 3(23), 613 (2018)","journal-title":"J. Open Source Softw."},{"key":"6_CR42","doi-asserted-by":"publisher","unstructured":"Wagner, S., et al.: Architecture and design of the heuristiclab optimization environment. In: Advanced Methods and Applications in Computational Intelligence, pp. 197\u2013261. Springer (2014). https:\/\/doi.org\/10.1007\/978-3-319-01436-4_10","DOI":"10.1007\/978-3-319-01436-4_10"},{"key":"6_CR43","unstructured":"Wall, M.: Galib: A c++ library of genetic algorithm components. Mech. Eng. Department, Massachusetts Institute of Technology 87, 54 (1996)"},{"issue":"11","key":"6_CR44","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/nmeth.3583","volume":"12","author":"C Wiwie","year":"2015","unstructured":"Wiwie, C., Baumbach, J., R\u00f6ttger, R.: Comparing the performance of biomedical clustering methods. Nat. Methods 12(11), 1033\u20131038 (2015)","journal-title":"Nat. Methods"},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"Yu, X., Gen, M.: Introduction to evolutionary algorithms. Springer Science & Business Media (2010)","DOI":"10.1007\/978-1-84996-129-5"},{"key":"6_CR46","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1613\/jair.1.11854","volume":"70","author":"M-A Z\u00f6ller","year":"2021","unstructured":"Z\u00f6ller, M.-A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. J. Artif. Intell. Res. 70, 409\u2013472 (2021)","journal-title":"J. Artif. Intell. Res."}],"container-title":["Lecture Notes in Computer Science","Applications of Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-02462-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T08:02:39Z","timestamp":1726992159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-02462-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031024610","9783031024627"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-02462-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoApplications","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Applications of Evolutionary Computation (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evoapplications2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.56","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}