{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:53:17Z","timestamp":1782935597241,"version":"3.54.5"},"reference-count":116,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10489-022-04427-x","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"page":"18715-18757","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Majdi","family":"Mafarja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thaer","family":"Thaher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Too","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed A.","family":"Awadallah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iyad","family":"Abu Doush","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamza","family":"Turabieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"issue":"05","key":"4427_CR1","first-page":"886","volume":"7","author":"N Honest","year":"2019","unstructured":"Honest N (2019) Role of testing in software development life cycle. Int J Comput Sci Eng 7 (05):886\u2013889","journal-title":"Int J Comput Sci Eng"},{"key":"4427_CR2","first-page":"12","volume":"122","author":"H Turabieh","year":"2018","unstructured":"Turabieh H, Mafarja M, Li X (2018) Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert Syst Appl 122:12","journal-title":"Expert Syst Appl"},{"issue":"01","key":"4427_CR3","first-page":"1","volume":"pp","author":"I Tumar","year":"2020","unstructured":"Tumar I, Hassouneh Y, Turabieh H, Thaher T (2020) Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction. IEEE Access pp(01):1\u20131","journal-title":"IEEE Access"},{"key":"4427_CR4","first-page":"1","volume":"05","author":"S Rathore","year":"2017","unstructured":"Rathore S, Kumar S (2017) A study on software fault prediction techniques. Artif Intell Rev 05:1\u201373","journal-title":"Artif Intell Rev"},{"issue":"8","key":"4427_CR5","first-page":"28","volume":"9","author":"M Fowler","year":"2001","unstructured":"Fowler M, Highsmith J et al (2001) The agile manifesto. Softw Dev 9(8):28\u201335","journal-title":"Softw Dev"},{"key":"4427_CR6","unstructured":"Royce WW (1987) Managing the development of large software systems: concepts and techniques, pp 1\u20139, August 1970. In: Reprinted in proceedings of the ninth international conference on software engineering, pp 328\u2013338"},{"key":"4427_CR7","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.infsof.2017.01.007","volume":"85","author":"R Hoda","year":"2017","unstructured":"Hoda R, Salleh N, Grundy J, Tee HM (2017) Systematic literature reviews in agile software development: a tertiary study. Inf Softw Technol 85:60\u201370","journal-title":"Inf Softw Technol"},{"key":"4427_CR8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00607-016-0489-6","volume":"99","author":"SS Rathore","year":"2017","unstructured":"Rathore SS, Kumar S (2017) A decision tree logic based recommendation system to select software fault prediction techniques. Computing 99:255\u2013285","journal-title":"Computing"},{"issue":"05","key":"4427_CR9","first-page":"655","volume":"42","author":"D Gupta","year":"2017","unstructured":"Gupta D, Saxena K (2017) Software bug prediction using object-oriented metrics. Sadhana - Acad Proc Eng Sci 42(05):655\u2013669","journal-title":"Sadhana - Acad Proc Eng Sci"},{"issue":"05","key":"4427_CR10","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1016\/j.eswa.2008.10.027","volume":"36","author":"C Catal","year":"2009","unstructured":"Catal C, Diri B (2009) A systematic review of software fault prediction studies. Expert Syst Appl 36(05):7346\u20137354","journal-title":"Expert Syst Appl"},{"key":"4427_CR11","unstructured":"Halstead MH (1977) Elements of software science (operating and programming systems series) USA: Elsevier science inc"},{"issue":"4","key":"4427_CR12","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1109\/TSE.1976.233837","volume":"SE-2","author":"TJ McCabe","year":"1976","unstructured":"McCabe TJ (1976) A complexity measure, IEEE. Trans Softw Eng SE-2(4):308\u2013320","journal-title":"Trans Softw Eng"},{"issue":"6","key":"4427_CR13","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/32.295895","volume":"20","author":"SR Chidamber","year":"1994","unstructured":"Chidamber SR, Kemerer CF (1994) A metrics suite for object oriented design. IEEE Trans Softw Eng 20(6):476\u2013493","journal-title":"IEEE Trans Softw Eng"},{"key":"4427_CR14","unstructured":"Lorenz M, Kidd J (1994) Object-oriented software metrics: a practical guide. Prentice-Hall, Inc"},{"issue":"1","key":"4427_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/32.979986","volume":"28","author":"J Bansiya","year":"2002","unstructured":"Bansiya J, Davis CG (2002) A hierarchical model for object-oriented design quality assessment. IEEE Trans Softw Eng 28(1):4\u201317","journal-title":"IEEE Trans Softw Eng"},{"key":"4427_CR16","doi-asserted-by":"crossref","unstructured":"Deep Singh P, Chug A (2017) Software defect prediction analysis using machine learning algorithms. In: 2017 7th International conference on cloud computing, data science engineering - confluence, pp 775\u2013781","DOI":"10.1109\/CONFLUENCE.2017.7943255"},{"issue":"10","key":"4427_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJOSSP.2019100101","volume":"10","author":"O Qasem","year":"2019","unstructured":"Qasem O, Akour M (2019) Software fault prediction using deep learning algorithms. Int J Open Source Softw Process 10(10): 1\u201319","journal-title":"Int J Open Source Softw Process"},{"key":"4427_CR18","doi-asserted-by":"publisher","first-page":"05","DOI":"10.1016\/j.eswa.2017.05.020","volume":"84","author":"J Oliveira","year":"2017","unstructured":"Oliveira J, Pontes K, Sartori I, Embiru\u00e7u M (2017) Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Syst Appl 84:05","journal-title":"Expert Syst Appl"},{"issue":"08","key":"4427_CR19","first-page":"241","volume":"14","author":"X Dong","year":"2019","unstructured":"Dong X, Yu Z, Cao W, Shi Y, Ma Q (2019) A survey on ensemble learning. Frontiers Comput Sci 14(08):241\u2013258","journal-title":"Frontiers Comput Sci"},{"issue":"1","key":"4427_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"4427_CR21","doi-asserted-by":"crossref","unstructured":"Parmar A, Katariya R, Patel V (2019) A review on random forest: an ensemble classifier. In: Hemanth J, Fernando X, Lafata P, Baig Z (eds) International conference on intelligent data communication technologies and internet of things (ICICI) 2018. Springer international publishing, Cham, pp 758\u2013763","DOI":"10.1007\/978-3-030-03146-6_86"},{"key":"4427_CR22","doi-asserted-by":"crossref","unstructured":"Shaik AB, Srinivasan S (2019) A brief survey on random forest ensembles in classification model. In: Bhattacharyya S, Hassanien AE, Gupta D, Khanna A, Pan I (eds) International conference on innovative computing and communications. Singapore, Springer Singapore pp 253\u2013260","DOI":"10.1007\/978-981-13-2354-6_27"},{"issue":"05","key":"4427_CR23","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TSMCA.2010.2084081","volume":"41","author":"T Khoshgoftaar","year":"2011","unstructured":"Khoshgoftaar T, Van Hulse J, Napolitano A (2011) Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Trans Syst Man Cybern Part A 41(05):552\u2013568","journal-title":"IEEE Trans Syst Man Cybern Part A"},{"issue":"3","key":"4427_CR24","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3233\/IDA-1997-1302","volume":"1","author":"M Dash","year":"1997","unstructured":"Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(3):131\u2013156","journal-title":"Intell Data Anal"},{"key":"4427_CR25","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312","journal-title":"Neurocomputing"},{"key":"4427_CR26","unstructured":"Liu H, Motoda H (2012) Feature selection for knowledge discovery and data mining. Springer science & business media, vol 454"},{"key":"4427_CR27","doi-asserted-by":"crossref","unstructured":"Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, vol 74","DOI":"10.1002\/9780470496916"},{"key":"4427_CR28","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ins.2013.02.041","volume":"237","author":"I Boussa\u00efd","year":"2013","unstructured":"Boussa\u00efd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82\u2013117","journal-title":"Inf Sci"},{"issue":"3","key":"4427_CR29","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/S0031-3203(01)00046-2","volume":"35","author":"H Zhang","year":"2002","unstructured":"Zhang H, Sun G (2002) Feature selection using tabu search method. Pattern Recognit 35 (3):701\u2013711","journal-title":"Pattern Recognit"},{"issue":"1","key":"4427_CR30","doi-asserted-by":"publisher","first-page":"7637","DOI":"10.1007\/s12652-020-02484-z","volume":"12","author":"MA Al-Betar","year":"2021","unstructured":"Al-Betar MA, Hammouri AI, Awadallah MA, Doush IA (2021) Binary \u03b2-hill climbing optimizer with s-shape transfer function for feature selection. J Ambient Intell Humanized Comput 12(1):7637\u20137665","journal-title":"J Ambient Intell Humanized Comput"},{"issue":"2","key":"4427_CR31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s40595-018-0107-y","volume":"5","author":"D Boughaci","year":"2018","unstructured":"Boughaci D, Alkhawaldeh AA-S (2018) Three local search-based methods for feature selection in credit scoring. Vietnam J Comput Sci 5(2):107\u2013121","journal-title":"Vietnam J Comput Sci"},{"issue":"4","key":"4427_CR32","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1016\/j.eswa.2013.09.004","volume":"41","author":"S Oreski","year":"2014","unstructured":"Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst Appl 41(4):2052\u20132064","journal-title":"Expert Syst Appl"},{"key":"4427_CR33","doi-asserted-by":"publisher","first-page":"105806","DOI":"10.1016\/j.knosys.2020.105806","volume":"196","author":"J Ma","year":"2020","unstructured":"Ma J, Gao X (2020) A filter-based feature construction and feature selection approach for classification using genetic programming. Knowl-Based Syst 196:105806","journal-title":"Knowl-Based Syst"},{"key":"4427_CR34","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ins.2019.08.040","volume":"507","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Gong D-W, Gao X-Z, Tian T, Sun X-Y (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67\u201385","journal-title":"Inf Sci"},{"key":"4427_CR35","doi-asserted-by":"publisher","first-page":"166066","DOI":"10.1109\/ACCESS.2019.2953298","volume":"7","author":"B Wei","year":"2019","unstructured":"Wei B, Zhang W, Xia X, Zhang Y, Yu F, Zhu Z (2019) Efficient feature selection algorithm based on particle swarm optimization with learning memory. IEEE Access 7:166066\u2013166078","journal-title":"IEEE Access"},{"key":"4427_CR36","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.knosys.2018.05.009","volume":"154","author":"H Faris","year":"2018","unstructured":"Faris H, Mafarja MM, Heidari AA, Aljarah I, Ala\u2019M A-Z, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43\u201367","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"4427_CR37","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1016\/j.jksuci.2021.01.015","volume":"34","author":"S Kassaymeh","year":"2022","unstructured":"Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M (2022) Salp swarm optimizer for modeling the software fault prediction problem. J King Saud Univ-Comput Inf Sci 34(6):3365\u20133378","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"4427_CR38","doi-asserted-by":"publisher","first-page":"106131","DOI":"10.1016\/j.knosys.2020.106131","volume":"203","author":"AI Hammouri","year":"2020","unstructured":"Hammouri AI, Mafarja M, Al-Betar MA, Awadallah MA, Abu-Doush I (2020) An improved dragonfly algorithm for feature selection. Knowl-Based Syst 203:106131. https:\/\/doi.org\/10.1016\/j.knosys.2020.106131","journal-title":"Knowl-Based Syst"},{"key":"4427_CR39","doi-asserted-by":"publisher","first-page":"105675","DOI":"10.1016\/j.compbiomed.2022.105675","volume":"147","author":"MA Awadallah","year":"2022","unstructured":"Awadallah MA, Al-Betar MA, Braik MS, Hammouri AI, Doush IA, Zitar RA (2022) An enhanced binary rat swarm optimizer based on local-best concepts of pso and collaborative crossover operators for feature selection. Comput Bio Med 147:105675. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105675","journal-title":"Comput Bio Med"},{"issue":"15","key":"4427_CR40","doi-asserted-by":"publisher","first-page":"6249","DOI":"10.1007\/s00500-018-3282-y","volume":"23","author":"MM Mafarja","year":"2019","unstructured":"Mafarja MM, Mirjalili S (2019) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Comput 23(15):6249\u20136265","journal-title":"Soft Comput"},{"issue":"2","key":"4427_CR41","first-page":"1","volume":"5","author":"R Sawalha","year":"2012","unstructured":"Sawalha R, Doush IA (2012) Face recognition using harmony search-based selected features. Int J Hybrid Inf Technol 5(2): 1\u201316","journal-title":"Int J Hybrid Inf Technol"},{"key":"4427_CR42","doi-asserted-by":"publisher","first-page":"107629","DOI":"10.1016\/j.knosys.2021.107629","volume":"235","author":"M Alweshah","year":"2022","unstructured":"Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowl-Based Syst 235:107629","journal-title":"Knowl-Based Syst"},{"key":"4427_CR43","doi-asserted-by":"publisher","first-page":"105285","DOI":"10.1016\/j.knosys.2019.105285","volume":"192","author":"M Paniri","year":"2020","unstructured":"Paniri M, Dowlatshahi MB, Nezamabadi-pour H (2020) Mlaco: a multi-label feature selection algorithm based on ant colony optimization. Knowl-Based Syst 192:105285","journal-title":"Knowl-Based Syst"},{"issue":"7","key":"4427_CR44","doi-asserted-by":"publisher","first-page":"7637","DOI":"10.1007\/s12652-020-02484-z","volume":"12","author":"MA Al-Betar","year":"2021","unstructured":"Al-Betar MA, Hammouri AI, Awadallah MA, Abu Doush I (2021) Binary \u03b2-hill climbing optimizer with s-shape transfer function for feature selection. J Ambient Intell Humanized Comput 12(7):7637\u20137665","journal-title":"J Ambient Intell Humanized Comput"},{"issue":"1","key":"4427_CR45","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00521-017-2988-6","volume":"31","author":"GI Sayed","year":"2019","unstructured":"Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171\u2013188","journal-title":"Neural Comput Appl"},{"issue":"12","key":"4427_CR46","doi-asserted-by":"publisher","first-page":"10875","DOI":"10.1007\/s13369-020-04871-2","volume":"45","author":"MA Awadallah","year":"2020","unstructured":"Awadallah MA, Al-Betar MA, Hammouri AI, Alomari OA (2020) Binary jaya algorithm with adaptive mutation for feature selection. Arab J Sci Eng 45(12):10875\u201310890","journal-title":"Arab J Sci Eng"},{"key":"4427_CR47","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.cose.2018.11.005","volume":"81","author":"B Selvakumar","year":"2019","unstructured":"Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Comput Security 81:148\u2013155","journal-title":"Comput Security"},{"key":"4427_CR48","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.eswa.2019.06.044","volume":"137","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Cheng S, Shi Y, Gong D-W, Zhao X (2019) Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. Expert Syst Appl 137:46\u201358","journal-title":"Expert Syst Appl"},{"key":"4427_CR49","doi-asserted-by":"publisher","first-page":"107470","DOI":"10.1016\/j.patcog.2020.107470","volume":"107","author":"RCT de Souza","year":"2020","unstructured":"de Souza RCT, de Macedo CA, Dos Santos Coelho L, Pierezan J, Mariani VC (2020) Binary coyote optimization algorithm for feature selection. Pattern Recognit 107:107470","journal-title":"Pattern Recognit"},{"issue":"3","key":"4427_CR50","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s12559-017-9542-9","volume":"10","author":"I Aljarah","year":"2018","unstructured":"Aljarah I, Ala\u2019M A-Z, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognit Computat 10(3):478\u2013495","journal-title":"Cognit Computat"},{"key":"4427_CR51","unstructured":"Karimi A, Irajimoghaddam M, Bastami E (2022) Feature selection using combination of genetic-whale-ant colony algorithms for software fault prediction by machine learning. Electr Cyber Defense, vol 10(1)"},{"issue":"1","key":"4427_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJAMC.292508","volume":"13","author":"W Rhmann","year":"2022","unstructured":"Rhmann W (2022) Software vulnerability prediction using grey wolf-optimized random forest on the unbalanced data sets. Int J Appl Metaheuristic Comput (IJAMC) 13(1):1\u201315","journal-title":"Int J Appl Metaheuristic Comput (IJAMC)"},{"key":"4427_CR53","doi-asserted-by":"publisher","first-page":"108511","DOI":"10.1016\/j.knosys.2022.108511","volume":"244","author":"S Kassaymeh","year":"2022","unstructured":"Kassaymeh S, Al-Laham M, Al-Betar MA, Alweshah M, Abdullah S, Makhadmeh SN (2022) Backpropagation neural network optimization and software defect estimation modelling using a hybrid salp swarm optimizer-based simulated annealing algorithm. Knowl-Based Syst 244:108511","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"4427_CR54","first-page":"100105","volume":"2","author":"K Tameswar","year":"2022","unstructured":"Tameswar K, Suddul G, Dookhitram K (2022) A hybrid deep learning approach with genetic and coral reefs metaheuristics for enhanced defect detection in software. Int J Inf Manag Data Insights 2(2):100105","journal-title":"Int J Inf Manag Data Insights"},{"key":"4427_CR55","doi-asserted-by":"publisher","first-page":"114616","DOI":"10.1016\/j.cma.2022.114616","volume":"392","author":"H Zamani","year":"2022","unstructured":"Zamani H, Nadimi-Shahraki MH, Gandomi AH (2022) Starling murmuration optimizer: a novel bio-inspired algorithm for global and engineering optimization. Comput Methods Appl Mech Eng 392:114616","journal-title":"Comput Methods Appl Mech Eng"},{"key":"4427_CR56","doi-asserted-by":"publisher","first-page":"104314","DOI":"10.1016\/j.engappai.2021.104314","volume":"104","author":"H Zamani","year":"2021","unstructured":"Zamani H, Nadimi-Shahraki MH, Gandomi AH (2021) Qana: quantum-based avian navigation optimizer algorithm. Eng Appl Artif Intell 104:104314","journal-title":"Eng Appl Artif Intell"},{"key":"4427_CR57","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.asoc.2018.07.033","volume":"71","author":"H Shayanfar","year":"2018","unstructured":"Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728\u2013746","journal-title":"Appl Soft Comput"},{"key":"4427_CR58","doi-asserted-by":"publisher","first-page":"107408","DOI":"10.1016\/j.cie.2021.107408","volume":"158","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021) African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Industr Eng 158:107408","journal-title":"Comput Industr Eng"},{"issue":"10","key":"4427_CR59","doi-asserted-by":"publisher","first-page":"5887","DOI":"10.1002\/int.22535","volume":"36","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021) Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems. Int J Intell Syst 36 (10):5887\u20135958","journal-title":"Int J Intell Syst"},{"key":"4427_CR60","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"issue":"2","key":"4427_CR61","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1080\/17445760.2019.1617866","volume":"36","author":"M Mafarja","year":"2021","unstructured":"Mafarja M, Jaber I, Ahmed S, Thaher T (2021) Whale optimisation algorithm for high-dimensional small-instance feature selection. Int J Parallel Emergent Distributed Syst 36(2):80\u201396","journal-title":"Int J Parallel Emergent Distributed Syst"},{"key":"4427_CR62","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441\u2013453","journal-title":"Appl Soft Comput"},{"key":"4427_CR63","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.future.2020.05.020","volume":"112","author":"M Mafarja","year":"2020","unstructured":"Mafarja M, Heidari AA, Habib M, Faris H, Thaher T, Aljarah I (2020) Augmented whale feature selection for iot attacks: structure, analysis and applications. Futur Gener Comput Syst 112:18\u201340","journal-title":"Futur Gener Comput Syst"},{"key":"4427_CR64","doi-asserted-by":"publisher","first-page":"105858","DOI":"10.1016\/j.compbiomed.2022.105858","volume":"148","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki MH, Zamani H, Mirjalili S (2022) Enhanced whale optimization algorithm for medical feature selection: a covid-19 case study. Comput Bio Med 148:105858","journal-title":"Comput Bio Med"},{"issue":"9","key":"4427_CR65","first-page":"1243","volume":"14","author":"H Zamani","year":"2016","unstructured":"Zamani H, Nadimi-Shahraki M-H (2016) Feature selection based on whale optimization algorithm for diseases diagnosis. Int J Comput Sci Inf Security 14(9):1243","journal-title":"Int J Comput Sci Inf Security"},{"key":"4427_CR66","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"01","author":"D Wolpert","year":"1997","unstructured":"Wolpert D, Macready W (1997) No free lunch theorems for optimization. Evolutionary Computat IEEE 01:67\u201382","journal-title":"Evolutionary Computat IEEE"},{"key":"4427_CR67","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.procs.2018.05.115","volume":"132","author":"A Singh","year":"2018","unstructured":"Singh A, Bhatia R, Singhrova A (2018) Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics. Procedia Comput Sci 132:993\u20131001","journal-title":"Procedia Comput Sci"},{"key":"4427_CR68","first-page":"07","volume":"2176","author":"S Yogesh","year":"2009","unstructured":"Yogesh S, Arvinder K, Malhotra R (2009) Software fault proneness prediction using support vector machines. Lecture Notes Eng Comput Sci 2176:07","journal-title":"Lecture Notes Eng Comput Sci"},{"issue":"03","key":"4427_CR69","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1142\/S0218213003001204","volume":"12","author":"TM Khoshgoftaar","year":"2003","unstructured":"Khoshgoftaar TM, Seliya N (2003) Software quality classification modeling using the sprint decision tree algorithm. Int J Artif Intell Tools 12(03):207\u2013225","journal-title":"Int J Artif Intell Tools"},{"key":"4427_CR70","doi-asserted-by":"crossref","unstructured":"Yuan X, Khoshgoftaar TM, Allen EB, Ganesan K (2000) An application of fuzzy clustering to software quality prediction. In: Application-specific systems and software engineering technology, 2000 proceedings. 3rd IEEE symposium on. IEEE, pp 85\u201390","DOI":"10.1109\/ASSET.2000.888052"},{"issue":"1","key":"4427_CR71","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TSE.2007.256941","volume":"33","author":"T Menzies","year":"2007","unstructured":"Menzies T, Greenwald J, Frank A (2007) Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng 33(1):2\u201313","journal-title":"IEEE Trans Softw Eng"},{"key":"4427_CR72","first-page":"31","volume":"4","author":"V kumar Dwivedi","year":"2016","unstructured":"kumar Dwivedi V, Singh MK (2016) Software defect prediction using data mining classification approach. Int J Tech Res Appl 4:31\u201335","journal-title":"Int J Tech Res Appl"},{"key":"4427_CR73","doi-asserted-by":"crossref","unstructured":"Carrozza G, Cotroneo D, Natella R, Pietrantuono R, Russo S (2013) Analysis and prediction of mandelbugs in an industrial software system. In: Software testing, verification and validation (ICST), 2013 IEEE sixth international conference on. IEEE, pp 262\u2013271","DOI":"10.1109\/ICST.2013.21"},{"key":"4427_CR74","first-page":"43","volume":"01","author":"M Bisi","year":"2015","unstructured":"Bisi M, Goyal N (2015) Early prediction of software fault-prone module using artificial neural network. Int J Performability Eng 01:43\u201352","journal-title":"Int J Performability Eng"},{"key":"4427_CR75","first-page":"06","volume":"23","author":"B Caglayan","year":"2014","unstructured":"Caglayan B, Tosun A, Bener A, Miranskyy A (2014) Predicting defective modules in different test phases. Softw Qual J 23:06","journal-title":"Softw Qual J"},{"issue":"2","key":"4427_CR76","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s11219-016-9353-3","volume":"26","author":"D Bowes","year":"2018","unstructured":"Bowes D, Hall T, Petri\u0107 J (2018) Software defect prediction: do different classifiers find the same defects? Softw Qual J 26(2):525\u2013552","journal-title":"Softw Qual J"},{"key":"4427_CR77","doi-asserted-by":"crossref","unstructured":"Thaher T, Khamayseh F (2021) A classification model for software bug prediction based on ensemble deep learning approach boosted with smote technique. In: Sharma H, Saraswat M, Yadav A, Kim JH, Bansal JC (eds) Congress on intelligent systems. (Singapore), Springer Singapore, pp 99\u2013113","DOI":"10.1007\/978-981-33-6984-9_9"},{"key":"4427_CR78","doi-asserted-by":"crossref","unstructured":"Cahill J, Hogan JM, Thomas R (2013) Predicting fault-prone software modules with rank sum classification. In: 2013 22nd Australian software engineering conference. IEEE, pp 211\u2013219","DOI":"10.1109\/ASWEC.2013.33"},{"key":"4427_CR79","doi-asserted-by":"publisher","first-page":"08","DOI":"10.1016\/j.asoc.2016.08.025","volume":"49","author":"E Erturk","year":"2016","unstructured":"Erturk E, Sezer E (2016) Iterative software fault prediction with a hybrid approach. Appl Soft Comput 49:08","journal-title":"Appl Soft Comput"},{"key":"4427_CR80","first-page":"02","volume":"259","author":"T Khoshgoftaar","year":"2014","unstructured":"Khoshgoftaar T, Xiao Y, Gao K (2014) Software quality assessment using a multi-strategy classifier. Inf Sci - ISCI 259:02","journal-title":"Inf Sci - ISCI"},{"key":"4427_CR81","doi-asserted-by":"crossref","unstructured":"Carrozza G, Cotroneo D, Natella R, Pietrantuono R, Russo S (2013) Analysis and prediction of mandelbugs in an industrial software system, 03","DOI":"10.1109\/ICST.2013.21"},{"key":"4427_CR82","doi-asserted-by":"publisher","first-page":"04","DOI":"10.1016\/j.eswa.2017.04.014","volume":"82","author":"S Rathore","year":"2017","unstructured":"Rathore S, Kumar S (2017) Towards an ensemble based system for predicting the number of software faults. Expert Syst Appl 82:04","journal-title":"Expert Syst Appl"},{"key":"4427_CR83","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.compeleceng.2018.02.043","volume":"67","author":"GR Choudhary","year":"2018","unstructured":"Choudhary GR, Kumar S, Kumar K, Mishra A, Catal C (2018) Empirical analysis of change metrics for software fault prediction. Comput Electr Eng 67:15\u201324","journal-title":"Comput Electr Eng"},{"key":"4427_CR84","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s11334-017-0295-0","volume":"13","author":"R Shatnawi","year":"2017","unstructured":"Shatnawi R (2017) The application of roc analysis in threshold identification, data imbalance and metrics selection for software fault prediction. Innov Syst Softw Eng 13:201\u2013217","journal-title":"Innov Syst Softw Eng"},{"issue":"16","key":"4427_CR85","doi-asserted-by":"publisher","first-page":"12201","DOI":"10.1007\/s00521-019-04368-6","volume":"32","author":"H Chantar","year":"2020","unstructured":"Chantar H, Mafarja M, Alsawalqah H, Heidari AA, Aljarah I, Faris H (2020) Feature selection using binary grey wolf optimizer with elite-based crossover for arabic text classification. Neural Comput Appl 32(16):12201\u201312220","journal-title":"Neural Comput Appl"},{"issue":"03","key":"4427_CR86","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.ins.2008.12.001","volume":"179","author":"C Catal","year":"2009","unstructured":"Catal C, Diri B (2009) Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Inf Sci 179(03):1040\u20131058","journal-title":"Inf Sci"},{"issue":"13","key":"4427_CR87","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.3390\/app9132764","volume":"9","author":"AO Balogun","year":"2019","unstructured":"Balogun AO, Basri S, Abdulkadir SJ, Hashim AS (2019) Performance analysis of feature selection methods in software defect prediction: a search method approach. Appl Sci 9(13):2764","journal-title":"Appl Sci"},{"key":"4427_CR88","doi-asserted-by":"crossref","unstructured":"Jia L (2018) A hybrid feature selection method for software defect prediction. In: IOP conference series: materials science and engineering. IOP publishing, vol 394, p 032035","DOI":"10.1088\/1757-899X\/394\/3\/032035"},{"issue":"1","key":"4427_CR89","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1166\/asl.2014.5283","volume":"20","author":"RS Wahono","year":"2014","unstructured":"Wahono RS, Herman NS (2014) Genetic feature selection for software defect prediction. Adv Sci Lett 20(1):239\u2013244","journal-title":"Adv Sci Lett"},{"issue":"5","key":"4427_CR90","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.4304\/jsw.9.5.1324-1333","volume":"9","author":"RS Wahono","year":"2014","unstructured":"Wahono RS, Suryana N, Ahmad S (2014) Metaheuristic optimization based feature selection for software defect prediction. J Softw 9(5):1324\u20131333","journal-title":"J Softw"},{"key":"4427_CR91","doi-asserted-by":"crossref","unstructured":"Thaher T, Arman N (2020) Efficient multi-swarm binary harris hawks optimization as a feature selection approach for software fault prediction. In: 2020 11th International conference on information and communication systems (ICICS), pp 249\u2013254","DOI":"10.1109\/ICICS49469.2020.239557"},{"issue":"04","key":"4427_CR92","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1093\/bib\/bbk007","volume":"7","author":"P Larranaga","year":"2006","unstructured":"Larranaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano J, Ar-ma\u00f1anzas R, Santaf\u00e9 G, P\u00e9rez A, Robles V (2006) Machine learning in bioinformatics. Briefings bioinformatics 7(04):86\u2013112","journal-title":"Briefings bioinformatics"},{"key":"4427_CR93","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.measurement.2018.04.069","volume":"125","author":"F Wang","year":"2018","unstructured":"Wang F, Ma S, Wang H, Li Y, Qin Z, Zhang J (2018) A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for nox emission estimation of coal-fired power plants. Measurement 125:303\u2013312","journal-title":"Measurement"},{"issue":"10","key":"4427_CR94","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1016\/j.eswa.2015.02.001","volume":"42","author":"M Malekipirbazari","year":"2015","unstructured":"Malekipirbazari M, Aksakalli V (2015) Risk assessment in social lending via random forests. Expert Syst Appl 42(10):4621\u20134631","journal-title":"Expert Syst Appl"},{"issue":"10","key":"4427_CR95","first-page":"1263","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia E (2009) Learning from imbalanced data. Knowl Data Eng IEEE Trans 21 (10):1263\u20131284","journal-title":"Knowl Data Eng IEEE Trans"},{"issue":"01","key":"4427_CR96","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N Chawla","year":"2002","unstructured":"Chawla N, Bowyer K, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res (JAIR) 16(01):321\u2013357","journal-title":"J Artif Intell Res (JAIR)"},{"key":"4427_CR97","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.knosys.2017.12.037","volume":"145","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Ala\u2019M A-Z, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25\u201345","journal-title":"Knowl-Based Syst"},{"issue":"01","key":"4427_CR98","first-page":"211","volume":"4","author":"V Kumar","year":"2014","unstructured":"Kumar V, Minz S (2014) Feature selection: a literature review. Smart Comput Rev 4 (01):211\u2013229","journal-title":"Smart Comput Rev"},{"key":"4427_CR99","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","volume":"260","author":"MM Mafarja","year":"2017","unstructured":"Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302\u2013312","journal-title":"Neurocomputing"},{"issue":"09","key":"4427_CR100","doi-asserted-by":"publisher","first-page":"119","DOI":"10.14257\/ijdta.2016.9.8.13","volume":"9","author":"E Amrieh","year":"2016","unstructured":"Amrieh E, Hamtini T, Aljarah I (2016) Mining educational data to predict student\u2019s academic performance using ensemble methods. Int J Database Theory Appl 9(09):119\u2013136","journal-title":"Int J Database Theory Appl"},{"key":"4427_CR101","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.knosys.2018.08.003","volume":"161","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA, Faris H, Fournier-Viger P, Li X, Mirjalili S (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl-Based Syst 161:185\u2013204","journal-title":"Knowl-Based Syst"},{"key":"4427_CR102","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.inffus.2018.08.002","volume":"48","author":"H Faris","year":"2019","unstructured":"Faris H, Ala\u2019M A-Z, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48:67\u201383","journal-title":"Inf Fusion"},{"key":"4427_CR103","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","volume":"9","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili S, Lewis A (2013) S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evolutionary Computat 9:1\u201314","journal-title":"Swarm Evolutionary Computat"},{"key":"4427_CR104","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Systems, man, and cybernetics, 1997. Computational cybernetics and simulation, 1997 IEEE international conference on. IEEE, vol 5, pp 4104\u20134108","DOI":"10.1109\/ICSMC.1997.637339"},{"issue":"3","key":"4427_CR105","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s11047-009-9175-3","volume":"9","author":"E Rashedi","year":"2010","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) Bgsa: binary gravitational search algorithm. Nat Comput 9(3):727\u2013745","journal-title":"Nat Comput"},{"key":"4427_CR106","doi-asserted-by":"crossref","unstructured":"Mirjalili S, Dong J (2020) Multi-objective optimization using artificial intelligence techniques. 01","DOI":"10.1007\/978-3-030-24835-2"},{"issue":"7","key":"4427_CR107","doi-asserted-by":"publisher","first-page":"e0158738","DOI":"10.1371\/journal.pone.0158738","volume":"11","author":"E Emary","year":"2016","unstructured":"Emary E, Zawbaa HM (2016) Impact of chaos functions on modern swarm optimizers. PloS One 11(7):e0158738","journal-title":"PloS One"},{"key":"4427_CR108","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"4427_CR109","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Generation Comput Syst 97:849\u2013872","journal-title":"Future Generation Comput Syst"},{"key":"4427_CR110","unstructured":"(2017). Tera-Promise. http:\/\/openscience.us\/repo. Last Accessed 24 Nov 2017"},{"key":"4427_CR111","doi-asserted-by":"crossref","unstructured":"Jureczko M, Madeyski L (2010) Towards identifying software project clusters with regard to defect prediction. In: Proceedings of the 6th international conference on predictive models in software engineering, PROMISE \u201910, (New York). ACM, pp 9:1\u20139:10","DOI":"10.1145\/1868328.1868342"},{"key":"4427_CR112","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/32.295895","volume":"20","author":"SR Chidamber","year":"1994","unstructured":"Chidamber SR, Kemerer CF (1994) A metrics suite for object oriented design. IEEE Trans Softw Eng 20:476\u2013493","journal-title":"IEEE Trans Softw Eng"},{"key":"4427_CR113","doi-asserted-by":"crossref","unstructured":"Thaher T, Heidari AA, Mafarja M, Dong JS, Mirjalili S (2020) Binary harris hawks optimizer for high-dimensional, low sample size feature selection. Singapore: Springer Singapore, pp 251\u2013272","DOI":"10.1007\/978-981-32-9990-0_12"},{"key":"4427_CR114","doi-asserted-by":"crossref","unstructured":"Thaher T, Chantar H, Too J, Mafarja M, Turabieh H, Houssein EH (2022) Boolean particle swarm optimization with various evolutionary population dynamics approaches for feature selection problems, vol 195","DOI":"10.1016\/j.eswa.2022.116550"},{"issue":"5","key":"4427_CR115","doi-asserted-by":"publisher","first-page":"499","DOI":"10.3897\/jucs.78218","volume":"28","author":"T Thaher","year":"2022","unstructured":"Thaher T, Awad M, Aldasht M, Sheta A, Turabieh H, Chantar H (2022) An enhanced evolutionary based feature selection approach using grey wolf optimizer for the classification of high-dimensional biological data. JUCS - J Univ Comput Sci 28(5):499\u2013539","journal-title":"JUCS - J Univ Comput Sci"},{"key":"4427_CR116","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/app11146516","volume":"11","author":"H Chantar","year":"2021","unstructured":"Chantar H, Thaher T, Turabieh H, Mafarja M, Sheta A (2021) Bhho-tvs: a binary harris hawks optimizer with time-varying scheme for solving data classification problems. Applied Sci 11:14","journal-title":"Applied Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04427-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04427-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04427-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:36:13Z","timestamp":1744158973000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04427-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":116,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["4427"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04427-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,9]]},"assertion":[{"value":"23 December 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2023","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}