{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:10:03Z","timestamp":1780611003779,"version":"3.54.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00521-024-09749-0","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T09:01:39Z","timestamp":1713862899000},"page":"12935-12950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cognition2Vocation: meta-learning via ConvNets and continuous transformers"],"prefix":"10.1007","volume":"36","author":[{"given":"Sara","family":"Kamran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1956-6373","authenticated-orcid":false,"given":"Saeid","family":"Hosseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayna","family":"Esmailzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Kangavari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"9749_CR1","doi-asserted-by":"crossref","unstructured":"AbdElminaam DS, ElMasry N, Talaat Y, Adel Md., Hisham A, Atef K, Mohamed A, Akram Md. (2021) Hr-chat bot: Designing and building effective interview chat-bots for fake cv detection. In: 2021 Int. Mobile, Intelligent, and Ubiquitous Computing Conf. IEEE","DOI":"10.1109\/MIUCC52538.2021.9447638"},{"key":"9749_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114833","volume":"176","author":"S George","year":"2021","unstructured":"George S, George S, Lathabai HH, Prabhakaran T, Changat M (2021) A framework for inventor collaboration recommendation system based on network approach. Expert Syst Appl 176:114833","journal-title":"Expert Syst Appl"},{"key":"9749_CR3","doi-asserted-by":"crossref","unstructured":"Kenthapadi K, Le B, Venkataraman G (2017) Personalized job recommendation system at linkedin: Practical challenges and lessons learned. In: Proc. of the eleventh ACM Conf. on recommender systems","DOI":"10.1145\/3109859.3109921"},{"key":"9749_CR4","doi-asserted-by":"crossref","unstructured":"Dias RD, Conboy HM, Gabany JM, Clarke LA, Osterwei LJ, Avrunin GS, Arney D, Goldman JM, Riccardi G, Yule SJ (2018) Development of an interactive dashboard to analyze cognitive workload of surgical teams during complex procedural care. In: IEEE Conf. on Cognitive and Computational Aspects of Situation Management (CogSIMA)","DOI":"10.1109\/COGSIMA.2018.8423995"},{"issue":"23","key":"9749_CR5","doi-asserted-by":"publisher","first-page":"4809","DOI":"10.3390\/ijerph16234809","volume":"16","author":"I Urquijo","year":"2019","unstructured":"Urquijo I, Extremera N, Azanza G (2019) The contribution of emotional intelligence to career success: beyond personality traits. Int J Environ Res Public Health 16(23):4809","journal-title":"Int J Environ Res Public Health"},{"key":"9749_CR6","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.jbusres.2019.04.029","volume":"101","author":"A Khedhaouria","year":"2019","unstructured":"Khedhaouria A, Cucchi A (2019) Technostress creators, personality traits, and job burnout: a fuzzy-set configurational analysis. J Bus Res 101:349\u2013361","journal-title":"J Bus Res"},{"key":"9749_CR7","unstructured":"Alamsyah A, Dudija N (2021) Identifying personality of the new job applicants using the ontology model on twitter data. In: 2021 2nd Int. Conf. on ICT for Rural Development (IC-ICTRuDev). IEEE,"},{"issue":"6","key":"9749_CR8","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1049\/el.2018.7980","volume":"55","author":"A Rusiecki","year":"2019","unstructured":"Rusiecki A (2019) Trimmed categorical cross entropy for deep learning with label noise. Electron Lett 55(6):319\u2013320","journal-title":"Electron Lett"},{"key":"9749_CR9","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1109\/TIP.2022.3152049","volume":"31","author":"X Sun","year":"2022","unstructured":"Sun X, Huang J, Zheng S, Rao X, Wang M (2022) Personality assessment based on multimodal attention network learning with category-based mean square error. IEEE Trans. Image Process 31:2162\u20132174","journal-title":"IEEE Trans. Image Process"},{"issue":"1","key":"9749_CR10","first-page":"148","volume":"1","author":"M Taylor","year":"2020","unstructured":"Taylor M (2020) Personality styles: Why they matter in the workplace. Econ Altern 1(1):148\u2013163","journal-title":"Econ Altern"},{"key":"9749_CR11","doi-asserted-by":"crossref","unstructured":"Nunes I, Treude C, Calefato F (2020) The impact of dynamics of collaborative software engineering on introverts: a study protocol. In: Proc. of the 17th Int. Conf. on Mining Software Repositories,","DOI":"10.1145\/3379597.3387505"},{"key":"9749_CR12","doi-asserted-by":"crossref","unstructured":"Kern ML, McCarthy PX, Chakrabarty D, Rizoiu M-A (2019) Social media-predicted personality traits and values can help match people to their ideal jobs. In: Proc. of the National Academy of Sciences","DOI":"10.1073\/pnas.1917942116"},{"issue":"1","key":"9749_CR13","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TKDE.2020.2982148","volume":"34","author":"S Najafipour","year":"2020","unstructured":"Najafipour S, Hosseini S, Hua W, Kangavari MR, Zhou X (2020) SoulMate: short-text author linking through multi-aspect temporal-textual embedding. IEEE Trans Knowl Data Eng 34(1):448\u2013461","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9749_CR14","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1007\/s10618-020-00688-7","volume":"34","author":"S Hosseini","year":"2020","unstructured":"Hosseini S, Najafipour S, Cheung NM, Yin H, Kangavari MR, Zhou X (2020) TEAGS: time-aware text embedding approach to generate subgraphs. Data Min Knowl Discov 34:1136\u20131174","journal-title":"Data Min Knowl Discov"},{"key":"9749_CR15","doi-asserted-by":"crossref","unstructured":"Hosseini S, Unankard S, Zhou X, Sadiq S (2014) Location oriented phrase detection in microblogs. In: Database Systems for Advanced Applications: 19th International Conference, Springer International Publishing","DOI":"10.1007\/978-3-319-05810-8_33"},{"issue":"18","key":"9749_CR16","doi-asserted-by":"publisher","first-page":"13565","DOI":"10.1007\/s00521-023-08435-x","volume":"35","author":"S Kamran","year":"2023","unstructured":"Kamran S, Zall R, Hosseini S, Kangavari M, Rahmani S, Hua W (2023) EmoDNN: understanding emotions from short texts through a deep neural network ensemble. Neural Comput Appl 35(18):13565\u201313582","journal-title":"Neural Comput Appl"},{"key":"9749_CR17","doi-asserted-by":"crossref","unstructured":"Jiang J, Ye S, Wang W, Xu J, Luo X (2020) Learning effective representations for person-job fit by feature fusion. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management","DOI":"10.1145\/3340531.3412717"},{"key":"9749_CR18","doi-asserted-by":"crossref","unstructured":"Zhenhong J, Lingxi P, Lei S (2021) Person-job fit model based on sentence-level representation and theme-word graph. In: IEEE Advanced Information Tech., Electronic and Automation Control Conf","DOI":"10.1109\/IAEAC50856.2021.9390614"},{"issue":"3","key":"9749_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3234465","volume":"9","author":"C Zhu","year":"2018","unstructured":"Zhu C, Zhu H, Xiong H, Ma C, Xie F, Ding P, Li P (2018) Person-job fit: adapting the right talent for the right job with joint representation learning. ACM Trans Manag Inform Syst 9(3):1\u201317","journal-title":"ACM Trans Manag Inform Syst"},{"key":"9749_CR20","first-page":"1","volume":"2021","author":"X Wang","year":"2021","unstructured":"Wang X, Jiang Z, Peng L (2021) A deep-learning-inspired person-job matching model based on sentence vectors and subject-term graphs. Complexity 2021:1\u201311","journal-title":"Complexity"},{"key":"9749_CR21","doi-asserted-by":"crossref","unstructured":"Hou Y, Pan X, Zhao WX, Bian S, Song Y, Zhang T, Wen JR (2022) Leveraging search history for improving person-job fit. In: International Conference on Database Systems for Advanced Applications. Cham: Springer International Publishing","DOI":"10.1007\/978-3-031-00123-9_3"},{"key":"9749_CR22","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.neucom.2022.06.012","volume":"501","author":"Z Wang","year":"2022","unstructured":"Wang Z, Wei W, Chenwei X, Jun X, Mao X-L (2022) Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks. Neurocomputing 501:14\u201324","journal-title":"Neurocomputing"},{"key":"9749_CR23","doi-asserted-by":"publisher","first-page":"32436","DOI":"10.1109\/ACCESS.2021.3060654","volume":"9","author":"SH Rafi","year":"2021","unstructured":"Rafi SH, Deeba SR, Hossain E (2021) A short-term load forecasting method using integrated cnn and lstm network. IEEE Access 9:32436\u201332448","journal-title":"IEEE Access"},{"issue":"30","key":"9749_CR24","doi-asserted-by":"publisher","first-page":"43229","DOI":"10.1007\/s11042-022-12930-z","volume":"81","author":"P Goel","year":"2022","unstructured":"Goel P, Jain R, Nayyar A, Singhal S, Srivastava M (2022) Sarcasm detection using deep learning and ensemble learning. Multimed Tools Appl 81(30):43229\u201343252","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"9749_CR25","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/TCSS.2021.3134458","volume":"10","author":"M He","year":"2021","unstructured":"He M, Shen D, Wang T, Zhao H, Zhang Z, He R (2021) Self-Attentional multi-field features representation and interaction learning for Person-Job Fit. IEEE Trans Comput Soc Syst 10(1):255\u2013268","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"9749_CR26","doi-asserted-by":"crossref","unstructured":"Roy PK, Singh SK, Das TK, Tripathy AK (2022) Automated resume classification using machine learning. In: Advances in distributed computing and machine learning: proceedings of ICADCML 2022. Singapore: Springer Nature Singapore. pp 307-316","DOI":"10.1007\/978-981-19-1018-0_26"},{"key":"9749_CR27","doi-asserted-by":"crossref","unstructured":"Hosseinpour S, Shakibian H (2023) An ensemble learning approach for sms spam detection. In: 2023 9th International Conference on Web Research (ICWR). IEEE,","DOI":"10.1109\/ICWR57742.2023.10139070"},{"issue":"5","key":"9749_CR28","doi-asserted-by":"publisher","first-page":"408","DOI":"10.3390\/f10050408","volume":"10","author":"L Gigovic","year":"2019","unstructured":"Gigovic L, Pourghasemi HR, Drobnjak S, Bai S (2019) Testing a new ensemble model based on svm and random forest in forest fire susceptibility assessment and its mapping in serbias tara national park. Forests 10(5):408","journal-title":"Forests"},{"issue":"29","key":"9749_CR29","first-page":"141","volume":"9","author":"JK Tan","year":"2017","unstructured":"Tan JK, Lee NK, Bong CH, Sofian SA (2017) Identification of personality traits for recruitment of unskilled occupations using Kansei engineering method. J Telecommun Electron Comput Eng 9(29):141\u2013146","journal-title":"J Telecommun Electron Comput Eng"},{"key":"9749_CR30","doi-asserted-by":"crossref","unstructured":"Nigam A, Roy A, Singh H, Waila H (2019) Job recommendation through progression of job selection. In: 2019 IEEE 6th international conference on cloud computing and intelligence systems (ccis). IEEE. pp 212\u2013216","DOI":"10.1109\/CCIS48116.2019.9073723"},{"key":"9749_CR31","doi-asserted-by":"crossref","unstructured":"Diaby M, Viennet E, Launay T (2013) Toward the next generation of recruitment tools: an online social network-based job recommender system. In: Proceedings of the 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining. pp 821\u2013828","DOI":"10.1145\/2492517.2500266"},{"key":"9749_CR32","doi-asserted-by":"crossref","unstructured":"Qin C, Zhu H, Xu T, Zhu C, Jiang L, Chen E, Xiong H (2018) Enhancing person-job fit for talent recruitment: An ability-aware neural network approach. In: The 41st international ACM SIGIR conference on research & development in information retrieval. pp 25\u201334","DOI":"10.1145\/3209978.3210025"},{"issue":"2","key":"9749_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3376927","volume":"38","author":"C Qin","year":"2020","unstructured":"Qin C, Zhu H, Xu T, Zhu C, Ma C, Chen E, Xiong H (2020) An enhanced neural network approach to person-job fit in talent recruitment. ACM Trans Inform Syst 38(2):1\u201333","journal-title":"ACM Trans Inform Syst"},{"key":"9749_CR34","doi-asserted-by":"crossref","unstructured":"Mujtaba DF, Mahapatra NR (2020) Mining and analyzing occupational characteristics from job postings. In: 2020 International Conference on Computational Science and Computational Intelligence (CSCI) pp 683\u2013685. IEEE","DOI":"10.1109\/CSCI51800.2020.00124"},{"issue":"4","key":"9749_CR35","doi-asserted-by":"publisher","first-page":"311","DOI":"10.22452\/mjcs.vol31no4.5","volume":"31","author":"TL Ting","year":"2018","unstructured":"Ting TL, Varathan KD (2018) Job recommendation using Facebook personality scores. Malays J Comput Sci 31(4):311\u2013331","journal-title":"Malays J Comput Sci"},{"key":"9749_CR36","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.procs.2016.09.349","volume":"95","author":"MF Uddin","year":"2016","unstructured":"Uddin MF, Lee J (2016) Utilizing relevant academic and personality features from big unstructured data to identify good and bad fit students. Proc Comput Sci 95:383\u2013391","journal-title":"Proc Comput Sci"},{"issue":"1","key":"9749_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-020-01522-8","volume":"63","author":"Mauricio Noris Freire and Leandro Nunes de Castro","year":"2021","unstructured":"Mauricio Noris Freire and Leandro Nunes de Castro (2021) e-recruitment recommender systems: a systematic review. Knowl Inf Syst 63(1):1\u201320","journal-title":"Knowl Inf Syst"},{"key":"9749_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106463","volume":"144","author":"D Lee","year":"2020","unstructured":"Lee D, Ahn C (2020) Industrial human resource management optimization based on skills and characteristics. Comput Ind Eng 144:106463","journal-title":"Comput Ind Eng"},{"key":"9749_CR39","doi-asserted-by":"publisher","first-page":"214039","DOI":"10.1109\/ACCESS.2020.3040338","volume":"8","author":"M Qamhieh","year":"2020","unstructured":"Qamhieh M, Sammaneh H, Demaidi MN (2020) PCRS: personalized career-path recommender system for engineering students. IEEE Access 8:214039\u2013214049","journal-title":"IEEE Access"},{"key":"9749_CR40","doi-asserted-by":"crossref","unstructured":"Razak TR, Hashim MA, Noor NM, Abd Halim IH, Shamsul NFF (2014) Career path recommendation system for UiTM Perlis students using fuzzy logic. In: 2014 5th international conference on intelligent and advanced systems (ICIAS). IEEE. pp 1\u20135","DOI":"10.1109\/ICIAS.2014.6869553"},{"key":"9749_CR41","doi-asserted-by":"crossref","unstructured":"Choudhary RS, Kukreja R, Jain N, Jain S (2014) Personality and education mining based job advisory system","DOI":"10.9781\/ijimai.2014.274"},{"key":"9749_CR42","doi-asserted-by":"crossref","unstructured":"Kolekar A (2021) Skynet: a platform for maximizing career opportunities. In: Biennial Int. Conf. on Nascent Technologies in Eng","DOI":"10.1109\/ICNTE51185.2021.9487581"},{"key":"9749_CR43","doi-asserted-by":"crossref","unstructured":"Le R, Hu W, Song Y, Zhang T, Zhao D, Yan R (2019) Towards effective and interpretable person-job fitting. In: Proceedings of the 28th ACM international conference on information and knowledge management pp 1883\u20131892","DOI":"10.1145\/3357384.3357949"},{"key":"9749_CR44","doi-asserted-by":"crossref","unstructured":"Pan S, Ding T (2019) Social media-based user embedding: A literature review. arXiv preprint arXiv: 1907.00725,","DOI":"10.24963\/ijcai.2019\/881"},{"issue":"9","key":"9749_CR45","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1109\/TKDE.2012.130","volume":"25","author":"J Bian","year":"2012","unstructured":"Bian J, Dong A, He X, Reddy S, Chang Y (2012) User action interpretation for online content optimization. IEEE Trans Knowl Data Eng 25(9):2161\u20132174","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9749_CR46","doi-asserted-by":"crossref","unstructured":"Yan J, Sun H, Wang X, Liu X, Song X (2018) Profiling developer expertise across software communities with heterogeneous information network analysis. In: Proceedings of the 10th Asia-Pacific Symposium on Internetware. pp 1\u20139","DOI":"10.1145\/3275219.3275226"},{"issue":"4","key":"9749_CR47","first-page":"2747","volume":"16","author":"W Yazi","year":"2019","unstructured":"Yazi W, Huaibo Sun Yu, Zhou ZW, Sifeng Z (2019) A heterogeneous graph embedding framework for location-based social network analysis in smart cities. IEEE Trans. Ind Inform 16(4):2747\u20132755","journal-title":"IEEE Trans. Ind Inform"},{"key":"9749_CR48","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"9749_CR49","doi-asserted-by":"crossref","unstructured":"Guan Z, Wu B, Wang B, Liu H (2020) Personality2vec: Network representation learning for personality. In: 2020 IEEE Fifth international conference on data science in cyberspace (DSC) pp 30\u201337. IEEE","DOI":"10.1109\/DSC50466.2020.00013"},{"issue":"2","key":"9749_CR50","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1007\/s42001-022-00178-4","volume":"5","author":"AR Feizi-Derakhshi","year":"2022","unstructured":"Feizi-Derakhshi AR, Feizi-Derakhshi MR, Ramezani M, Nikzad-Khasmakhi N, Asgari-Chenaghlu M, Akan T, Jahanbakhsh-Naghadeh Z (2022) Text-based automatic personality prediction: a bibliographic review. J Comput Soc Sci 5(2):1555\u20131593","journal-title":"J Comput Soc Sci"},{"issue":"4","key":"9749_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3441302","volume":"15","author":"N Ghasemi","year":"2021","unstructured":"Ghasemi N, Fatourechi R, Momtazi S (2021) User embedding for expert finding in community question answering. ACM Trans Knowl Discov Data 15(4):1\u201316","journal-title":"ACM Trans Knowl Discov Data"},{"key":"9749_CR52","doi-asserted-by":"crossref","unstructured":"Shuster K, Humeau S, Hu H, Bordes A, Weston J (2019) Engaging image captioning via personality. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pp 12516\u201312526","DOI":"10.1109\/CVPR.2019.01280"},{"key":"9749_CR53","volume-title":"Fourier analysis of time series: an introduction","author":"P Bloomfield","year":"2004","unstructured":"Bloomfield P (2004) Fourier analysis of time series: an introduction. John Wiley and Sons, Hoboken"},{"key":"9749_CR54","doi-asserted-by":"publisher","first-page":"6861","DOI":"10.1109\/ACCESS.2019.2963426","volume":"8","author":"S Seo","year":"2020","unstructured":"Seo S, Kim C, Kim H, Mo K, Kang P (2020) Comparative study of deep learning-based sentiment classification. IEEE Access 8:6861\u20136875","journal-title":"IEEE Access"},{"key":"9749_CR55","doi-asserted-by":"publisher","first-page":"22371","DOI":"10.1109\/ACCESS.2021.3054915","volume":"9","author":"Z Hu","year":"2021","unstructured":"Hu Z, Zhang J, Ge Y (2021) Handling vanishing gradient problem using artificial derivative. IEEE Access 9:22371\u201322377","journal-title":"IEEE Access"},{"key":"9749_CR56","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980,"},{"key":"9749_CR57","doi-asserted-by":"crossref","unstructured":"Ponomareva N, Colthurst T, Hendry G, Haykal S, Radpour S (2017) Compact multi-class boosted trees. In: 2017 IEEE International Conference on Big Data (Big Data) pp 47\u201356. IEEE","DOI":"10.1109\/BigData.2017.8257910"},{"key":"9749_CR58","first-page":"77","volume":"18","author":"R Vilalta","year":"2002","unstructured":"Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. AI Rev 18:77\u201395","journal-title":"AI Rev"},{"issue":"9","key":"9749_CR59","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales T, Antoniou A, Micaelli P, Storkey A (2021) Meta-learning in neural networks: a survey. IEEE Trans Pattern Anal Mach Intell 44(9):5149\u20135169","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9749_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v015.i09","volume":"15","author":"A Karatzoglou","year":"2006","unstructured":"Karatzoglou A, Meyer D, Hornik K (2006) Support vector machines in R. J Statist Softw 15:1\u201328","journal-title":"J Statist Softw"},{"key":"9749_CR61","doi-asserted-by":"crossref","unstructured":"Stidham H, Summers J, Shuffler M (2018) Using the five factor model to study personality convergence on student engineering design teams. In: DS 92: Proceedings of the DESIGN 2018 15th International Design Conference pp 2145\u20132154","DOI":"10.21278\/idc.2018.0508"},{"issue":"4","key":"9749_CR62","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1109\/JSYST.2014.2342375","volume":"11","author":"F Xia","year":"2014","unstructured":"Xia F, Asabere NY, Liu H, Chen Z, Wang W (2014) Socially aware conference participant recommendation with personality traits. IEEE Syst J 11(4):2255\u20132266","journal-title":"IEEE Syst J"},{"key":"9749_CR63","doi-asserted-by":"publisher","first-page":"bay120","DOI":"10.1093\/database\/bay120","volume":"2018","author":"F Mehryary","year":"2018","unstructured":"Mehryary F, Bjorne J, Salakoski T, Ginter F (2018) Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction. Database 2018:bay120","journal-title":"Database"},{"key":"9749_CR64","doi-asserted-by":"publisher","first-page":"171013","DOI":"10.1109\/ACCESS.2019.2956342","volume":"7","author":"Y Shao","year":"2019","unstructured":"Shao Y, Wang C (2019) HIBoosting: a recommender system based on a gradient boosting machine. IEEE Access 7:171013\u2013171022","journal-title":"IEEE Access"},{"issue":"11","key":"9749_CR65","doi-asserted-by":"publisher","first-page":"4016","DOI":"10.3390\/app10114016","volume":"10","author":"X Hu","year":"2020","unstructured":"Hu X, Zhang H, Mei H, Xiao D, Li Y, Li M (2020) Landslide susceptibility mapping using the stacking ensemble machine learning method in Lushui Southwest China. Appl Sci 10(11):4016","journal-title":"Appl Sci"},{"key":"9749_CR66","doi-asserted-by":"crossref","unstructured":"Byeon H (2021) Development of a stacking-based ensemble machine learning for detection of depression in Parkinson\u2019s disease","DOI":"10.3390\/ECCM-10857"},{"issue":"22","key":"9749_CR67","doi-asserted-by":"publisher","first-page":"4893","DOI":"10.3390\/s19224893","volume":"19","author":"H Shahabi","year":"2019","unstructured":"Shahabi H, Jarihani B, Tavakkoli\u00a0Piralilou S, Chittleborough D, Avand M, Ghorbanzadeh O (2019) A semi-automated object-based gully networks detection using different machine learning models: a case study of bowen catchment, queensland, australia. Sensors 19(22):4893","journal-title":"Sensors"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09749-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09749-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09749-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:07:01Z","timestamp":1720778821000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09749-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":67,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["9749"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09749-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]},"assertion":[{"value":"18 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","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 no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}