{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:42:34Z","timestamp":1769283754258,"version":"3.49.0"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Province Philosophy and Social Science Program Youth Project","award":["2018CJY035"],"award-info":[{"award-number":["2018CJY035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2022,1,11]]},"abstract":"<jats:p>Aiming at the problems of low accuracy of recognition results, long recognition time, and easy interference in traditional methods, a deep learning-oriented recognition modeling method of college students' psychological stress indicators is proposed. First, the ECG signal is collected by the ECG signal acquisition system, and the wavelet transform method is used to denoise the collected ECG signal. Then, the sequential backward selection algorithm is used to select the features of psychological stress indicators to reduce the feature dimension. Finally, based on the convolutional neural network in deep learning technology, a mental pressure indicator recognition model is established and the model parameters are optimized to realize the recognition of college students\u2019 mental pressure indicators. Experimental results show that the method in this paper has high recognition accuracy, has high recognition efficiency, is not susceptible to interference, and has certain feasibility and effectiveness.<\/jats:p>","DOI":"10.1155\/2022\/6048088","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:20:09Z","timestamp":1641936009000},"page":"1-9","source":"Crossref","is-referenced-by-count":12,"title":["Identification and Modeling of College Students\u2019 Psychological Stress Indicators for Deep Learning"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7563-5797","authenticated-orcid":true,"given":"Yuan","family":"Tian","sequence":"first","affiliation":[{"name":"School of Education, Xinyang University, Xinyang, Henan 4640, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1504\/ijris.2020.10029492"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9979770"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.nepr.2021.103163"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1589\/jpts.31.17"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3390\/su13020896"},{"issue":"3","key":"6","first-page":"611","article-title":"Mental stress assessment approach based on smartphone sensing data","volume":"56","author":"F. Wang","year":"2019","journal-title":"Journal of Computer Research and Development"},{"issue":"2","key":"7","first-page":"166","article-title":"The moderating effect of emotional regulation strategies in the relationship between emotional experiences and physical health of college students","volume":"28","author":"H. F. Liu","year":"2019","journal-title":"Chinese Journal of Behavioral Medicine and Brain Science"},{"issue":"4","key":"8","first-page":"6","article-title":"An improved particle swarm optimization back propogation neural network algorithm for psychological stress identification","volume":"20","author":"Y. Shang","year":"2020","journal-title":"Science Technology and Engineering, Science Technology and Engineering"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3724\/sp.j.1042.2021.00571"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-191518"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.3390\/s19071731"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.3390\/s19102350"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8811962"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3531-0"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.3390\/s20092537"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109178"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s13351-020-9036-7"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1007\/s11242-018-1219-7"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3131-4"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.3390\/s21062006"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.3390\/su13095304"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-179956"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05745-w"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9985185"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.3390\/rs12091379"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.3390\/rs12091403"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3390\/s21124223"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1117\/1.oe.59.5.051407"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-189058"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.3390\/s21010307"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1063\/5.0033376"},{"issue":"1","key":"32","first-page":"387","article-title":"Patient examination demand forecasting based on lasso and tabu search[J]","volume":"38","author":"Y. Qing","year":"2021","journal-title":"Computer Simulation"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.14704\/nq.2020.18.7.nq20185"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9976939"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.9b06298"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.3390\/s21041036"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2022\/6048088.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2022\/6048088.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2022\/6048088.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:20:16Z","timestamp":1641936016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2022\/6048088\/"}},"subtitle":[],"editor":[{"given":"Baiyuan","family":"Ding","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":36,"alternative-id":["6048088","6048088"],"URL":"https:\/\/doi.org\/10.1155\/2022\/6048088","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"value":"1875-919X","type":"electronic"},{"value":"1058-9244","type":"print"}],"subject":[],"published":{"date-parts":[[2022,1,11]]}}}