{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:39:28Z","timestamp":1747190368475,"version":"3.40.5"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,11,12]]},"abstract":"<jats:p>Aiming at the problems of low prediction accuracy and efficiency and poor prediction effect in the current psychological pressure prediction methods, a psychological pressure prediction method for college students based on deep neural network is proposed. The structure and algorithm of depth neural network and gray theory model are analyzed. Using the deep neural network, this paper establishes the sample set data of college students\u2019 psychological pressure prediction and constructs the college students\u2019 psychological pressure prediction model combined with the deep neural network algorithm of gray theory. The physical network information model is formed through the relationship between neurons. According to the dynamic changes of college students\u2019 psychological pressure in each neuron of the physical network, the prediction of college students\u2019 psychological pressure is completed. The experimental results show that the proposed method is effective in predicting college students\u2019 psychological pressure and can effectively improve the accuracy and efficiency of college students\u2019 psychological pressure prediction.<\/jats:p>","DOI":"10.1155\/2021\/2943678","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T02:05:07Z","timestamp":1636769107000},"page":"1-9","source":"Crossref","is-referenced-by-count":2,"title":["Prediction Method of College Students\u2019 Psychological Pressure Based on Deep Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation, Northeast Forestry University, Haerbin 150000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7355-1549","authenticated-orcid":true,"given":"Sitong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Northeast Forestry University, Haerbin 150000, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s12144-021-01450-y"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1791\/1\/012063"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.25136\/2409-8701.2020.5.33806"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1002\/hec.4256"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000015576"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10902-018-0033-9"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1515\/ijamh-2018-0221"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212844"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-5246-0"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01165"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.12.004"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-019-03081-4"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106580"},{"key":"14","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TIP.2020.3042083","article-title":"Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity","volume":"30","author":"C. Zhang","year":"2020","journal-title":"IEEE Transactions on Image Processing"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2021.3055617"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19354-z"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2021.3087946"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108125"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02054-y"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1142\/s0217732320502570"},{"issue":"18","key":"21","first-page":"854","article-title":"An accuracy controlled iterative method for efficient sigmoid function approximation","volume":"56","author":"M. Tripathy","year":"2020","journal-title":"Electronics Letters"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2021.167631"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2917234"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2021.104724"},{"issue":"01","key":"25","first-page":"452","article-title":"Research on image interframe compensation based on deep convolutional neural network","volume":"37","author":"S. Yang","year":"2020","journal-title":"Computer Simulation"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02503-2"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.04.115"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.135447"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110968"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.04.021"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.01.028"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.07.111"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28148"},{"key":"34","article-title":"A theoretical analysis of deep neural networks and parametric PDEs","volume":"304","author":"G. Kutyniok","year":"2021","journal-title":"Constructive Approximation"},{"issue":"6","key":"35","first-page":"02428","article-title":"Depth-adaptive neural networks from the optimal control viewpoint","volume":"5","author":"J. Aghili","year":"2020","journal-title":"ArXiv Preprint"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2020.104696"}],"container-title":["Scientific Programming"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/2943678.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/2943678.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/sp\/2021\/2943678.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T02:05:15Z","timestamp":1636769115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/sp\/2021\/2943678\/"}},"subtitle":[],"editor":[{"given":"M","family":"Pallikonda Rajasekaran","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":36,"alternative-id":["2943678","2943678"],"URL":"https:\/\/doi.org\/10.1155\/2021\/2943678","relation":{},"ISSN":["1875-919X","1058-9244"],"issn-type":[{"type":"electronic","value":"1875-919X"},{"type":"print","value":"1058-9244"}],"subject":[],"published":{"date-parts":[[2021,11,12]]}}}