{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:09:44Z","timestamp":1753884584073,"version":"3.41.2"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Ltd","issue":"Supp02","funder":[{"DOI":"10.13039\/501100011447","name":"science and technology department of henan province","doi-asserted-by":"publisher","award":["172400410124"],"award-info":[{"award-number":["172400410124"]}],"id":[{"id":"10.13039\/501100011447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Inter. Net."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p> In order to solve the problem that the evaluation algorithm is easy to fall into local extremum, which leads to slow convergence speed, a skilled talent quality evaluation algorithm based on a deep belief network model was designed. Establish an evaluation set with 4 first level indicators and 14 second level indicators, and calculate the corresponding weights to complete the construction of the evaluation index system. A DBN structure composed of several RBMs and a BP network is constructed. Based on the DBN, a quality evaluation algorithm is designed. The algorithm training is used to evaluate the test data and output the evaluation level. The experimental results show that the convergence speed of DBN based evaluation algorithm is significantly better than that of BP neural network and SVM based evaluation algorithm under the same number of iterations, which is suitable for the accurate evaluation of talent quality. <\/jats:p>","DOI":"10.1142\/s0219265921430350","type":"journal-article","created":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T03:13:04Z","timestamp":1641611584000},"source":"Crossref","is-referenced-by-count":1,"title":["Evaluation Algorithm of Skilled Talents\u2019 Quality Based on Deep Belief Network Model"],"prefix":"10.1142","volume":"22","author":[{"given":"Jiang","family":"Hua","sequence":"first","affiliation":[{"name":"College of Information Engineering, Jiaozuo University, Jiaozuo 454000, P. R. China"}]},{"given":"Sun","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Jiaozuo Technician College, Jiaozuo 454003, P. R. 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