{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:58:55Z","timestamp":1777705135472,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,5]]},"abstract":"<jats:p>As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.<\/jats:p>","DOI":"10.3233\/jifs-220246","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T11:21:31Z","timestamp":1666092091000},"page":"1337-1348","source":"Crossref","is-referenced-by-count":1,"title":["The safety evaluation method of steel enterprises based on deep learning"],"prefix":"10.1177","volume":"44","author":[{"given":"Jingmin","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Beiye Functional Materials Corporation, Beijing, China"}]},{"given":"Shuzhen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai, Shandong, China"}]}],"member":"179","reference":[{"issue":"7553","key":"10.3233\/JIFS-220246_ref4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"2","key":"10.3233\/JIFS-220246_ref5","first-page":"53","article-title":"Major theories of constructionaccident causation models: A literature review","volume":"4","author":"Hosseinian","year":"2012","journal-title":"InternationalJournal of Advances in Engineering & Technology"},{"issue":"2","key":"10.3233\/JIFS-220246_ref6","first-page":"173","article-title":"Models of accident causation and theirapplication: Review and reappraisal","volume":"8","author":"Lehto","year":"1991","journal-title":"Journal of Engineering andTechnology Management"},{"key":"10.3233\/JIFS-220246_ref7","doi-asserted-by":"crossref","first-page":"107363","DOI":"10.1016\/j.ress.2020.107363","article-title":"An accidentcausation model based on safety information cognition and itsapplication","volume":"207","author":"Chen","year":"2021","journal-title":"Reliability Engineering & System Safety"},{"issue":"4","key":"10.3233\/JIFS-220246_ref8","first-page":"131","article-title":"The risks facing chinas mining companies-an analysisfrom global perspective1","volume":"6","author":"Chunyan","year":"2012","journal-title":"International Journal of Security andits Applications"},{"issue":"1","key":"10.3233\/JIFS-220246_ref9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3390\/en12010161","article-title":"Research on gasconcentration prediction models based on lstm multidimensional timeseries","volume":"12","author":"Zhang","year":"2019","journal-title":"Energies"},{"issue":"10","key":"10.3233\/JIFS-220246_ref10","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"Lstm: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE transactionson neural networks and learning systems"},{"issue":"6","key":"10.3233\/JIFS-220246_ref12","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TGRS.2020.3010541","article-title":"Aenet: Automatic pickingof p-wave first arrivals using deep learning","volume":"59","author":"Guo","year":"2020","journal-title":"IEEE Transactionson Geoscience and Remote Sensing"},{"issue":"7","key":"10.3233\/JIFS-220246_ref17","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1061\/(ASCE)0733-9364(2005)131:7(816)","article-title":"Systems model ofconstruction accident causation","volume":"131","author":"Mitropoulos","year":"2005","journal-title":"Journal of constructionengineering and management"},{"issue":"4","key":"10.3233\/JIFS-220246_ref18","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1061\/(ASCE)0733-9364(2001)127:4(337)","article-title":"Development of causal modelof construction accident causation","volume":"127","author":"Suraji","year":"2001","journal-title":"Journal of constructionengineering and management"},{"issue":"7","key":"10.3233\/JIFS-220246_ref19","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.ssci.2008.11.002","article-title":"Towards anevaluation of accident investigation methods in terms of theiralignment with accident causation models","volume":"47","author":"Katsakiori","year":"2009","journal-title":"Safety Science"},{"issue":"4-5","key":"10.3233\/JIFS-220246_ref23","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/S0375-9601(00)00725-8","article-title":"Extended tanh-function method and its applications tononlinear equations","volume":"277","author":"Fan","year":"2000","journal-title":"Physics Letters A"},{"issue":"6","key":"10.3233\/JIFS-220246_ref24","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1088\/0031-8949\/54\/6\/003","article-title":"The tanh method: I. exact solutions ofnonlinear evolution and wave equations","volume":"54","author":"Malfliet","year":"1996","journal-title":"Physica Scripta"},{"issue":"10","key":"10.3233\/JIFS-220246_ref26","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/math7100992","article-title":"Universal function approximation by deep neural nets withbounded width and relu activations","volume":"7","author":"Hanin","year":"2019","journal-title":"Mathematics"},{"key":"10.3233\/JIFS-220246_ref32","doi-asserted-by":"crossref","first-page":"120297","DOI":"10.1109\/ACCESS.2021.3108972","article-title":"Rolling element fault diagnosis basedon vmd and sensitivity mckd","volume":"9","author":"Cui","year":"2021","journal-title":"IEEE Access"},{"issue":"5","key":"10.3233\/JIFS-220246_ref34","doi-asserted-by":"crossref","first-page":"4404","DOI":"10.1109\/TIE.2020.2984443","article-title":"Deep learning withspatiotemporal attention-based lstm for industrial soft sensor modeldevelopment","volume":"68","author":"Yuan","year":"2020","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.3233\/JIFS-220246_ref35","doi-asserted-by":"crossref","first-page":"012177","DOI":"10.1088\/1757-899X\/451\/1\/012177","article-title":"Analysis and evaluation oftechnogenic risk of technological equipment of metallurgicalenterprises","volume":"451","author":"Izvekov","year":"2018","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"issue":"2","key":"10.3233\/JIFS-220246_ref36","doi-asserted-by":"crossref","first-page":"313","DOI":"10.46519\/ij3dptdi.958712","article-title":"Risk perception and data mining in theiron and steel industry","volume":"5","author":"Ers\u00f6z","year":"2021","journal-title":"International Journal of 3D PrintingTechnologies and Digital Industry"},{"key":"10.3233\/JIFS-220246_ref37","first-page":"71","article-title":"Risk assessment and controlmeasures for chemical hazards in stainless steel industry a review","volume":"5","author":"Nitin","year":"2015","journal-title":"Int. Rev. Bus. Res. Pap"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-220246","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:59Z","timestamp":1777455779000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-220246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,5]]},"references-count":19,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-220246","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,5]]}}}