{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:07:06Z","timestamp":1766268426814,"version":"3.41.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:00:00Z","timestamp":1633392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["CNS-0435060, CCR-0325197, and EN-CS-0329609"],"award-info":[{"award-number":["CNS-0435060, CCR-0325197, and EN-CS-0329609"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61902203"],"award-info":[{"award-number":["61902203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research and Development Plan \u2013 Major Scientific and Technological Innovation Projects of ShanDong Province","award":["2019JZZY020101"],"award-info":[{"award-number":["2019JZZY020101"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            Two-dimensional\n            <jats:sup>1<\/jats:sup>\n            arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements.\n          <\/jats:p>\n          <jats:p>Scientific information technology has been developed rapidly. Here, the purposes are to make people's lives more convenient and ensure information management and classification. The machine learning algorithm is improved to obtain the optimized Light Gradient Boosting Machine (LightGBM) algorithm. Then, an Android-based intelligent support information management system is designed based on LightGBM for the big data analysis and classification management of information in the intelligent support information management system. The system is designed with modules of employee registration and login, company announcement notice, attendance and attendance management, self-service, and daily tools with the company as the subject. Furthermore, the performance of the constructed information management system is analyzed through simulations. Results demonstrate that the training time of the optimized LightGBM algorithm can stabilize at about 100s, and the test time can stabilize at 0.68s. Besides, its accuracy rate can reach 89.24%, which is at least 3.6% higher than other machine learning algorithms. Moreover, the acceleration efficiency analysis of each algorithm suggests that the optimized LightGBM algorithm is suitable for processing large amounts of data; its acceleration effect is more apparent, and its acceleration ratio is higher than other algorithms. Hence, the constructed intelligent support information management system can reach a high accuracy while ensuring the error, with apparent acceleration effect. Therefore, this model can provide an experimental reference for information classification and management in various fields.<\/jats:p>","DOI":"10.1145\/3469890","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T18:37:36Z","timestamp":1633459056000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Novel Machine Learning for Big Data Analytics in Intelligent Support Information Management Systems"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2525-3074","authenticated-orcid":false,"given":"Zhihan","family":"Lv","sequence":"first","affiliation":[{"name":"School of Data Science and Software Engineering, Qingdao University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranran","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Data Science and Software Engineering, Qingdao University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailin","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhejiang A &amp; F University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongliang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Data Science and Software Engineering, Qingdao University, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibin","family":"Lv","sequence":"additional","affiliation":[{"name":"North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.3012393"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04040-z"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2833452"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386250"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2529723"},{"key":"e_1_3_2_7_2","first-page":"1","article-title":"Coverage guided differential adversarial testing of deep learning systems","author":"Guo J.","year":"2020","unstructured":"J. Guo, Y. Zhao, Y. Jiang, and H. Song. 2020. Coverage guided differential adversarial testing of deep learning systems. IEEE Transactions on Network Science and Engineering 1\u20131.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110261"},{"key":"e_1_3_2_9_2","first-page":"1","article-title":"Uncertain-driven analytics of sequence data in IoCV environments","author":"Srivastava G.","year":"2020","unstructured":"G. Srivastava, J. C. W. Lin, A. Jolfaei, Y. Li, and Y. Djenouri. 2020. Uncertain-driven analytics of sequence data in IoCV environments. IEEE Transactions on Intelligent Transportation Systems 1\u201312.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"A pre-large weighted-fusion system of sensed high-utility patterns","author":"Srivastava G.","year":"2020","unstructured":"G. Srivastava, J. C. W. Lin, M. Pirouz, Y. Li, and U. Yun. 2020. A pre-large weighted-fusion system of sensed high-utility patterns. IEEE Sensors Journal 1\u201314.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2017.53"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2018.2873783"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800254"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3035440"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696365"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2837692"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2894819"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2981959"},{"key":"e_1_3_2_19_2","first-page":"1","article-title":"Large-scale high-utility sequential pattern analytics in internet of things","author":"Srivastava G.","year":"2020","unstructured":"G. Srivastava, J. C. W. Lin, X. Zhang, and Y. Li. 2020. Large-scale high-utility sequential pattern analytics in internet of things. IEEE Internet of Things Journal 1\u201310.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900107"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2706978"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2815989"},{"issue":"5","key":"e_1_3_2_23_2","first-page":"4182","article-title":"LSTM-based SQL injection detection method for intelligent transportation system","volume":"68","author":"Li Q.","year":"2019","unstructured":"Q. Li, F. Wang, J. Wang, and W. Li. 2019. LSTM-based SQL injection detection method for intelligent transportation system. IEEE Transactions on Vehicular Technology 68, 5 (2019), 4182\u20134191.","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"e_1_3_2_24_2","first-page":"6048","article-title":"Development of home intelligent fall detection IoT system based on feedback optical flow convolutional neural network","author":"Hsieh Y. Z.","year":"2017","unstructured":"Y. Z. Hsieh and Y. L. Jeng. 2017. Development of home intelligent fall detection IoT system based on feedback optical flow convolutional neural network. IEEE Access 6 (2017), 6048\u20136057.","journal-title":"IEEE Access"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2898477"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2791469"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.23919\/SICE48898.2020.9240357"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2740206"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2848470"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2946791"},{"key":"e_1_3_2_31_2","first-page":"1","article-title":"CerebelluMorphic: Large-scale neuromorphic model and architecture for supervised motor learning","author":"Yang S.","year":"2021","unstructured":"S. Yang, J. Wang, N. Zhang, B. Deng, Y. Pang, and M. R. Azghadi. 2021. CerebelluMorphic: Large-scale neuromorphic model and architecture for supervised motor learning. IEEE Transactions on Neural Networks and Learning Systems 1\u201315.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_32_2","first-page":"1","article-title":"BiCoSS: Toward large-scale cognition brain with multigranular neuromorphic architecture","author":"Yang S.","year":"2021","unstructured":"S. Yang, J. Wang, X. Hao, H. Li, X. Wei, B. Deng, and K. A. Loparo. 2021. BiCoSS: Toward large-scale cognition brain with multigranular neuromorphic architecture. IEEE Transactions on Neural Networks and Learning Systems 1\u20139.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2899936"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900649"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2805723"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2900460"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2764844"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3469890","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3469890","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3469890","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:16Z","timestamp":1750195696000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3469890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,5]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3,31]]}},"alternative-id":["10.1145\/3469890"],"URL":"https:\/\/doi.org\/10.1145\/3469890","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2021,10,5]]},"assertion":[{"value":"2020-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}