{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:18Z","timestamp":1750219998164,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,22]]},"DOI":"10.1145\/3548785.3548796","type":"proceedings-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T16:08:13Z","timestamp":1663085293000},"page":"161-165","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Quality prediction in a smart factory: a real case study"],"prefix":"10.1145","author":[{"given":"Sana","family":"Ben Abdallah Ben Lamine","sequence":"first","affiliation":[{"name":"Riadi Laboratory ENSI \/ ISAMM University of Manouba, University of Manouba, Tunisia"}]},{"given":"Malek","family":"Kamoua","sequence":"additional","affiliation":[{"name":"Riadi Laboratory ENSI \/ ISAMM University of Manouba, University of Manouba, Tunisia"}]},{"given":"Haythem","family":"Grioui","sequence":"additional","affiliation":[{"name":"ADDIXO, France"}]}],"member":"320","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[1] Productivity and performance improvement from industry 4.0 adoption IndonesiaStatista Research Department: (2019)"},{"key":"e_1_3_2_1_2_1","unstructured":"[2] Jacob D Quality 4.0 impact and strategy handbookLNS Research MaterControl (2017)"},{"key":"e_1_3_2_1_3_1","unstructured":"[3] Chunquan Li Yaqiong Chen Yuling Shan A review of industrial big data for decision making in intelligent manufacturingEngineering Science and Technology an International Journal (2021)"},{"key":"e_1_3_2_1_4_1","unstructured":"[4] Gian Antonio Susto Andrea Schirru Simone Pampuri Se\u00e1n McLoone Machine learning for predictive maintenance: A multiple classifier approachIEEE Transactions on Industrial Informatics (2015)"},{"key":"e_1_3_2_1_5_1","volume-title":"Weiming Shen A sensor fusion and support vector machine based approach for recognition of complex machining conditionsJournal of Intelligent Manufacturing","author":"Liu Changqing","year":"2018","unstructured":"[5] Changqing Liu, Yingguang Li, Guanyan Zhou, Weiming Shen A sensor fusion and support vector machine based approach for recognition of complex machining conditionsJournal of Intelligent Manufacturing, Springer (2018)"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"[6] Yang Guo Zhenyu Wu Yang Ji A hybrid deep representation learning model for time series classification and prediction3rd International Conference on Big Data Computing and Communications (BIGCOM) (2017)","DOI":"10.1109\/BIGCOM.2017.13"},{"key":"e_1_3_2_1_7_1","unstructured":"[7] David Gyulai Andras Pfeiffer Gabor Nick Viola Gallina A hybrid deep representation learning model for time series classification and prediction3rd International Conference on Big Data Computing and Communications (BIGCOM) (2017)"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"[8] Jianjing Zhang Peng Wang Ruqiang Yan Robert X. Gao Long short-term memory for machine remaining life predictionJournal of Manufacturing Systems (2017)","DOI":"10.1016\/j.jmsy.2018.05.011"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"[9] Johannes Futterer Maksymilia nKochanski Dirk Muller Application of selected supervised learning methods for time series classification in Building Automation and Control SystemsInternational ConferenceFuture Buildings & Districts \u2013 Energy Efficiency from Nano to Urban Scale (2017)","DOI":"10.1016\/j.egypro.2017.07.428"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"[10] Chen L Xu G Zhang S Yan W Wu Q Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networksJournal of Manufacturing Systems (2020)","DOI":"10.1016\/j.jmsy.2019.11.008"},{"key":"e_1_3_2_1_11_1","unstructured":"[11] Stu Johnson Quality 4.0: a trend within a trendQuality magazine (2019)"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"[12] Andrew V. Metcalfe Paul S.P. Cowpertwait Introductory Time Series with RSpringer (2009)","DOI":"10.1007\/978-0-387-88698-5"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"[13] Giuseppe Schlitzer Testing the stationarity of economic time series: further Monte Carlo evidenceRicerche Economiche (1995)","DOI":"10.1016\/0035-5054(95)90019-5"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"[14] Ismael Ramos-P\u00e9rez \u00c1lvar Arnaiz-Gonz\u00e1lez Juan J. Rodr\u00edguez C\u00e9sar Garc\u00eda-Osorio When is resampling beneficial for feature selection with imbalanced wide dataExpert Systems with Applications (2021)","DOI":"10.1016\/j.eswa.2021.116015"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"[15] Bommert A Sun X Bischl B Rahnenf\u00fchrer J Lang M Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics and Data AnalysisComputational Statistics and Data Analysis (2020)","DOI":"10.1016\/j.csda.2019.106839"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"[16] Kohavi R John G. H. Wrappers for feature subset selection. Artificial Intelligence Artificial Intelligence (1997)","DOI":"10.1016\/S0004-3702(97)00043-X"},{"volume-title":"Kremer An accurate, fast embedded feature selection for SVMs 13th International Conference on Machine Learning and Applications (2014)","author":"Hamed Tarfa","key":"e_1_3_2_1_17_1","unstructured":"[17] Tarfa Hamed, Rozita Dara, Stefan C. Kremer An accurate, fast embedded feature selection for SVMs 13th International Conference on Machine Learning and Applications (2014)"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_19_1","unstructured":"[19] Ferri J.V. Albert E. Vidal Considerations about sample-size sensitivity of a family of edited nearest-neighbor rules IEEE Trans Syst Man Cybern B Cybern (1997)"},{"key":"e_1_3_2_1_20_1","unstructured":"[20] I. Tomek Two modifications of CNN IEEE Transactions on Systems Man and Cybernetics (1976)"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"[21] Zohair Malki El-Sayed Atlam Ashraf Ewis ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound Neural Computing and Applications (2020)","DOI":"10.21203\/rs.3.rs-34702\/v1"},{"key":"e_1_3_2_1_22_1","unstructured":"[22] How to Convert a Time Series to a Supervised Learning Problem in Python machinelearningmastery.com"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"[23] Grace W. Lindsay Attention in Psychology Neuroscience and Machine Learning. Front. Comput. Neurosci (2020)","DOI":"10.3389\/fncom.2020.00029"},{"volume-title":"Illia Polosukhin Attention Is All You Need NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems (2017)","author":"Vaswani Ashish","key":"e_1_3_2_1_24_1","unstructured":"[24] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin Attention Is All You Need NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems (2017)"},{"volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)","author":"Ribeiro Marco Tulio","key":"e_1_3_2_1_25_1","unstructured":"[25] Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin Why Should I Trust You?\u201d: Explaining the Predictions of Any ClassifierKDD \u201916: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)"},{"key":"e_1_3_2_1_26_1","unstructured":"[26] James Bergstra Yoshua Bengio Random Search for Hyper-Parameter Optimization Journal of Machine Learning Research (2012)"},{"key":"e_1_3_2_1_27_1","unstructured":"[27] Charles X. Ling Jin Huang Harry Zhang AUC: A Better Measure than Accuracy in Comparing Learning Algorithms Conference of the Canadian Society for Computational Studies of Intelligence (2003)"}],"event":{"name":"IDEAS'22: International Database Engineered Applications Symposium","acronym":"IDEAS'22","location":"Budapest Hungary"},"container-title":["Proceedings of the 26th International Database Engineered Applications Symposium"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548785.3548796","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548785.3548796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:50:53Z","timestamp":1750182653000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548785.3548796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,22]]},"references-count":27,"alternative-id":["10.1145\/3548785.3548796","10.1145\/3548785"],"URL":"https:\/\/doi.org\/10.1145\/3548785.3548796","relation":{},"subject":[],"published":{"date-parts":[[2022,8,22]]},"assertion":[{"value":"2022-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}