{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:05:14Z","timestamp":1744344314158,"version":"3.40.4"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819646050"},{"type":"electronic","value":"9789819646067"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-4606-7_20","type":"book-chapter","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T20:54:15Z","timestamp":1744145655000},"page":"237-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhance Air Quality Index Prediction with\u00a0Inception Time - BiLSTM Model and\u00a0Huber Loss"],"prefix":"10.1007","author":[{"given":"Hao","family":"Do","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"20_CR1","unstructured":"W.\u00a0H. Organization, \u201cAir pollution impact.\u201d https:\/\/www.who.int\/health-topics\/air-pollution. Accessed 2024"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the arima model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp.\u00a0106\u2013112. IEEE (2014)","DOI":"10.1109\/UKSim.2014.67"},{"key":"20_CR3","doi-asserted-by":"crossref","first-page":"100093","DOI":"10.1016\/j.dche.2023.100093","volume":"7","author":"NN Maltare","year":"2023","unstructured":"Maltare, N.N., Vahora, S.: Air quality index prediction using machine learning for Ahmedabad city. Digit. Chem. Eng. 7, 100093 (2023)","journal-title":"Digit. Chem. Eng."},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Gupta, N.S., Mohta, Y., Heda, K., Armaan, R., Valarmathi, B., Arulkumaran, G.: Prediction of air quality index using machine learning techniques: a comparative analysis. J. Environ. Public Health 2023(1), 4916267 (2023)","DOI":"10.1155\/2023\/4916267"},{"key":"20_CR5","first-page":"791","volume":"81","author":"D Saravanan","year":"2023","unstructured":"Saravanan, D., Kumar, K.S.: Improving air pollution detection accuracy and quality monitoring based on bidirectional rnn and the internet of things. Mater. Today: Proc. 81, 791\u2013796 (2023)","journal-title":"Mater. Today: Proc."},{"issue":"1","key":"20_CR6","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1038\/s41598-023-28287-8","volume":"13","author":"Z Zhao","year":"2023","unstructured":"Zhao, Z., Wu, J., Cai, F., Zhang, S., Wang, Y.-G.: A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the covid-19 pandemic. Sci. Rep. 13(1), 1015 (2023)","journal-title":"Sci. Rep."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Ismail Fawaz, H., et al.: Inceptiontime: Finding alexnet for time series classification. Data Min. Knowl. Disc. 34(6), 1936\u20131962 (2020)","DOI":"10.1007\/s10618-020-00710-y"},{"key":"20_CR8","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional lstm-crf models for sequence tagging, arXiv preprintarXiv:1508.01991 (2015)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Huber, P.J.: Robust estimation of a location parameter. In: Breakthroughs in Statistics: Methodology and Distribution, pp.\u00a0492\u2013518. Springer (1992)","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Sun, G.S.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a07132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"20_CR11","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.procs.2020.03.240","volume":"167","author":"R Kumar","year":"2020","unstructured":"Kumar, R., Kumar, Y.: Time series data prediction using IoT and machine learning technique. Procedia Comput. Sci. 167, 373\u2013381 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp.\u00a0929\u2013934. IEEE (2015)","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.177"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Sharma, M., Jain, S., Mittal, S., Sheikh, T.H.: Forecasting and prediction of air pollutants concentrates using machine learning techniques: the case of india. In: IOP conference series: Materials science and engineering, vol.\u00a01022, p.\u00a0012123, IOP Publishing (2021)","DOI":"10.1088\/1757-899X\/1022\/1\/012123"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Sanober, S., Usha\u00a0Rani, K.: A substantial approach to predict air quality using lvq neural network. In: Proceedings of the 2nd International Conference on Computational and Bio Engineering: CBE 2020, pp.\u00a0523\u2013532. Springer (2021)","DOI":"10.1007\/978-981-16-1941-0_52"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, A., Jamatia, A.: Prediction of air quality using machine learning. In: International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp.\u00a0199\u2013209. Springer (2022)","DOI":"10.1007\/978-981-19-7513-4_18"},{"key":"20_CR16","doi-asserted-by":"crossref","first-page":"135771","DOI":"10.1016\/j.scitotenv.2019.135771","volume":"705","author":"J Ma","year":"2020","unstructured":"Ma, J., Li, Z., Cheng, J.C., Ding, Y., Lin, C., Xu, Z.: Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network. Sci. Total Environ. 705, 135771 (2020)","journal-title":"Sci. Total Environ."},{"key":"20_CR17","doi-asserted-by":"crossref","first-page":"120404","DOI":"10.1016\/j.envpol.2022.120404","volume":"315","author":"N Sarkar","year":"2022","unstructured":"Sarkar, N., Gupta, R., Keserwani, P.K., Govil, M.C.: Air quality index prediction using an effective hybrid deep learning model. Environ. Pollut. 315, 120404 (2022)","journal-title":"Environ. Pollut."},{"issue":"1","key":"20_CR18","doi-asserted-by":"crossref","first-page":"8373","DOI":"10.1038\/s41598-022-12355-6","volume":"12","author":"J Wang","year":"2022","unstructured":"Wang, J., Li, X., Jin, L., Li, J., Sun, Q., Wang, H.: An air quality index prediction model based on cnn-ilstm. Sci. Rep. 12(1), 8373 (2022)","journal-title":"Sci. Rep."},{"issue":"9","key":"20_CR19","doi-asserted-by":"crossref","first-page":"7367","DOI":"10.3390\/su15097367","volume":"15","author":"Y Shu","year":"2023","unstructured":"Shu, Y., Ding, C., Tao, L., Hu, C., Tie, Z.: Air pollution prediction based on discrete wavelets and deep learning. Sustainability 15(9), 7367 (2023)","journal-title":"Sustainability"},{"key":"20_CR20","unstructured":"C.\u00a0P.\u00a0C. Board, Air quality data in india (2015 - 2020). https:\/\/www.kaggle.com\/datasets\/rohanrao\/air-quality-data-in-india. Accessed 2024"},{"key":"20_CR21","unstructured":"E.\u00a0Y. R.\u00a0T. Environmental Protection\u00a0Administration, Air quality in northern taiwan. https:\/\/www.kaggle.com\/datasets\/nelsonchu\/air-quality-in-northern-taiwan. Accessed 2024"},{"issue":"4","key":"20_CR22","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1109\/JSEN.2022.3230361","volume":"23","author":"Y Wei","year":"2023","unstructured":"Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., Boulic, M.: Lstm-autoencoder-based anomaly detection for indoor air quality time-series data. IEEE Sens. J. 23(4), 3787\u20133800 (2023)","journal-title":"IEEE Sens. J."},{"key":"20_CR23","doi-asserted-by":"crossref","first-page":"136180","DOI":"10.1016\/j.chemosphere.2022.136180","volume":"308","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Li, S.: Air quality index forecast in Beijing based on cnn-lstm multi-model. Chemosphere 308, 136180 (2022)","journal-title":"Chemosphere"}],"container-title":["Lecture Notes in Computer Science","Integrated Uncertainty in Knowledge Modelling and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-4606-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T09:29:10Z","timestamp":1744277350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-4606-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819646050","9789819646067"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-4606-7_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IUKM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 March 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 March 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iukm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.jaist.ac.jp\/IUKM\/IUKM2025\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}