{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T08:30:15Z","timestamp":1746088215865,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031777301"},{"type":"electronic","value":"9783031777318"}],"license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"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\/978-3-031-77731-8_30","type":"book-chapter","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:42:59Z","timestamp":1732034579000},"page":"325-336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Employing Explainable AI Techniques for\u00a0Air Pollution: An Ante-Hoc and\u00a0Post-Hoc Approach in\u00a0Dioxide Nitrogen Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7143-5413","authenticated-orcid":false,"given":"Pedro","family":"Oliveira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1542-8735","authenticated-orcid":false,"given":"Francisco","family":"Franco","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0348-0713","authenticated-orcid":false,"given":"Afonso","family":"Bessa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8313-7023","authenticated-orcid":false,"given":"Dalila","family":"Dur\u00e3es","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3549-0754","authenticated-orcid":false,"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"issue":"1","key":"30_CR1","doi-asserted-by":"publisher","first-page":"6596397","DOI":"10.1155\/2022\/6596397","volume":"2022","author":"DM Ahmed","year":"2022","unstructured":"Ahmed, D.M., Hassan, M.M., Mstafa, R.J.: A review on deep sequential models for forecasting time series data. Appl. Comput. Intell. Soft Comput. 2022(1), 6596397 (2022). https:\/\/doi.org\/10.1155\/2022\/6596397","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"172195","DOI":"10.1016\/j.scitotenv.2024.172195","volume":"929","author":"N Bacanin","year":"2024","unstructured":"Bacanin, N., et al.: The explainable potential of coupling hybridized metaheuristics, XGBoost, and shap in revealing toluene behavior in the atmosphere. Sci. Total Environ. 929, 172195 (2024)","journal-title":"Sci. Total Environ."},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Chamola, V., Hassija, V., Sulthana, A.R., Ghosh, D., Dhingra, D., Sikdar, B.: A review of trustworthy and explainable artificial intelligence (XAI). IEEE Access (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3294569","DOI":"10.1109\/ACCESS.2023.3294569"},{"issue":"9","key":"30_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/356104","volume":"55","author":"R Dwivedi","year":"2023","unstructured":"Dwivedi, R., et al.: Explainable AI (XAI): core ideas, techniques, and solutions. ACM Comput. Surv. 55(9), 1\u201333 (2023). https:\/\/doi.org\/10.1145\/356104","journal-title":"ACM Comput. Surv."},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Hossain, M.R.: Killing billions to save millions? Analyzing the double jeopardy of fossil-fuel-led economic development in bangladesh. Environ. Dev. Sustain. 1\u201332 (2023). https:\/\/doi.org\/10.1007\/s10668-023-03497-2","DOI":"10.1007\/s10668-023-03497-2"},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fpubh.2020.00014","volume":"8","author":"I Manisalidis","year":"2020","unstructured":"Manisalidis, I., Stavropoulou, E., Stavropoulos, A., Bezirtzoglou, E.: Environmental and health impacts of air pollution: a review. Front. Public Health 8, 14 (2020). https:\/\/doi.org\/10.3389\/fpubh.2020.00014","journal-title":"Front. Public Health"},{"issue":"15","key":"30_CR7","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.3390\/math10152552","volume":"10","author":"RI Mukhamediev","year":"2022","unstructured":"Mukhamediev, R.I., et al.: Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges. Mathematics 10(15), 2552 (2022). https:\/\/doi.org\/10.3390\/math10152552","journal-title":"Mathematics"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Orellano, P., Reynoso, J., Quaranta, N., Bardach, A., Ciapponi, A.: Short-term exposure to particulate matter (pm10 and pm2. 5), nitrogen dioxide (no2), and ozone (O3) and all-cause and cause-specific mortality: systematic review and meta-analysis. Environ. Int. 142, 105876 (2020). https:\/\/doi.org\/10.1016\/j.envint.2020.105876","DOI":"10.1016\/j.envint.2020.105876"},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"Ozyegen, O., Ilic, I., Cevik, M.: Evaluation of interpretability methods for multivariate time series forecasting. Appl. Intell. 1\u201317 (2022). https:\/\/doi.org\/10.1007\/s10489-021-02662-2","DOI":"10.1007\/s10489-021-02662-2"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Pantiskas, L., Verstoep, K., Bal, H.: Interpretable multivariate time series forecasting with temporal attention convolutional neural networks. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1687\u20131694. IEEE (2020)","DOI":"10.1109\/SSCI47803.2020.9308570"},{"key":"30_CR11","doi-asserted-by":"publisher","first-page":"101243","DOI":"10.1016\/j.cogsys.2024.101243","volume":"86","author":"CO Retzlaff","year":"2024","unstructured":"Retzlaff, C.O., et al.: Post-hoc vs ante-hoc explanations: XAI design guidelines for data scientists. Cogn. Syst. Res. 86, 101243 (2024). https:\/\/doi.org\/10.1016\/j.cogsys.2024.101243","journal-title":"Cogn. Syst. Res."},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Sen, J., Mehtab, S.: Long-and-short-term memory (LSTM) networks architectures and applications in stock price prediction. Emerg. Comput. Paradigms: Principles Adv. Appl. 143\u2013160 (2022). https:\/\/doi.org\/10.1002\/9781119813439.ch8","DOI":"10.1002\/9781119813439.ch8"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Sunder, M.S., Tikkiwal, V.A., Kumar, A., Tyagi, B.: Unveiling the transparency of prediction models for spatial PM2. 5 over Singapore: comparison of different machine learning approaches with explainable artificial intelligence. AI 4(4), 787\u2013811 (2023)","DOI":"10.3390\/ai4040040"},{"issue":"5","key":"30_CR14","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/computers12050091","volume":"12","author":"MM Taye","year":"2023","unstructured":"Taye, M.M.: Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5), 91 (2023). https:\/\/doi.org\/10.3390\/computers12050091","journal-title":"Computers"},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"122079","DOI":"10.1016\/j.apenergy.2023.122079","volume":"353","author":"C Van Zyl","year":"2024","unstructured":"Van Zyl, C., Ye, X., Naidoo, R.: Harnessing explainable artificial intelligence for feature selection in time series energy forecasting: a comparative analysis of grad-cam and shap. Appl. Energy 353, 122079 (2024). https:\/\/doi.org\/10.1016\/j.apenergy.2023.122079","journal-title":"Appl. Energy"},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"101039","DOI":"10.1016\/j.ecoinf.2019.101039","volume":"56","author":"GM Vega","year":"2020","unstructured":"Vega, G.M., Aznarte Jos\u00e9, L.: Shapley additive explanations for NO2 forecasting. Eco. Inform. 56, 101039 (2020)","journal-title":"Eco. Inform."},{"key":"30_CR17","doi-asserted-by":"publisher","unstructured":"Wang, Y., et\u00a0al.: Contrasting trends of PM2. 5 and surface-ozone concentrations in China from 2013 to 2017. Natl. Sci. Rev. 7(8), 1331\u20131339 (2020). https:\/\/doi.org\/10.1093\/nsr\/nwaa032","DOI":"10.1093\/nsr\/nwaa032"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77731-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:46:41Z","timestamp":1732034801000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77731-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"ISBN":["9783031777301","9783031777318"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77731-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"14 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valencia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}