{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:20:56Z","timestamp":1769854856459,"version":"3.49.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031401763","type":"print"},{"value":"9783031401770","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-40177-0_12","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"185-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Selecting Explanation Methods for\u00a0Intelligent IoT Systems: A Case-Based Reasoning Approach"],"prefix":"10.1007","author":[{"given":"Humberto","family":"Parejas-Llanovarced","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesus M.","family":"Darias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5239-8207","authenticated-orcid":false,"given":"Marta","family":"Caro-Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8731-6195","authenticated-orcid":false,"given":"Juan A.","family":"Recio-Garcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Abioye, S.O., et al.: Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges. J. Build. Eng. 44, 103299 (2021). https:\/\/doi.org\/10.1016\/J.JOBE.2021.103299","DOI":"10.1016\/J.JOBE.2021.103299"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Ahmed, I., Jeon, G., Piccialli, F.: From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Trans. Industr. Inf. 18(8), 5031\u20135042 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3146552","DOI":"10.1109\/TII.2022.3146552"},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/J.COMCOM.2022.06.039","volume":"193","author":"MM Alani","year":"2022","unstructured":"Alani, M.M.: BotStop: packet-based efficient and explainable IoT botnet detection using machine learning. Comput. Commun. 193, 53\u201362 (2022). https:\/\/doi.org\/10.1016\/J.COMCOM.2022.06.039","journal-title":"Comput. Commun."},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 11(5) (2021). https:\/\/doi.org\/10.1002\/widm.1424","DOI":"10.1002\/widm.1424"},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible ai. Inf. fusion 58, 82\u2013115 (2020)","journal-title":"Inf. fusion"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Atakishiyev, S., Salameh, M., Yao, H., Goebel, R.: Explainable artificial intelligence for autonomous driving: a comprehensive overview and field guide for future research directions (2021). https:\/\/doi.org\/10.48550\/arxiv.2112.11561","DOI":"10.48550\/arxiv.2112.11561"},{"key":"12_CR7","unstructured":"Caruana, R., Kangarloo, H., Dionisio, J.D., Sinha, U., Johnson, D.: Case-based explanation of non-case-based learning methods. In: Proceedings of the AMIA Symposium, p. 212 (1999). https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC2232607\/"},{"key":"12_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-14923-8_1","volume-title":"Case-Based Reasoning Research and Development","author":"JM Darias","year":"2022","unstructured":"Darias, J.M., Caro-Mart\u00ednez, M., D\u00edaz-Agudo, B., Recio-Garcia, J.A.: Using case-based reasoning for capturing expert knowledge on explanation methods. In: Keane, M.T., Wiratunga, N. (eds.) ICCBR 2022. LNCS, vol. 13405, pp. 3\u201317. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-14923-8_1"},{"key":"12_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.009.2100500","author":"H Elayan","year":"2022","unstructured":"Elayan, H., Aloqaily, M., Karray, F., Guizani, M.: Internet of behavior (IoB) and explainable AI systems for influencing IoT behavior. IEEE Netw. (2022). https:\/\/doi.org\/10.1109\/MNET.009.2100500","journal-title":"IEEE Netw."},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI-explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)","DOI":"10.1126\/scirobotics.aay7120"},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1109\/OJCOMS.2022.3188750","volume":"3","author":"ZAE Houda","year":"2022","unstructured":"Houda, Z.A.E., Brik, B., Khoukhi, L.: \u2018Why should i trust your IDS?\u2019: an explainable deep learning framework for intrusion detection systems in internet of things networks. IEEE Open J. Commun. Soc. 3, 1164\u20131176 (2022). https:\/\/doi.org\/10.1109\/OJCOMS.2022.3188750","journal-title":"IEEE Open J. Commun. Soc."},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Islam, M.R., Ahmed, M.U., Barua, S., Begum, S.: A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci. 12(3), 1353 (2022). https:\/\/doi.org\/10.3390\/APP12031353","DOI":"10.3390\/APP12031353"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Kenny, E.M., Keane, M.T.: Twin-systems to explain artificial neural networks using case-based reasoning: comparative tests of feature-weighting methods in ANN-CBR twins for XAI. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2708\u20132715 (2019). https:\/\/doi.org\/10.24963\/IJCAI.2019\/376","DOI":"10.24963\/IJCAI.2019\/376"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.future.2021.09.010","volume":"127","author":"IA Khan","year":"2022","unstructured":"Khan, I.A., et al.: XSRU-IoMT: explainable simple recurrent units for threat detection in internet of medical things networks. Futur. Gener. Comput. Syst. 127, 181\u2013193 (2022). https:\/\/doi.org\/10.1016\/j.future.2021.09.010","journal-title":"Futur. Gener. Comput. Syst."},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Kok, I., Okay, F.Y., Muyanli, O., Ozdemir, S.: Explainable artificial intelligence (XAI) for internet of things: a survey (2022). https:\/\/doi.org\/10.48550\/arxiv.2206.04800","DOI":"10.48550\/arxiv.2206.04800"},{"key":"12_CR16","unstructured":"Lakkaraju, H., Arsov, N., Bastani, O.: Robust and stable black box explanations. In: International Conference on Machine Learning, pp. 5628\u20135638. PMLR (2020)"},{"key":"12_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-642-23291-6_9","volume-title":"Case-Based Reasoning Research and Development","author":"D Leake","year":"2011","unstructured":"Leake, D., Wilson, M.: How many cases do you need? Assessing and predicting case-base coverage. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS (LNAI), vol. 6880, pp. 92\u2013106. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23291-6_9"},{"key":"12_CR18","doi-asserted-by":"publisher","first-page":"66933","DOI":"10.1109\/ACCESS.2022.3184693","volume":"10","author":"T Mansouri","year":"2022","unstructured":"Mansouri, T., Vadera, S.: A deep explainable model for fault prediction using IoT sensors. IEEE Access 10, 66933\u201366942 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3184693","journal-title":"IEEE Access"},{"issue":"3","key":"12_CR19","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1017\/S0269888906000646","volume":"20","author":"RL de M\u00e1ntaras","year":"2005","unstructured":"de M\u00e1ntaras, R.L., et al.: Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20(3), 215\u2013240 (2005). https:\/\/doi.org\/10.1017\/S0269888906000646","journal-title":"Knowl. Eng. Rev."},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"McDermid, J.A., Jia, Y., Porter, Z., Habli, I.: Artificial intelligence explainability: the technical and ethical dimensions. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 379(2207), 20200363 (2021). https:\/\/doi.org\/10.1098\/rsta.2020.0363, https:\/\/royalsocietypublishing.org\/doi\/10.1098\/rsta.2020.0363","DOI":"10.1098\/rsta.2020.0363"},{"issue":"22","key":"12_CR21","doi-asserted-by":"publisher","first-page":"24920","DOI":"10.1109\/JSEN.2021.3055618","volume":"21","author":"SC Mukhopadhyay","year":"2021","unstructured":"Mukhopadhyay, S.C., Tyagi, S.K.S., Suryadevara, N.K., Piuri, V., Scotti, F., Zeadally, S.: Artificial intelligence-based sensors for next generation IoT applications: a review. IEEE Sens. J. 21(22), 24920\u201324932 (2021). https:\/\/doi.org\/10.1109\/JSEN.2021.3055618","journal-title":"IEEE Sens. J."},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Naeem, H., Alshammari, B.M., Ullah, F.: Explainable artificial intelligence-based IoT device malware detection mechanism using image visualization and fine-tuned CNN-based transfer learning model. Comput. Intell. Neurosci. 2022 (2022). https:\/\/doi.org\/10.1155\/2022\/7671967","DOI":"10.1155\/2022\/7671967"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Parejas-Llanovarced, H., Darias, J., Caro-Martinez, M., Recio-Garcia, J.A.: A case base of explainable artificial intelligence of the things (XAIoT) systems (2023). https:\/\/doi.org\/10.21227\/4nb2-q910, https:\/\/dx.doi.org\/10.21227\/4nb2-q910","DOI":"10.21227\/4nb2-q910"},{"key":"12_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-58342-2_12","volume-title":"Case-Based Reasoning Research and Development","author":"JA Recio-Garc\u00eda","year":"2020","unstructured":"Recio-Garc\u00eda, J.A., D\u00edaz-Agudo, B., Pino-Castilla, V.: CBR-LIME: a case-based reasoning approach to provide specific local interpretable model-agnostic explanations. In: Watson, I., Weber, R. (eds.) ICCBR 2020. LNCS (LNAI), vol. 12311, pp. 179\u2013194. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58342-2_12"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Sado, F., et al.: Explainable goal-driven agents and robots - a comprehensive review. ACM Comput. Surv. 1(211) (2023). https:\/\/doi.org\/10.1145\/3564240","DOI":"10.1145\/3564240"},{"key":"12_CR26","unstructured":"Senevirathna, Tet al.: A survey on XAI for beyond 5G security: technical aspects, use cases, challenges and research directions (2022)"},{"issue":"3","key":"12_CR27","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3390\/MAKE3030032","volume":"3","author":"G Vilone","year":"2021","unstructured":"Vilone, G., Longo, L.: Classification of explainable artificial intelligence methods through their output formats. Mach. Learn. Knowl. Extract. 3(3), 615\u2013661 (2021). https:\/\/doi.org\/10.3390\/MAKE3030032","journal-title":"Mach. Learn. Knowl. Extract."},{"key":"12_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/978-3-030-01081-2_29","volume-title":"Case-Based Reasoning Research and Development","author":"RO Weber","year":"2018","unstructured":"Weber, R.O., Johs, A.J., Li, J., Huang, K.: Investigating textual case-based XAI. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 431\u2013447. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01081-2_29"}],"container-title":["Lecture Notes in Computer Science","Case-Based Reasoning Research and Development"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40177-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T23:08:36Z","timestamp":1691708916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40177-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031401763","9783031401770"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40177-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Case-Based Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberdeen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccbr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.comp.rgu.ac.uk\/ICCBR23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}