{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:14Z","timestamp":1742914274337,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031610684"},{"type":"electronic","value":"9783031610691"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-61069-1_9","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T15:02:00Z","timestamp":1717254120000},"page":"116-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Sustainable Power Systems: Exploring the\u00a0Opportunities of\u00a0Multi-task Learning for\u00a0Battery Degradation Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0418-3871","authenticated-orcid":false,"given":"Emilie","family":"Gr\u00e9goire","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1742-5561","authenticated-orcid":false,"given":"Sam","family":"Verboven","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,2]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Abbas, W., Tap, M.: Adaptively weighted multi-task learning using inverse validation loss. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1408\u20131412 (2019)","DOI":"10.1109\/ICASSP.2019.8683776"},{"issue":"3","key":"9_CR2","doi-asserted-by":"publisher","DOI":"10.1149\/1945-7111\/abec55","volume":"168","author":"M Aykol","year":"2021","unstructured":"Aykol, M., et al.: Perspective-combining physics and machine learning to predict battery lifetime. J. Electrochem. Soc. 168(3), 030525 (2021). https:\/\/doi.org\/10.1149\/1945-7111\/abec55","journal-title":"J. Electrochem. Soc."},{"key":"9_CR3","doi-asserted-by":"publisher","unstructured":"Bao, X., Liu, Y., Liu, B., Liu, H., Wang, Y.: Multi-state online estimation of lithium-ion batteries based on multi-task learning. Energies 16(7) (2023). https:\/\/doi.org\/10.3390\/en16073002","DOI":"10.3390\/en16073002"},{"issue":"2","key":"9_CR4","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0378-7753(03)00537-8","volume":"123","author":"JR Belt","year":"2003","unstructured":"Belt, J.R., Ho, C.D., Motloch, C.G., Miller, T.J., Duong, T.Q.: A capacity and power fade study of li-ion cells during life cycle testing. J. Power Sources 123(2), 241\u2013246 (2003)","journal-title":"J. Power Sources"},{"key":"9_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2019.227558","volume":"449","author":"C Bian","year":"2020","unstructured":"Bian, C., He, H., Yang, S., Huang, T.: State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture. J. Power Sources 449, 227558 (2020). https:\/\/doi.org\/10.1016\/j.jpowsour.2019.227558","journal-title":"J. Power Sources"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Chandran, V., Patil, C., Karthick, A., Ganeshaperumal, D., Rahim, R., Ghosh, A.: State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms. World Electr. Veh. J. 12, 38 (2021). https:\/\/doi.org\/10.3390\/WEVJ12010038","DOI":"10.3390\/WEVJ12010038"},{"key":"9_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123222","volume":"245","author":"D Chen","year":"2022","unstructured":"Chen, D., et al.: An empirical-data hybrid driven approach for remaining useful life prediction of lithium-ion batteries considering capacity diving. Energy 245, 123222 (2022). https:\/\/doi.org\/10.1016\/j.energy.2022.123222","journal-title":"Energy"},{"key":"9_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120451","volume":"227","author":"J Chen","year":"2021","unstructured":"Chen, J., Feng, X., Jiang, L., Zhu, Q.: State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network. Energy 227, 120451 (2021)","journal-title":"Energy"},{"key":"9_CR9","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: ICML, pp. 793\u2013802 (2018)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Chennupati, S., Sistu, G., Yogamani, S.K., Rawashdeh, S.A.: Multinet++: multi-stream feature aggregation and geometric loss strategy for multi-task learning. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1200\u20131210 (2019)","DOI":"10.1109\/CVPRW.2019.00159"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR abs\/1406.1078 (2014). http:\/\/arxiv.org\/abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Cipolla, R., Gal, Y., Kendall, A.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7482\u20137491 (2018)","DOI":"10.1109\/CVPR.2018.00781"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160\u2013167 (2008)","DOI":"10.1145\/1390156.1390177"},{"key":"9_CR14","doi-asserted-by":"publisher","unstructured":"Costa, C., Barbosa, J., Gon\u00e7alves, R., Castro, H., Campo, F.D., Lanceros-M\u00e9ndez, S.: Recycling and environmental issues of lithium-ion batteries: advances, challenges and opportunities. Energy Storage Mater. 37, 433\u2013465 (2021). https:\/\/doi.org\/10.1016\/j.ensm.2021.02.032, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2405829721000829","DOI":"10.1016\/j.ensm.2021.02.032"},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"27374","DOI":"10.1109\/ACCESS.2021.3058018","volume":"9","author":"S Cui","year":"2021","unstructured":"Cui, S., Joe, I.: A dynamic spatial-temporal attention-based GRU model with healthy features for state-of-health estimation of lithium-ion batteries. IEEE Access 9, 27374\u201327388 (2021)","journal-title":"IEEE Access"},{"key":"9_CR16","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-030-51965-0_15","volume-title":"Intelligent Algorithms in Software Engineering","author":"S Cui","year":"2020","unstructured":"Cui, S., Yong, X., Kim, S., Hong, S., Joe, I.: An LSTM-based encoder-decoder model for state-of-charge estimation of lithium-ion batteries. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1224, pp. 178\u2013188. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-51965-0_15"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Das, S., Tariq, A., Santos, T., Kantareddy, S.S., Banerjee, I.: Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research, pp. 117\u2013138. Springer US, New York, NY (2023). https:\/\/doi.org\/10.1007\/978-1-0716-3195-9_4","DOI":"10.1007\/978-1-0716-3195-9_4"},{"key":"9_CR18","first-page":"27503","volume":"34","author":"C Fifty","year":"2021","unstructured":"Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., Finn, C.: Efficiently identifying task groupings for multi-task learning. Adv. Neural. Inf. Process. Syst. 34, 27503\u201327516 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"141627","DOI":"10.1109\/ACCESS.2019.2943604","volume":"7","author":"T Gong","year":"2019","unstructured":"Gong, T., et al.: A comparison of loss weighting strategies for multi task learning in deep neural networks. IEEE Access 7, 141627\u2013141632 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2943604","journal-title":"IEEE Access"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Gr\u00e9goire, E., Chaudhary, H., Verboven, S.: Sample-level weighting for multi-task learning with auxiliary tasks. arXiv preprint arXiv:2306.04519 (2023)","DOI":"10.1007\/s10489-024-05300-9"},{"key":"9_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115646","volume":"278","author":"J Hong","year":"2020","unstructured":"Hong, J., Lee, D., Jeong, E.R., Yi, Y.: Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning. Appl. Energy 278, 115646 (2020)","journal-title":"Appl. Energy"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Hosen, M.S., Jaguemont, J., Van Mierlo, J., Berecibar, M.: Battery lifetime prediction and performance assessment of different modeling approaches. iScience 24(2), 102060 (2021). https:\/\/doi.org\/10.1016\/j.isci.2021.102060","DOI":"10.1016\/j.isci.2021.102060"},{"key":"9_CR23","doi-asserted-by":"publisher","unstructured":"Hossain Lipu, M., Hannan, M., Karim, T.F., Hussain, A., Saad, M.H.M., Ayob, A., Miah, M.S., Indra Mahlia, T.: Intelligent algorithms and control strategies for battery management system in electric vehicles: progress, challenges and future outlook. J. Cleaner Prod. 292, 126044 (2021). https:\/\/doi.org\/10.1016\/j.jclepro.2021.126044, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095965262100264X","DOI":"10.1016\/j.jclepro.2021.126044"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Jin, S., Sui, X., Huang, X., Wang, S., Teodorescu, R., Stroe, D.I.: Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction. Electronics 10(24) (2021). https:\/\/doi.org\/10.3390\/electronics10243126, https:\/\/www.mdpi.com\/2079-9292\/10\/24\/3126","DOI":"10.3390\/electronics10243126"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Jones, P.K., Stimming, U., Lee, A.A.: Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nature News (2022). https:\/\/www.nature.com\/articles\/s41467-022-32422-w#citeas","DOI":"10.26434\/chemrxiv-2021-2kxxt"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Karger, A., Wildfeuer, L., Ayg\u00fcl, D., Maheshwari, A., Singer, J.P., Jossen, A.: Modeling capacity fade of lithium-ion batteries during dynamic cycling considering path dependence. J. Energy Storage 52, 104718 (2022). https:\/\/doi.org\/10.1016\/j.est.2022.104718","DOI":"10.1016\/j.est.2022.104718"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482\u20137491 (2018)","DOI":"10.1109\/CVPR.2018.00781"},{"key":"9_CR28","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.ensm.2022.09.013","volume":"53","author":"W Li","year":"2022","unstructured":"Li, W., Zhang, H., van Vlijmen, B., Dechent, P., Sauer, D.U.: Forecasting battery capacity and power degradation with multi-task learning. Energy Storage Mater. 53, 453\u2013466 (2022)","journal-title":"Energy Storage Mater."},{"key":"9_CR29","unstructured":"Lin, B., Ye, F., Zhang, Y., Tsang, I.W.: Reasonable effectiveness of random weighting: a litmus test for multi-task learning. arXiv preprint arXiv:2111.10603 (2021)"},{"key":"9_CR30","unstructured":"Lin, X., Baweja, H., Kantor, G., Held, D.: Adaptive auxiliary task weighting for reinforcement learning. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019)"},{"key":"9_CR31","unstructured":"Liu, B., Liu, X., Jin, X., Stone, P., Liu, Q.: Conflict-averse gradient descent for multi-task learning. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 18878\u201318890. Curran Associates, Inc. (2021)"},{"key":"9_CR32","doi-asserted-by":"publisher","unstructured":"Liu, S., Liang, Y., Gitter, A.: Loss-balanced task weighting to reduce negative transfer in multi-task learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 33(01), 9977\u20139978 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33019977, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5125","DOI":"10.1609\/aaai.v33i01.33019977"},{"key":"9_CR33","unstructured":"Luong, M.T., Le, Q.V., Sutskever, I., Vinyals, O., Kaiser, L.: Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114 (2015)"},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Mehmood, T., Gerevini, A.E., Lavelli, A., Serina, I.: Combining multi-task learning with transfer learning for biomedical named entity recognition. Procedia Comput. Sci. 176, 848\u2013857 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.09.080, knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020","DOI":"10.1016\/j.procs.2020.09.080"},{"key":"9_CR35","doi-asserted-by":"publisher","unstructured":"Nikentari, N., Wei, H.L.: Multi-task learning for time series forecasting using NARMAX-LSTM. In: 2022 27th International Conference on Automation and Computing (ICAC), pp.\u00a01\u20136 (2022). https:\/\/doi.org\/10.1109\/ICAC55051.2022.9911071","DOI":"10.1109\/ICAC55051.2022.9911071"},{"key":"9_CR36","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1038\/nclimate2564","volume":"5","author":"B Nykvist","year":"2015","unstructured":"Nykvist, B., Nilsson, M.: Rapidly falling costs of battery packs for electric vehicles. Nat. Clim. Chang. 5, 329\u2013332 (2015)","journal-title":"Nat. Clim. Chang."},{"key":"9_CR37","doi-asserted-by":"publisher","unstructured":"Patil, M.A., .: A novel multistage support vector machine based approach for li ion battery remaining useful life estimation. Appl. Energy 159, 285\u2013297 (2015). https:\/\/doi.org\/10.1016\/j.apenergy.2015.08.119, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306261915010557","DOI":"10.1016\/j.apenergy.2015.08.119"},{"key":"9_CR38","doi-asserted-by":"publisher","unstructured":"Rieger, L.H., et al.: Uncertainty-aware and explainable machine learning for early prediction of battery degradation (2022). https:\/\/doi.org\/10.26434\/chemrxiv-2022-h1g21","DOI":"10.26434\/chemrxiv-2022-h1g21"},{"key":"9_CR39","doi-asserted-by":"publisher","unstructured":"Scrosati, B., Garche, J.: Lithium batteries: status, prospects and future. J. Power Sources 195(9), 2419\u20132430 (2010). https:\/\/doi.org\/10.1016\/j.jpowsour.2009.11.048, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0378775309020564","DOI":"10.1016\/j.jpowsour.2009.11.048"},{"key":"9_CR40","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"9_CR41","doi-asserted-by":"crossref","unstructured":"Senushkin, D., Patakin, N., Kuznetsov, A., Konushin, A.: Independent component alignment for multi-task learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20083\u201320093 (2023)","DOI":"10.1109\/CVPR52729.2023.01923"},{"issue":"1","key":"9_CR42","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/batteries9010013","volume":"9","author":"P Sharma","year":"2022","unstructured":"Sharma, P., Bora, B.J.: A review of modern machine learning techniques in the prediction of remaining useful life of lithium-ion batteries. Batteries 9(1), 13 (2022). https:\/\/doi.org\/10.3390\/batteries9010013","journal-title":"Batteries"},{"key":"9_CR43","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"9_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.104701","volume":"52","author":"T Tang","year":"2022","unstructured":"Tang, T., Yuan, H.: An indirect remaining useful life prognosis for li-ion batteries based on health indicator and novel artificial neural network. J. Energy Storage 52, 104701 (2022)","journal-title":"J. Energy Storage"},{"key":"9_CR45","doi-asserted-by":"publisher","first-page":"29705","DOI":"10.1007\/s11042-018-6463-x","volume":"77","author":"KH Thung","year":"2018","unstructured":"Thung, K.H., Wee, C.Y.: A brief review on multi-task learning. Multimedia Tools Appl. 77, 29705\u201329725 (2018)","journal-title":"Multimedia Tools Appl."},{"key":"9_CR46","doi-asserted-by":"publisher","unstructured":"Tian, J., Xiong, R., Shen, W., Lu, J.: Data-driven battery degradation prediction: forecasting voltage-capacity curves using one-cycle data. EcoMat 4(5) (2022). https:\/\/doi.org\/10.1002\/eom2.12213","DOI":"10.1002\/eom2.12213"},{"key":"9_CR47","doi-asserted-by":"publisher","unstructured":"Tomaszewska, A., et al.: Lithium-ion battery fast charging: a review. eTransportation 1, 100011 (2019). https:\/\/doi.org\/10.1016\/j.etran.2019.100011, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2590116819300116","DOI":"10.1016\/j.etran.2019.100011"},{"issue":"7","key":"9_CR48","doi-asserted-by":"publisher","first-page":"3614","DOI":"10.1109\/TPAMI.2021.3054719","volume":"44","author":"S Vandenhende","year":"2022","unstructured":"Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., Van Gool, L.: Multi-task learning for dense prediction tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3614\u20133633 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3054719","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"9_CR49","first-page":"5808","volume":"53","author":"S Verboven","year":"2023","unstructured":"Verboven, S., Chaudhary, M.H., Berrevoets, J., Ginis, V., Verbeke, W.: Hydalearn: Highly dynamic task weighting for multitask learning with auxiliary tasks. Appl. Intell. 53(5), 5808\u20135822 (2023)","journal-title":"Appl. Intell."},{"issue":"9","key":"9_CR50","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.3390\/electronics9091363","volume":"9","author":"N Vithayathil Varghese","year":"2020","unstructured":"Vithayathil Varghese, N., Mahmoud, Q.H.: A survey of multi-task deep reinforcement learning. Electronics 9(9), 1363 (2020)","journal-title":"Electronics"},{"key":"9_CR51","doi-asserted-by":"publisher","unstructured":"Yang, F., Yang, F., Li, W., Li, C., Miao, Q.: State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy (2019). https:\/\/doi.org\/10.1016\/J.ENERGY.2019.03.059","DOI":"10.1016\/J.ENERGY.2019.03.059"},{"key":"9_CR52","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 5824\u20135836. Curran Associates, Inc. (2020)"},{"issue":"1","key":"9_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-13993-7","volume":"11","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U., Lee, A.A.: Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nat. Commun. 11(1), 1\u20136 (2020)","journal-title":"Nat. Commun."}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence for Knowledge Management, Energy and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61069-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T22:55:16Z","timestamp":1732143316000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61069-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031610684","9783031610691"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61069-1_9","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI4KMES","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Workshop on Artificial Intelligence for Knowledge Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"30 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ai4km2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/ai4s\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}