{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:24:14Z","timestamp":1743150254061,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030975456"},{"type":"electronic","value":"9783030975463"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-97546-3_1","type":"book-chapter","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T04:41:28Z","timestamp":1647578488000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explanation Module for\u00a0Deep Neural Networks Facing Multivariate Time Series Classification"],"prefix":"10.1007","author":[{"given":"Chao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,19]]},"reference":[{"key":"1_CR1","unstructured":"Ancona, M., Oztireli, C., Gross, M.: Explaining deep neural networks with a polynomial time algorithm for shapley value approximation. In: International Conference on Machine Learning, pp. 272\u2013281. PMLR (2019)"},{"key":"1_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Bai, L., Yao, L., Kanhere, S.S., Wang, X., Yang, Z.: Automatic device classification from network traffic streams of internet of things. In: 2018 IEEE 43rd Conference on Local Computer Networks (LCN), pp. 1\u20139. IEEE (2018)","DOI":"10.1109\/LCN.2018.8638232"},{"key":"1_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-030-47426-3_50","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"L Bai","year":"2020","unstructured":"Bai, L., Yao, L., Wang, X., Kanhere, S.S., Xiao, Y.: Prototype similarity learning for\u00a0activity recognition. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 649\u2013661. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47426-3_50"},{"key":"1_CR5","unstructured":"Borovykh, A., Bohte, S., Oosterlee, C.W.: Conditional time series forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691 (2017)"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.physa.2018.11.061","volume":"519","author":"J Cao","year":"2019","unstructured":"Cao, J., Li, Z., Li, J.: Financial time series forecasting model based on CEEMDAN and LSTM. Phys. A 519, 127\u2013139 (2019)","journal-title":"Phys. A"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839\u2013847. IEEE (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"1_CR9","unstructured":"Choi, E., Bahadori, M.T., Kulas, J.A., Schuetz, A., Stewart, W.F., Sun, J.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. arXiv preprint arXiv:1608.05745 (2016)"},{"key":"1_CR10","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. (CSUR) 45(1), 1\u201334 (2012)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"4","key":"1_CR12","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"H Ismail Fawaz","year":"2019","unstructured":"Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019). https:\/\/doi.org\/10.1007\/s10618-019-00619-1","journal-title":"Data Min. Knowl. Disc."},{"key":"1_CR13","unstructured":"Gamboa, J.C.B.: Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887 (2017)"},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S Gao","year":"2019","unstructured":"Gao, S., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.H.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.aquaculture.2012.07.034","volume":"362","author":"B Guo","year":"2012","unstructured":"Guo, B., Mu, Y., Wang, F., Dong, S.: Effect of periodic light color change on the molting frequency and growth of litopenaeus vannamei. Aquaculture 362, 67\u201371 (2012)","journal-title":"Aquaculture"},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.aquaculture.2013.02.033","volume":"396","author":"B Guo","year":"2013","unstructured":"Guo, B., Wang, F., Li, Y., Dong, S.: Effect of periodic light intensity change on the molting frequency and growth of litopenaeus vannamei. Aquaculture 396, 66\u201370 (2013)","journal-title":"Aquaculture"},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.neucom.2012.12.006","volume":"110","author":"M Han","year":"2013","unstructured":"Han, M., Liu, X.: Feature selection techniques with class separability for multivariate time series. Neurocomputing 110, 29\u201334 (2013)","journal-title":"Neurocomputing"},{"issue":"6","key":"1_CR19","doi-asserted-by":"publisher","first-page":"7833","DOI":"10.1109\/JSEN.2019.2923982","volume":"21","author":"Z Han","year":"2019","unstructured":"Han, Z., Zhao, J., Leung, H., Ma, K.F., Wang, W.: A review of deep learning models for time series prediction. IEEE Sens. J. 21(6), 7833\u20137848 (2019)","journal-title":"IEEE Sens. J."},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"1_CR21","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Hoermann, S., Bach, M., Dietmayer, K.: Dynamic occupancy grid prediction for urban autonomous driving: a deep learning approach with fully automatic labeling. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2056\u20132063. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460874"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Hsieh, T.Y., Wang, S., Sun, Y., Honavar, V.: Explainable multivariate time series classification: a deep neural network which learns to attend to important variables as well as time intervals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 607\u2013615 (2021)","DOI":"10.1145\/3437963.3441815"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2019.12.030","volume":"125","author":"Z Karevan","year":"2020","unstructured":"Karevan, Z., Suykens, J.A.: Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw. 125, 1\u20139 (2020)","journal-title":"Neural Netw."},{"key":"1_CR26","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","volume":"116","author":"F Karim","year":"2019","unstructured":"Karim, F., Majumdar, S., Darabi, H., Harford, S.: Multivariate LSTM-FCNS for time series classification. Neural Netw. 116, 237\u2013245 (2019)","journal-title":"Neural Netw."},{"key":"1_CR27","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-319-49409-8_7","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"C Lea","year":"2016","unstructured":"Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: a unified approach to action segmentation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 47\u201354. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_7"},{"issue":"7553","key":"1_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"1_CR30","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"key":"1_CR31","unstructured":"Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)"},{"issue":"11","key":"1_CR32","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1542\/pir.28.11.405","volume":"28","author":"P Major","year":"2007","unstructured":"Major, P., Thiele, E.A.: Seizures in children: laboratory. Pediatr. Rev. 28(11), 405 (2007)","journal-title":"Pediatr. Rev."},{"key":"1_CR33","unstructured":"Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017)"},{"key":"1_CR34","unstructured":"Olszewski, R.T.: Bobski\u2019s world (2012). http:\/\/www.cs.cmu.edu\/bobski\/"},{"key":"1_CR35","unstructured":"Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310\u20131318. PMLR (2013)"},{"key":"1_CR36","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","volume":"323","author":"A Sagheer","year":"2019","unstructured":"Sagheer, A., Kotb, M.: Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 323, 203\u2013213 (2019)","journal-title":"Neurocomputing"},{"key":"1_CR37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., Namin, A.S.: A comparison of ARIMA and LSTM in forecasting time series. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394\u20131401. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"1_CR40","doi-asserted-by":"publisher","first-page":"155304","DOI":"10.1109\/ACCESS.2019.2949287","volume":"7","author":"C Yang","year":"2019","unstructured":"Yang, C., Jiang, W., Guo, Z.: Time series data classification based on dual path CNN-RNN cascade network. IEEE Access 7, 155304\u2013155312 (2019)","journal-title":"IEEE Access"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Yoon, H., Shahabi, C.: Feature subset selection on multivariate time series with extremely large spatial features. In: Sixth IEEE International Conference on Data Mining-Workshops (ICDMW 2006), pp. 337\u2013342. IEEE (2006)","DOI":"10.1109\/ICDMW.2006.81"},{"key":"1_CR42","unstructured":"Yoon, J., Jordon, J., van der Schaar, M.: Invase: instance-wise variable selection using neural networks. In: International Conference on Learning Representations (2018)"},{"key":"1_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"1_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-319-08010-9_33","volume-title":"Web-Age Information Management","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298\u2013310. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08010-9_33"}],"container-title":["Lecture Notes in Computer Science","AI 2021: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97546-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T04:41:52Z","timestamp":1647578512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97546-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030975456","9783030975463"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97546-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 February 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ajcai2021.net","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":"120","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":"64","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":"53% - 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":"3","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":"5","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)"}},{"value":"The conference was postponed to 2022 and held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}