{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:13:10Z","timestamp":1776885190412,"version":"3.51.2"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781650","type":"print"},{"value":"9783031781667","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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\/978-3-031-78166-7_28","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:34:58Z","timestamp":1733088898000},"page":"430-445","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FAT-LSTM: A Multimodal Data Fusion Model with\u00a0Gating and\u00a0Attention-Based LSTM for\u00a0Time-Series Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-2709","authenticated-orcid":false,"given":"Pouya","family":"Hosseinzadeh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6961-3046","authenticated-orcid":false,"given":"Omar","family":"Bahri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-6383","authenticated-orcid":false,"given":"Soukaina Filali","family":"Boubrahimi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9303-7835","authenticated-orcid":false,"given":"Shah Muhammad","family":"Hamdi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Archila, J., Manzanera, A., Martinez, F.: A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision. In: Computer Methods and Programs in Biomedicine, 2015, pp. 106607 (2015)","DOI":"10.1016\/j.cmpb.2021.106607"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Gunes, H., Piccardi, M.: Affect recognition from face and body: early fusion vs. late fusion. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3437\u20133443. IEEE (2005)","DOI":"10.1109\/ICSMC.2005.1571679"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Gadzicki, K., Khamsehashari, R., Zetzsche, C.: Early vs late fusion in multimodal convolutional neural networks. In: 2020 IEEE 23rd International Conference on Information Fusion (FUSION), pp. 1\u20136. IEEE (2020)","DOI":"10.23919\/FUSION45008.2020.9190246"},{"key":"28_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-030-87986-0_26","volume-title":"Artificial Intelligence and Soft Computing","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Kempton, D.J., Ahmadzadeh, A., Angryk, R.A.: Towards synthetic multivariate time series generation for flare forecasting. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2021. LNCS (LNAI), vol. 12854, pp. 296\u2013307. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87986-0_26"},{"key":"28_CR5","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142\u2013153 (2013)","journal-title":"Inf. Sci."},{"key":"28_CR6","unstructured":"Whitman, K., et al.: Review of solar energetic particle models. In: Advances in Space Research, Elsevier (2022)"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hosseinzadeh, P., Boubrahimi, S.F., Hamdi, S.M.: Improving solar energetic particle event prediction through multivariate time series data augmentation. Astrophys. J. Suppl. Ser. 270(2), 31 (2024). IOP Publishing","DOI":"10.3847\/1538-4365\/ad1de0"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Hosseinzadeh, P., Filali Boubrahimi, S., Hamdi, S.M.: Toward enhanced prediction of high-impact solar energetic particle events using multimodal time series data fusion models. Space Weather 22(6), e2024SW003982 (2024)","DOI":"10.1029\/2024SW003982"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Filali Boubrahimi, S., Neema, A., Nassar, A., Hosseinzadeh, P., Hamdi, S.M.: Spatiotemporal data augmentation of MODIS-landsat water bodies using adversarial networks. Water Resour. Res. 60(3), e2023WR036342 (2024)","DOI":"10.1029\/2023WR036342"},{"issue":"7","key":"28_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3543848","volume":"55","author":"WC Sleeman IV","year":"2022","unstructured":"Sleeman, W.C., IV., Kapoor, R., Ghosh, P.: Multimodal classification: current landscape, taxonomy and future directions. ACM Comput. Surv. 55(7), 1\u201331 (2022)","journal-title":"ACM Comput. Surv."},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans. Med. Imaging 38(5), 1116\u2013-1126 (2018)","DOI":"10.1109\/TMI.2018.2878669"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Shenoy, A., Sardana, A.: Multilogue-net: A context-aware RNN for multi-modal emotion detection and sentiment analysis in conversation. arXiv preprint arXiv:2002.08267 (2020)","DOI":"10.18653\/v1\/2020.challengehml-1.3"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liang, X., Zhiyuli, A., Zhang, S., Xu, R., Wu, B.: At-LSTM: An attention-based LSTM model for financial time series prediction. In: IOP Conference Series: Materials Science and Engineering, vol. 569, no. 5, pp. 052037. IOP Publishing (2019)","DOI":"10.1088\/1757-899X\/569\/5\/052037"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Adversarial multimodal fusion with attention mechanism for skin lesion classification using clinical and dermoscopic images. Med. Image Anal. 81, 102535 (2022). Elsevier","DOI":"10.1016\/j.media.2022.102535"},{"issue":"1","key":"28_CR15","doi-asserted-by":"publisher","first-page":"8861","DOI":"10.1038\/s41598-024-58886-y","volume":"14","author":"M EskandariNasab","year":"2024","unstructured":"EskandariNasab, M., Raeisi, Z., Lashaki, R.A., Najafi, H.: A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis. Sci. Rep. 14(1), 8861 (2024)","journal-title":"Sci. Rep."},{"key":"28_CR16","unstructured":"Suzuki, M., Nakayama, K., Matsuo, Y.: Joint multimodal learning with deep generative models. arXiv preprint arXiv:1611.01891 (2016)"},{"issue":"1","key":"28_CR17","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1038\/s41598-019-56923-9","volume":"10","author":"M Guggenmos","year":"2020","unstructured":"Guggenmos, M., et al.: A multimodal neuroimaging classifier for alcohol dependence. Sci. Rep. 10(1), 298 (2020)","journal-title":"Sci. Rep."},{"issue":"6","key":"28_CR18","doi-asserted-by":"publisher","first-page":"e0129126","DOI":"10.1371\/journal.pone.0129126","volume":"10","author":"C Higuera","year":"2015","unstructured":"Higuera, C., Gardiner, K.J., Cios, K.J.: Self-organizing feature maps identify proteins critical to learning in a mouse model of Down syndrome. PLoS ONE 10(6), e0129126 (2015)","journal-title":"PLoS ONE"},{"key":"28_CR19","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.inffus.2012.09.005","volume":"19","author":"R Singh","year":"2014","unstructured":"Singh, R., Khare, A.: Fusion of multimodal medical images using Daubechies complex wavelet transform-a multiresolution approach. Inf. Fusion 19, 49\u201360 (2014)","journal-title":"Inf. Fusion"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Weerakoon, K., Sathyamoorthy, A.J., Liang, J., Guan, T., Patel, U., Manocha, D.: GrASPE: Graph based multimodal fusion for robot navigation in unstructured outdoor environments. arXiv preprint arXiv:2209.05722 (2022)","DOI":"10.1109\/LRA.2023.3320013"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Kubelka, V., Reinstein, M., Svoboda, T.: Improving multimodal data fusion for mobile robots by trajectory smoothing. Robot. Auton. Syst. 84, 88\u201396 (2016). Elsevier","DOI":"10.1016\/j.robot.2016.07.006"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"\u00d6zt\u00fcrk, \u015e.: Stacked auto-encoder based tagging with deep features for content-based medical image retrieval. Expert Syst. Appl. 161, 113693 (2020). Elsevier","DOI":"10.1016\/j.eswa.2020.113693"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Thomas, S.A., Race, A.M., Steven, R.T., Gilmore, I.S., Bunch, J.: Dimensionality reduction of mass spectrometry imaging data using autoencoders. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2016, pp. 1\u20137 (2016)","DOI":"10.1109\/SSCI.2016.7849863"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Hosseinzadeh, P., Bahri, O., Li, P., Boubrahimi, S.F., Hamdi, S.M.: METFORC: Classification with Meta-Learning and Multimodal Stratified Time Series Forest. In: 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 1248-1252. IEEE (2023)","DOI":"10.1109\/ICMLA58977.2023.00188"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662\u20131669 (2017)","DOI":"10.1109\/ACCESS.2017.2779939"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Ieracitano, C., Mammone, N., Hussain, A., Morabito, F.C.: A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Comput. Appl. 1\u201314 (2021)","DOI":"10.1007\/s00521-020-05624-w"},{"issue":"3","key":"28_CR27","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s10044-017-0597-8","volume":"20","author":"K Usman","year":"2017","unstructured":"Usman, K., Rajpoot, K.: Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal. Appl. 20(3), 871\u2013881 (2017). https:\/\/doi.org\/10.1007\/s10044-017-0597-8","journal-title":"Pattern Anal. Appl."},{"issue":"4","key":"28_CR28","doi-asserted-by":"publisher","first-page":"e0176244","DOI":"10.1371\/journal.pone.0176244","volume":"12","author":"H-W Kang","year":"2017","unstructured":"Kang, H.-W., Kang, H.-B.: Prediction of crime occurrence from multi-modal data using deep learning. PLoS ONE 12(4), e0176244 (2017)","journal-title":"PLoS ONE"},{"key":"28_CR29","doi-asserted-by":"publisher","first-page":"4014","DOI":"10.1109\/TMM.2020.3035277","volume":"23","author":"X Yang","year":"2020","unstructured":"Yang, X., Feng, S., Wang, D., Zhang, Y.: Image-text multimodal emotion classification via multi-view attentional network. IEEE Trans. Multimedia 23, 4014\u20134026 (2020)","journal-title":"IEEE Trans. Multimedia"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Chen, Y., Kempton, D.J., Ahmadzadeh, A., Angryk, R.A.: Towards synthetic multivariate time series generation for flare forecasting. In: Artificial Intelligence and Soft Computing: 20th International Conference, ICAISC 2021, Virtual Event, June 21\u201323, 2021, Proceedings, Part I, vol. 20, Springer, 2021, pp. 296\u2013307 (2021)","DOI":"10.1007\/978-3-030-87986-0_26"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78166-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:38:43Z","timestamp":1733096323000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78166-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781650","9783031781667"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78166-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}