{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:41:02Z","timestamp":1765546862730,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811675966"},{"type":"electronic","value":"9789811675973"}],"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\/978-981-16-7597-3_12","type":"book-chapter","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T11:02:48Z","timestamp":1646046168000},"page":"149-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Hybrid CNN-LSTM-Based Emotional Status Determination using Physiological Signals"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4279-0878","authenticated-orcid":false,"given":"Nazmun","family":"Nahar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5028-4986","authenticated-orcid":false,"given":"Ferdous","family":"Ara","sequence":"additional","affiliation":[]},{"given":"Jubair Ahmed","family":"Junjun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3090-7645","authenticated-orcid":false,"given":"Mohammad Shahadat","family":"Hossain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-3561","authenticated-orcid":false,"given":"Karl","family":"Andersson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"12_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et\u00a0al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). pp. 265\u2013283 (2016)"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Abedin, M.Z., Akther, S., Hossain, M.S.: An artificial neural network model for epilepsy seizure detection. In: 2019 5th International Conference on Advances in Electrical Engineering (ICAEE). pp. 860\u2013865. IEEE (2019)","DOI":"10.1109\/ICAEE48663.2019.8975569"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Afroze, T., Akther, S., Chowdhury, M.A., Hossain, E., Hossain, M.S., Andersson, K.: Glaucoma detection using inception convolutional neural network v3. In: International Conference on Applied Intelligence and Informatics. pp. 17\u201328. Springer (2021)","DOI":"10.1007\/978-3-030-82269-9_2"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed, T.U., Hossain, M.S., Alam, M.J., Andersson, K.: An integrated cnn-rnn framework to assess road crack. In: 2019 22nd International Conference on Computer and Information Technology (ICCIT). pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/ICCIT48885.2019.9038607"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Ahmed, T.U., Jamil, M.N., Hossain, M.S., Andersson, K., Hossain, M.S.: An integrated real-time deep learning and belief rule base intelligent system to assess facial expression under uncertainty. In: 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). pp.\u00a01\u20136. IEEE (2020)","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306622"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Anderson, K., McOwan, P.W.: A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 36(1), 96\u2013105 (2006)","DOI":"10.1109\/TSMCB.2005.854502"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Ayata, D., Yaslan, Y., Kamasak, M.E.: Emotion based music recommendation system using wearable physiological sensors. IEEE Transactions on Consumer Electronics 64(2), 196\u2013203 (2018)","DOI":"10.1109\/TCE.2018.2844736"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Basnin, N., Nahar, L., Hossain, M.S.: An integrated cnn-lstm model for micro hand gesture recognition. In: International Conference on Intelligent Computing & Optimization. pp. 379\u2013392. Springer (2020)","DOI":"10.1007\/978-3-030-68154-8_35"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Basnin, N., Nahar, L., Hossain, M.S.: An integrated cnn-lstm model for bangla lexical sign language recognition. In: Proceedings of International Conference on Trends in Computational and Cognitive Engineering. pp. 695\u2013707. Springer (2021)","DOI":"10.1007\/978-981-33-4673-4_57"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, B., Liu, G.: Emotion recognition from surface emg signal using wavelet transform and neural network. In: Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE). pp. 1363\u20131366 (2008)","DOI":"10.1109\/ICBBE.2008.670"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Gosh, S., Nahar, N., Wahab, M.A., Biswas, M., Hossain, M.S., Andersson, K.: Recommendation system for e-commerce using alternating least squares (als) on apache spark. In: International Conference on Intelligent Computing & Optimization. pp. 880\u2013893. Springer (2020)","DOI":"10.1007\/978-3-030-68154-8_75"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., Steunebrink, B.R., Schmidhuber, J.: Lstm: A search space odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10), 2222\u20132232 (2016)","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Hanjalic, A., Xu, L.Q.: Affective video content representation and modeling. IEEE Trans. Multimedia 7(1), 143\u2013154 (2005)","DOI":"10.1109\/TMM.2004.840618"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"190637","DOI":"10.1109\/ACCESS.2020.3031438","volume":"8","author":"RU Islam","year":"2020","unstructured":"Islam, R.U., Hossain, M.S., Andersson, K.: A deep learning inspired belief rule-based expert system. IEEE Access 8, 190637\u2013190651 (2020)","journal-title":"IEEE Access"},{"issue":"18","key":"12_CR15","doi-asserted-by":"publisher","first-page":"3438","DOI":"10.3390\/en12183438","volume":"12","author":"RU Islam","year":"2019","unstructured":"Islam, R.U., Ruci, X., Hossain, M.S., Andersson, K., Kor, A.L.: Capacity management of hyperscale data centers using predictive modelling. Energies 12(18), 3438 (2019)","journal-title":"Energies"},{"issue":"7","key":"12_CR16","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.3390\/s20071956","volume":"20","author":"S Kabir","year":"2020","unstructured":"Kabir, S., Islam, R.U., Hossain, M.S., Andersson, K.: An integrated approach of belief rule base and deep learning to predict air pollution. Sensors 20(7), 1956 (2020)","journal-title":"Sensors"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Katsis, C.D., Katertsidis, N., Ganiatsas, G., Fotiadis, D.I.: Toward emotion recognition in car-racing drivers: a biosignal processing approach. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(3), 502\u2013512 (2008)","DOI":"10.1109\/TSMCA.2008.918624"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Kim, J., Andr\u00e9, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067\u20132083 (2008)","DOI":"10.1109\/TPAMI.2008.26"},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1016\/j.neucom.2017.09.081","volume":"275","author":"SK Kim","year":"2018","unstructured":"Kim, S.K., Kang, H.B.: An analysis of smartphone overuse recognition in terms of emotions using brainwaves and deep learning. Neurocomputing 275, 1393\u20131406 (2018)","journal-title":"Neurocomputing"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.: Independently recurrent neural network (indrnn): Building a longer and deeper rnn. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5457\u20135466 (2018)","DOI":"10.1109\/CVPR.2018.00572"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Li, X., Song, D., Zhang, P., Yu, G., Hou, Y., Hu, B.: Emotion recognition from multi-channel eeg data through convolutional recurrent neural network. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 352\u2013359. IEEE (2016)","DOI":"10.1109\/BIBM.2016.7822545"},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Nahar, N., Ara, F., Neloy, M.A.I., Barua, V., Hossain, M.S., Andersson, K.: A comparative analysis of the ensemble method for liver disease prediction. In: 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET). pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/ICIET48527.2019.9290507"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Nahar, N., Hossain, M.S., Andersson, K.: A machine learning based fall detection for elderly people with neurodegenerative disorders. In: International Conference on Brain Informatics. pp. 194\u2013203. Springer (2020)","DOI":"10.1007\/978-3-030-59277-6_18"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Pathan, R.K., Uddin, M.A., Nahar, N., Ara, F., Hossain, M.S., Andersson, K.: Gender classification from inertial sensor-based gait dataset. In: International Conference on Intelligent Computing & Optimization. pp. 583\u2013596. Springer (2020)","DOI":"10.1007\/978-3-030-68154-8_51"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Pathan, R.K., Uddin, M.A., Nahar, N., Ara, F., Hossain, M.S., Andersson, K.: Human age estimation using deep learning from gait data. In: International Conference on Applied Intelligence and Informatics. pp. 281\u2013294. Springer (2021)","DOI":"10.1007\/978-3-030-82269-9_22"},{"key":"12_CR26","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et\u00a0al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"12_CR27","unstructured":"Petrushin, V.: Emotion in speech: recognition and application to call centers. In: Proceedings of Artificial Neural Networks in Engineering. vol.\u00a0710, p.\u00a022 (1999)"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Rattanyu, K., Ohkura, M., Mizukawa, M.: Emotion monitoring from physiological signals for service robots in the living space. In: ICCAS 2010. pp. 580\u2013583. IEEE (2010)","DOI":"10.1109\/ICCAS.2010.5669914"},{"issue":"8","key":"12_CR29","first-page":"329","volume":"9","author":"ES Salama","year":"2018","unstructured":"Salama, E.S., El-Khoribi, R.A., Shoman, M.E., Shalaby, M.A.W.: Eeg-based emotion recognition using 3d convolutional neural networks. Int. J. Adv. Comput. Sci. Appl 9(8), 329\u2013337 (2018)","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"key":"12_CR30","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/ACCESS.2018.2883213","volume":"7","author":"L Santamaria-Granados","year":"2018","unstructured":"Santamaria-Granados, L., Munoz-Organero, M., Ramirez-Gonzalez, G., Abdulhay, E., Arunkumar, N.: Using deep convolutional neural network for emotion detection on a physiological signals dataset (amigos). IEEE Access 7, 57\u201367 (2018)","journal-title":"IEEE Access"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Shahani, B.T., Halperin, J., Boulu, P., Cohen, J.: Sympathetic skin response-a method of assessing unmyelinated axon dysfunction in peripheral neuropathies. J. Neurol. Neurosurg. Psychiatry 47(5), 536\u2013542 (1984)","DOI":"10.1136\/jnnp.47.5.536"},{"key":"12_CR32","unstructured":"Siddharth, S., Jung, T.P., Sejnowski, T.J.: Utilizing deep learning towards multi-modal bio-sensing and vision-based affective computing. IEEE Trans. Affect. Comput. (2019)"},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427\u2013437 (2009)","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Soleymani, M., Pantic, M., Pun, T.: Multimodal emotion recognition in response to videos. IEEE Trans. Affect. Comput. 3(2), 211\u2013223 (2011)","DOI":"10.1109\/T-AFFC.2011.37"},{"key":"12_CR35","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)"},{"key":"12_CR36","doi-asserted-by":"crossref","unstructured":"Sultana, Z., Nahar, L., Basnin, N., Hossain, M.S.: Inference and learning methodology of belief rule based expert system to assess chikungunya. In: International Conference on Applied Intelligence and Informatics. pp. 3\u201316. Springer (2021)","DOI":"10.1007\/978-3-030-82269-9_1"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, J., Wang, X.: Bilateral lstm: A two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans. Industr. Inf. 14(2), 748\u2013758 (2017)","DOI":"10.1109\/TII.2017.2754641"},{"key":"12_CR38","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., Chen, X.: Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp.\u00a01\u20137. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"12_CR39","doi-asserted-by":"crossref","unstructured":"Yin, Z., Zhao, M., Wang, Y., Yang, J., Zhang, J.: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. Comput. Methods Programs Biomed. 140, 93\u2013110 (2017)","DOI":"10.1016\/j.cmpb.2016.12.005"},{"key":"12_CR40","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39\u201358 (2008)","DOI":"10.1109\/TPAMI.2008.52"},{"key":"12_CR41","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.inffus.2020.01.011","volume":"59","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Yin, Z., Chen, P., Nichele, S.: Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion 59, 103\u2013126 (2020)","journal-title":"Information Fusion"},{"key":"12_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, Y., Liu, Y.: Eeg-based emotion recognition using an improved radial basis function neural network. J. Ambient Intell. Humanized Comput. pp. 1\u201312 (2020)","DOI":"10.1007\/s12652-020-02049-0"},{"key":"12_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhao, X., Lei, B.: Spoken emotion recognition using radial basis function neural network. In: International Conference on Computer Science, Environment, Ecoinformatics, and Education. pp. 437\u2013442. Springer (2011)","DOI":"10.1007\/978-3-642-23321-0_68"},{"issue":"11","key":"12_CR44","doi-asserted-by":"publisher","first-page":"3886","DOI":"10.3390\/s18113886","volume":"18","author":"X Zhang","year":"2018","unstructured":"Zhang, X., Xu, C., Xue, W., Hu, J., He, Y., Gao, M.: Emotion recognition based on multichannel physiological signals with comprehensive nonlinear processing. Sensors 18(11), 3886 (2018)","journal-title":"Sensors"},{"key":"12_CR45","doi-asserted-by":"crossref","unstructured":"Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from eeg. IEEE Trans Affect. Comput. 10(3), 417\u2013429 (2017)","DOI":"10.1109\/TAFFC.2017.2712143"},{"key":"12_CR46","doi-asserted-by":"crossref","unstructured":"Zisad, S.N., Hossain, M.S., Andersson, K.: Speech emotion recognition in neurological disorders using convolutional neural network. In: International Conference on Brain Informatics. pp. 287\u2013296. Springer (2020)","DOI":"10.1007\/978-3-030-59277-6_26"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7597-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T14:08:12Z","timestamp":1659103692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7597-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811675966","9789811675973"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7597-3_12","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}