{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:36:56Z","timestamp":1791668216125,"version":"4.3.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"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":["Vis Comput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00371-024-03369-4","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:25:34Z","timestamp":1713281134000},"page":"831-851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Instructor emotion recognition system using manta ray foraging algorithm for improving the content delivery in video lecture"],"prefix":"10.1007","volume":"41","author":[{"given":"Sameer Bhimrao","family":"Patil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh","family":"Shirgave","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"3369_CR1","doi-asserted-by":"crossref","DOI":"10.1016\/j.chb.2020.106554","volume":"114","author":"AP Lawson","year":"2021","unstructured":"Lawson, A.P., Mayer, R.E., Adamo-Villani, N., Benes, B., Lei, X., Cheng, J.: Recognizing the emotional state of human and virtual instructors. Comput. Human Behavior. 114, 106554 (2021)","journal-title":"Comput. Human Behavior."},{"key":"3369_CR2","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.3390\/s21093046","volume":"21","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Minaei, M., Abdolrashidi, A.: Deep-emotion: facial expression recognition using attentional convolutional network. Sensors. 21, 3046 (2021)","journal-title":"Sensors."},{"key":"3369_CR3","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1007\/s42452-020-2234-1","volume":"2","author":"N Mehendale","year":"2020","unstructured":"Mehendale, N.: Facial emotion recognition using convolutional neural networks (FERC). SN Appl. Sci. 2, 446 (2020)","journal-title":"SN Appl. Sci."},{"key":"3369_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.image.2019.01.002","volume":"74","author":"F Makhmudkhujaev","year":"2019","unstructured":"Makhmudkhujaev, F., Abdullah-Al-Wadud, M., Iqbal, M.T., Ryu, B., Chae, O.: Facial expression recognition with local prominent directional pattern. Signal Process Image Commun. 74, 1\u201312 (2019)","journal-title":"Signal Process Image Commun."},{"key":"3369_CR5","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang, K., Peng, X., Yang, J., Meng, D., Qiao, Y.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057\u20134069 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"3369_CR6","doi-asserted-by":"crossref","first-page":"78000","DOI":"10.1109\/ACCESS.2019.2921220","volume":"7","author":"S Miao","year":"2019","unstructured":"Miao, S., Xu, H., Han, Z., Zhu, Y.: Recognizing facial expressions using a shallow convolutional neural network. IEEE Access. 7, 78000\u201378011 (2019)","journal-title":"IEEE Access."},{"key":"3369_CR7","doi-asserted-by":"crossref","first-page":"30335","DOI":"10.1007\/s11042-019-07863-z","volume":"78","author":"H Sadeghi","year":"2019","unstructured":"Sadeghi, H., Raie, A.-A.: Human vision inspired feature extraction for facial expression recognition. Multimed. Tools Appl. 78, 30335\u201330353 (2019)","journal-title":"Multimed. Tools Appl."},{"key":"3369_CR8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.3390\/biomimetics8020199","volume":"8","author":"Z Wen","year":"2023","unstructured":"Wen, Z., Lin, W., Wang, T., Xu, G.: Distract your attention: multi-head cross attention network for facial expression recognition. Biomimetics. 8, 199 (2023)","journal-title":"Biomimetics."},{"key":"3369_CR9","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.3390\/s20071936","volume":"20","author":"D Jeong","year":"2020","unstructured":"Jeong, D., Kim, B.-G., Dong, S.-Y.: Deep joint spatiotemporal network (DJSTN) for efficient facial expression recognition. Sensors. 20, 1936 (2020)","journal-title":"Sensors."},{"key":"3369_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, J., Qin, Q., Liu, X., Ye, Q., Du, W.: Emotion-wise feature interaction analysis-based visual emotion distribution learning.\u00a0Visual Comput.\u00a01\u201310 (2023).","DOI":"10.1007\/s00371-023-02854-6"},{"issue":"11","key":"3369_CR11","doi-asserted-by":"crossref","first-page":"5693","DOI":"10.1007\/s00371-022-02690-0","volume":"39","author":"H Boughanem","year":"2023","unstructured":"Boughanem, H., Ghazouani, H., Barhoumi, W.: Multichannel convolutional neural network for human emotion recognition from in-the-wild facial expressions. Visual Comput. 39(11), 5693\u20135718 (2023)","journal-title":"Visual Comput."},{"issue":"5","key":"3369_CR12","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1007\/s00371-022-02472-8","volume":"39","author":"H Yang","year":"2023","unstructured":"Yang, H., Fan, Y., Lv, G., Liu, S., Guo, Z.: Exploiting emotional concepts for image emotion recognition. Visual Comput. 39(5), 2177\u20132190 (2023)","journal-title":"Visual Comput."},{"issue":"8","key":"3369_CR13","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1007\/s00371-020-01988-1","volume":"37","author":"RJR Kumar","year":"2021","unstructured":"Kumar, R.J.R., Sundaram, M., Arumugam, N.: Facial emotion recognition using subband selective multilevel stationary wavelet gradient transform and fuzzy support vector machine. Visual Comput. 37(8), 2315\u20132329 (2021)","journal-title":"Visual Comput."},{"key":"3369_CR14","first-page":"1","volume":"2022","author":"S Zhang","year":"2022","unstructured":"Zhang, S., Li, C.: Research on feature fusion speech emotion recognition technology for smart teaching. Mobile Info. Syst. 2022, 1\u20139 (2022)","journal-title":"Mobile Info. Syst."},{"key":"3369_CR15","doi-asserted-by":"crossref","first-page":"41273","DOI":"10.1109\/ACCESS.2019.2907327","volume":"7","author":"J-H Kim","year":"2019","unstructured":"Kim, J.-H., Kim, B.-G., Roy, P.P., Jeong, D.-M.: Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access. 7, 41273\u201341285 (2019)","journal-title":"IEEE Access."},{"key":"3369_CR16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compedu.2019.103797","volume":"148","author":"G Tongu\u00e7","year":"2020","unstructured":"Tongu\u00e7, G., Ozayd\u0131n Ozkara, B.: Automatic recognition of student emotions from facial expressions during a lecture. Comput. Education. 148, 103797 (2020)","journal-title":"Comput. Education."},{"key":"3369_CR17","doi-asserted-by":"crossref","DOI":"10.1016\/j.micpro.2021.103834","volume":"82","author":"D Lakshmi","year":"2021","unstructured":"Lakshmi, D., Ponnusamy, R.: Facial emotion recognition using modified hog and LBP features with deep stacked autoencoders. Microprocess. Microsyst. 82, 103834 (2021)","journal-title":"Microprocess. Microsyst."},{"key":"3369_CR18","volume":"112","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Zeng, F., Liu, S., Zeng, B.: OAENet: Oriented attention ensemble for accurate facial expression recognition. Patt. Recogn. 112, 107694 (2021)","journal-title":"Patt. Recogn."},{"key":"3369_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3031835","volume":"70","author":"K Mohan","year":"2021","unstructured":"Mohan, K., Seal, A., Krejcar, O., Yazidi, A.: Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks. IEEE Trans. Instrum Measur. 70, 1\u201312 (2021)","journal-title":"IEEE Trans. Instrum Measur."},{"key":"3369_CR20","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.neucom.2020.06.014","volume":"411","author":"J Li","year":"2020","unstructured":"Li, J., Jin, K., Zhou, D., Kubota, N., Ju, Z.: Attention mechanism-based CNN for facial expression recognition. Neurocomput. 411, 340\u2013350 (2020)","journal-title":"Neurocomput."},{"key":"3369_CR21","doi-asserted-by":"crossref","first-page":"159081","DOI":"10.1109\/ACCESS.2019.2949741","volume":"7","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Jolfaei, A., Alazab, M.: A face emotion recognition method using convolutional neural network and image edge computing. IEEE Access. 7, 159081\u2013159089 (2019)","journal-title":"IEEE Access."},{"key":"3369_CR22","doi-asserted-by":"crossref","first-page":"32297","DOI":"10.1109\/ACCESS.2019.2901521","volume":"7","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Pan, X., Cui, Y., Zhao, X., Liu, L.: Learning affective video features for facial expression recognition via hybrid deep learning. IEEE Access. 7, 32297\u201332304 (2019)","journal-title":"IEEE Access."},{"key":"3369_CR23","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s00371-019-01660-3","volume":"36","author":"X Zhu","year":"2019","unstructured":"Zhu, X., Chen, Z.: Dual-modality spatiotemporal feature learning for spontaneous facial expression recognition in e-learning using hybrid deep neural network. Visual Comput. 36, 743\u2013755 (2019)","journal-title":"Visual Comput."},{"key":"3369_CR24","first-page":"1819","volume":"15","author":"R Singh","year":"2023","unstructured":"Singh, R., Saurav, S., Kumar, T., Saini, R., Vohra, A., Singh, S.: Facial expression recognition in videos using hybrid CNN & CONVLSTM. Int. J. Inf. Tech. 15, 1819\u20131830 (2023)","journal-title":"Int. J. Inf. Tech."},{"key":"3369_CR25","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1080\/01587919.2018.1553559","volume":"40","author":"C Hughes","year":"2018","unstructured":"Hughes, C., Costley, J., Lange, C.: The effects of multimedia video lectures on extraneous load. Distance Educ. 40, 54\u201375 (2018)","journal-title":"Distance Educ."},{"key":"3369_CR26","volume":"237","author":"J Wei","year":"2024","unstructured":"Wei, J., Hu, G., Yang, X., Luu, A.T., Dong, Y.: Learning facial expression and body gesture visual information for video emotion recognition. Expert Syst. Appl. 237, 121419 (2024)","journal-title":"Expert Syst. Appl."},{"key":"3369_CR27","first-page":"3560","volume":"80","author":"K Sarvakar","year":"2023","unstructured":"Sarvakar, K., Senkamalavalli, R., Raghavendra, S., Santosh Kumar, J., Manjunath, R., Jaiswal, S.: Facial emotion recognition using convolutional neural networks. Mater Today: Proceed. 80, 3560\u20133564 (2023)","journal-title":"Mater Today: Proceed."},{"key":"3369_CR28","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.3390\/ijerph20021400","volume":"20","author":"S Zhou","year":"2023","unstructured":"Zhou, S., Wu, X., Jiang, F., Huang, Q., Huang, C.: Emotion recognition from large-scale video clips with cross-attention and hybrid feature weighting neural networks. Int. J. Environ. Res. Public Health 20, 1400 (2023)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"3369_CR29","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.aej.2022.08.003","volume":"63","author":"L Yan","year":"2023","unstructured":"Yan, L., Shi, Y., Wei, M., Wu, Y.: Multi-feature fusing local directional ternary pattern for facial expressions signal recognition based on video communication system. Alexandria Eng. J. 63, 307\u2013320 (2023)","journal-title":"Alexandria Eng. J."},{"key":"3369_CR30","unstructured":"Li, C., Li, X., Wang, X., Huang, D., Liu, Z., Liao, L.: FG-AGR: Fine-grained associative graph representation for facial expression recognition in the wild. IEEE Trans. Circuits Syst. Video Tech. 1\u20131 (2023)"},{"key":"3369_CR31","doi-asserted-by":"crossref","first-page":"104418","DOI":"10.1016\/j.bspc.2022.104418","volume":"81","author":"F Di Luzio","year":"2023","unstructured":"Di Luzio, F., Rosato, A., Panella, M.: A randomized deep neural network for emotion recognition with landmarks detection. Biomed. Signal Process. Control 81, 104418 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"3369_CR32","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.3390\/s22093366","volume":"22","author":"RR Devaram","year":"2022","unstructured":"Devaram, R.R., Beraldo, G., De Benedictis, R., Mongiov\u00ec, M., Cesta, A.: Lemon: a lightweight facial emotion recognition system for assistive robotics based on dilated residual convolutional neural networks. Sensors. 22, 3366 (2022)","journal-title":"Sensors."},{"key":"3369_CR33","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3390\/fi14060177","volume":"14","author":"C Hou","year":"2022","unstructured":"Hou, C., Ai, J., Lin, Y., Guan, C., Li, J., Zhu, W.: Evaluation of online teaching quality based on facial expression recognition. Future Internet. 14, 177 (2022)","journal-title":"Future Internet."},{"key":"3369_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/5570870","volume":"2021","author":"YK Bhatti","year":"2021","unstructured":"Bhatti, Y.K., Jamil, A., Nida, N., Yousaf, M.H., Viriri, S., Velastin, S.A.: Facial expression recognition of instructor using deep features and extreme learning machine. Comput. Intell. Neurosci. 2021, 1\u201317 (2021)","journal-title":"Comput. Intell. Neurosci."},{"key":"3369_CR35","first-page":"1240","volume":"11","author":"D Duki\u0107","year":"2022","unstructured":"Duki\u0107, D., Sovic Krzic, A.: Real-time facial expression recognition using deep learning with application in the active classroom environment. Electr. 11, 1240 (2022)","journal-title":"Electr."},{"key":"3369_CR36","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1016\/j.aej.2021.09.066","volume":"61","author":"Y Nan","year":"2022","unstructured":"Nan, Y., Ju, J., Hua, Q., Zhang, H., Wang, B.: A-MobileNet: an approach of facial expression recognition. Alexandria Eng. J. 61, 4435\u20134444 (2022)","journal-title":"Alexandria Eng. J."},{"key":"3369_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102919","volume":"70","author":"D Muduli","year":"2021","unstructured":"Muduli, D., Dash, R., Majhi, B.: Fast discrete curvelet transform and modified PSO based improved evolutionary extreme learning machine for breast cancer detection. Biomed. Signal Process. Control 70, 102919 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"3369_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105437","volume":"145","author":"S Chattopadhyay","year":"2022","unstructured":"Chattopadhyay, S., Dey, A., Singh, P.K., Sarkar, R.: DRDA-net: dense residual dual-shuffle attention network for breast cancer classification using histopathological images. Comput. Biology Med. 145, 105437 (2022)","journal-title":"Comput. Biology Med."},{"key":"3369_CR39","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.3390\/diagnostics12112632","volume":"12","author":"X Wang","year":"2022","unstructured":"Wang, X., Xin, J., Wang, Z., Li, C., Wang, Z.: An evolving hypergraph convolutional network for the diagnosis of alzheimer\u2019s disease. Diagnostics. 12, 2632 (2022)","journal-title":"Diagnostics."},{"key":"3369_CR40","doi-asserted-by":"crossref","first-page":"103300","DOI":"10.1016\/j.engappai.2019.103300","volume":"87","author":"W Zhao","year":"2023","unstructured":"Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng. Appl. Artificial Intell. 87, 103300 (2020)","journal-title":"Eng. Appl. Artificial Intell."},{"key":"3369_CR41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/8808283","volume":"2022","author":"AA Pise","year":"2022","unstructured":"Pise, A.A., Vadapalli, H., Sanders, I.: Estimation of learning affects experienced by learners: an approach using relational reasoning and adaptive mapping. Wirel. Commun. Mobile Comput. 2022, 1\u201314 (2022)","journal-title":"Wirel. Commun. Mobile Comput."},{"key":"3369_CR42","unstructured":"https:\/\/github.com\/AI4Bharat\/NPTEL2020-Indian-English-Speech Dataset\/blob\/master\/README.md"},{"key":"3369_CR43","doi-asserted-by":"crossref","first-page":"5543","DOI":"10.1007\/s10489-020-02125-0","volume":"51","author":"S Saurav","year":"2021","unstructured":"Saurav, S., Saini, R., Singh, S.: EmNet: a deep integrated convolutional neural network for facial emotion recognition in the wild. Appl. Intell. 51, 5543\u20135570 (2021)","journal-title":"Appl. Intell."},{"issue":"4","key":"3369_CR44","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.3390\/s20041087","volume":"20","author":"MN Riaz","year":"2020","unstructured":"Riaz, M.N., Shen, Y., Sohail, M., Guo, M.: Exnet: an efficient approach for emotion recognition in the wild. Sensors 20(4), 1087 (2020)","journal-title":"Sensors"},{"issue":"3","key":"3369_CR45","doi-asserted-by":"crossref","first-page":"870","DOI":"10.3390\/s20030870","volume":"20","author":"Y Guo","year":"2020","unstructured":"Guo, Y., Xia, Y., Wang, J., Yu, H., Chen, R.C.: Real-time facial affective computing on mobile devices. Sensors 20(3), 870 (2020)","journal-title":"Sensors"},{"key":"3369_CR46","doi-asserted-by":"crossref","first-page":"104367","DOI":"10.1109\/ACCESS.2021.3099075","volume":"9","author":"J Kim","year":"2021","unstructured":"Kim, J., Kang, J.K., Kim, Y.: A resource efficient integer-arithmetic-only FPGA-based CNN accelerator for real-time facial emotion recognition. IEEE Access 9, 104367\u2013104381 (2021)","journal-title":"IEEE Access"},{"key":"3369_CR47","doi-asserted-by":"crossref","unstructured":"AlEisa, H. N., Alrowais, F., Negm, N., Almalki, N., Khalid, M., Marzouk, R., Alneil, A. A.: Henry gas solubility optimization with deep learning based facial emotion recognition for human computer interface.\u00a0IEEE Access (2023).","DOI":"10.1109\/ACCESS.2023.3284457"},{"issue":"5","key":"3369_CR48","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1080\/03772063.2021.1902868","volume":"69","author":"PM Ashok Kumar","year":"2023","unstructured":"Ashok Kumar, P.M., Maddala, J.B., Martin Sagayam, K.: Enhanced facial emotion recognition by optimal descriptor selection with neural network. IETE J. Res. 69(5), 2595\u20132614 (2023)","journal-title":"IETE J. Res."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03369-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03369-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03369-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:36:14Z","timestamp":1738586174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03369-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3369"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03369-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]},"assertion":[{"value":"10 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All applicable institutional and\/or national guidelines for the care and use of animals were followed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statement of animal and human rights"}},{"value":"For this type of analysis formal consent is not needed.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}