Sander D. Models of emotion. In: The Cambridge handbook of human affective neuroscience. Cambridge: Cambridge University Press; 2013. p. 5–56.

Munezero M, Montero CS, Sutinen E, Pajunen J. Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Trans Affect Comput. 2014;5(2):101–11. https://doi.org/10.1109/TAFFC.2014.2317187.

Article  Google Scholar 

Saberi B, Saad S. Sentiment analysis or opinion mining: a review. Int J Adv Sci Eng Inf Technol. 2017;7(5):1660–6.

Article  Google Scholar 

Gunes H, Pantic M. Automatic, dimensional and continuous emotion recognition. Int J Synth Emot. 2010;1(1):68–99. https://doi.org/10.4018/jse.2010101605.

Article  Google Scholar 

Tanko D, Dogan S, Demir FB, Baygin M, Sahin SE, Tuncer T. Shoelace pattern-based speech emotion recognition of the lecturers in distance education: Shoepat23. Appl Acoust. 2022;190:108637. https://doi.org/10.1016/j.apacoust.2022.108637.

Article  Google Scholar 

Manohare M, Rajasekar E, Parida M. Electroencephalography based classification of emotions associated with road traffic noise using gradient boosting algorithm. Appl Acoust. 2023;206:109306. https://doi.org/10.1016/j.apacoust.2023.109306.

Article  Google Scholar 

Guo C, Lin S, Huang Z, Yao Y. Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM. Health Inf Sci Syst. 2022;10(1):15. https://doi.org/10.1007/s13755-022-00184-w.

Article  Google Scholar 

Gautam C, Seeja K. Facial emotion recognition using handcrafted features and CNN. Procedia Comput Sci. 2023;218:1295–303. https://doi.org/10.1016/j.procs.2023.01.108.

Article  Google Scholar 

Lei Y, Cao H. Audio-visual emotion recognition with preference learning based on intended and multi-modal perceived labels. IEEE Trans Affect Comput. 2023;14(4):2954–69. https://doi.org/10.1109/TAFFC.2023.3234777.

Article  Google Scholar 

Jerritta S, Murugappan M, Nagarajan R, Wan K. Physiological signals based human emotion recognition: a review. In: 2011 IEEE 7th international colloquium on signal processing and its applications. IEEE; 2011, p. 410–5. https://doi.org/10.1109/CSPA.2011.5759912

Murugappan M, Ramachandran N, Sazali Y, et al. Classification of human emotion from EEG using discrete wavelet transform. J Biomed Sci Eng. 2010;3(04):390.

Article  Google Scholar 

Luo L, Huang X, Xiao Y, Chen R, Yu E, Yuan Q, et al. Facial emotion perception and recognition deficits in acute ischemic stroke. J Clin Neurosci. 2022;106:219–25. https://doi.org/10.1016/j.jocn.2022.10.002.

Article  Google Scholar 

Pace-Schott EF, Amole MC, Aue T, Balconi M, Bylsma LM, Critchley H, et al. Physiological feelings. Neurosci Biobehav Rev. 2019;103:267–304. https://doi.org/10.1016/j.neubiorev.2019.05.002.

Article  Google Scholar 

Wen W, Liu G, Cheng N, Wei J, Shangguan P, Huang W. Emotion recognition based on multi-variant correlation of physiological signals. IEEE Trans Affect Comput. 2014;5(2):126–40. https://doi.org/10.1109/TAFFC.2014.2327617.

Article  Google Scholar 

Caruelle D, Gustafsson A, Shams P, Lervik-Olsen L. The use of electrodermal activity (EDA) measurement to understand consumer emotions—a literature review and a call for action. J Bus Res. 2019;104:146–60. https://doi.org/10.1016/j.jbusres.2019.06.041.

Article  Google Scholar 

Hui L, Ting LH, See SL, Chan PY. Use of electroglottograph (EGG) to find a relationship between pitch, emotion and personality. Procedia Manuf. 2015;3:1926–31. https://doi.org/10.1016/j.promfg.2015.07.236.

Article  Google Scholar 

Segerstrom SC, Smith GT. Personality and coping: individual differences in responses to emotion. Annu Rev Psychol. 2019;70(1):651–71. https://doi.org/10.1146/annurev-psych-010418-102917.

Article  Google Scholar 

Alswaidan N, Menai MEB. A survey of state-of-the-art approaches for emotion recognition in text. Knowl Inf Syst. 2020;62(8):2937–87. https://doi.org/10.1007/s10115-020-01449-0.

Article  Google Scholar 

Bazgir O, Mohammadi Z, Habibi SAH. Emotion recognition with machine learning using eeg signals. In: 2018 25th National and 3rd international iranian conference on biomedical engineering (ICBME). IEEE. 2018, p. 1–5. arXiv:1903.07272

Li M, Xu H, Liu X, Lu S. Emotion recognition from multichannel EEG signals using k-nearest neighbor classification. Technol Health Care. 2018;26(S1):509–19. https://doi.org/10.3233/THC-174836.

Article  Google Scholar 

Dong G, Liu H, editors. Feature engineering for machine learning and data analytics. Boca Raton: CRC Press; 2018.

Google Scholar 

Abgeena A, Garg S. S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram. Health Inf Sci Syst. 2023;11(1):40. https://doi.org/10.1007/s13755-023-00242-x.

Article  Google Scholar 

Zou C, Deng Z, He B, Yan M, Wu J, Zhu Z. Emotion classification with multi-modal physiological signals using multi-attention-based neural network. Cognit Comput Syst. 2024. https://doi.org/10.1049/ccs2.12107.

Article  Google Scholar 

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). IEEE; 2016, p. 352–9. https://doi.org/10.1109/BIBM.2016.7822545

Zhang J, Yin Z, Chen P, Nichele S. Emotion recognition using multi-modal data and machine learning techniques: a tutorial and review. Inf Fusion. 2020;59:103–26. https://doi.org/10.1016/j.inffus.2020.01.011.

Article  Google Scholar 

Sravani M, Murthy MK, Muppidi S. Utilizing visual geometry group (VGG16) and Inceptionv3 convolutional neural network (CNN) models for accurate diagnosis of lung cancer: an artificial intelligence (AI)-based approach. Multimed Tools Appl. 2024;83(19):57477–94. https://doi.org/10.1007/s11042-023-17807-3.

Article  Google Scholar 

Ahadit AB, Jatoth RK. A novel multi-feature fusion deep neural network using hog and VGG-face for facial expression classification. Mach Vis Appl. 2022;33(4):55. https://doi.org/10.1007/s00138-022-01304-y.

Article  Google Scholar 

Wen L, Li X, Li X, Gao L. A new transfer learning based on VGG-19 network for fault diagnosis. In: 2019 IEEE 23rd International conference on computer supported cooperative work in design (CSCWD). 2019, p. 205–9. https://doi.org/10.1109/CSCWD.2019.8791884

Majib MS, Rahman MM, Sazzad TS, Khan NI, Dey SK. VGG-SCNET: a VGG net-based deep learning framework for brain tumor detection on MRI images. IEEE Access. 2021;9:116942–52. https://doi.org/10.1109/ACCESS.2021.3105874.

Article  Google Scholar 

Kusuma GP, Jonathan J, Lim A. Emotion recognition on FER-2013 face images using fine-tuned VGG-16. Adv Sci Technol Eng Syst J. 2020;5(6):315–22. https://doi.org/10.25046/aj050638.

Article  Google Scholar 

Vignesh S, Savithadevi M, Sridevi M, Sridhar R. A novel facial emotion recognition model using segmentation VGG-19 architecture. Int J Inf Technol. 2023;15(4):1777–87. https://doi.org/10.1007/s41870-023-01184-z.

Article  Google Scholar 

Wang F, Geng S, Zhang D, Zhou M, Nian W, Li L. A fine-grained classification method of thangka image based on senet. In: 2022 International conference on cyberworlds (CW). IEEE; 2022, p. 23–30. https://doi.org/10.1109/CW55638.2022.00013.

Pan W, Sun Y, Cheng R, Cao S. A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion. Meas Sci Technol. 2023;34(12):125117. https://doi.org/10.1088/1361-6501/acf335.

Article  Google Scholar 

Wright L. New deep learning optimizer, ranger: synergistic combination of Radam+ lookahead for the best of both. Github. 2019:8. https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer

Sannasi Chakravarthy S, Bharanidharan N, Vinothini C, Vinoth Kumar V, Mahesh T, Guluwadi S. Adaptive mish activation and ranger optimizer-based sea-resnet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images. BMC Med Imaging. 2024;24(1):206. https://doi.org/10.1186/s12880-024-01394-2.

Article  Google Scholar 

Picard RW, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell. 2001;23(10):1175–91. https://doi.org/10.1109/34.954607.

Article  Google Scholar 

Li R, Ren C, Zhang S, Yang Y, Zhao Q, Hou K, et al. STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition. Health Inf Sci Syst. 2023;11(1):25. https://doi.org/10.1007/s13755-023-00226-x.

Article  Google Scholar 

Ganapathy N, Veeranki YR, Kumar H, Swaminathan R. Emotion recognition using electrodermal activity signals and multiscale deep convolutional neural network. J Med Syst. 2021;45(4):49. https://doi.org/10.1007/s10916-020-01676-6.

Article  Google Scholar 

Kim J, André E. Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell. 2008;30(12):2067–83. https://doi.org/10.1109/TPAMI.2008.26.

Article  Google Scholar 

Raheel A, Majid M, Alnowami M, Anwar SM. Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia. Sensors. 2020;20(14):4037. https://doi.org/10.3390/s20144037.

Article  Google Scholar 

Liao J, Zhong Q, Zhu Y, Cai D. Multimodal physiological signal emotion recognition based on convolutional recurrent neural network. IOP Conf Ser. 2020;782:032005. https://doi.org/10.1088/1757-899X/782/3/032005.

Article  Google Scholar 

Wu Y, Daoudi M, Amad A. Transformer-based self-supervised multimodal representation learning for wearable emotion recognition. IEEE Trans Affect Comput. 2023;15(1):157–72. https://doi.org/10.1109/TAFFC.2023.3263907.

Article  Google Scholar 

Ali M, Al Machot F, Haj Mosa A, Jdeed M, Al Machot E, Kyamakya K. A globally generalized emotion recognition system involving different physiological signals. Sensors. 2018;18(6):1905. https://doi.org/10.3390/s18061905.

Article  Google Scholar 

Sun J, Portilla J, Otero A. Negative emotion recognition based on physiological signals using a CNN-LSTM model. In: 2024 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE; 2024, p. 3736–41. https://doi.org/10.1109/BIBM62325.2024.10822762.