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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors

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Dynamics of Information Systems (DIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14321))

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Abstract

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition.

S. Nahavandi—Associate Deputy Vice-Chancellor Research.

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References

  1. Mehrabian, A.: Communication without words. In: Communication Theory, pp. 193–200. Routledge (2017)

    Google Scholar 

  2. Valstar, M.F., Mehu, M., Jiang, B., Pantic, M., Scherer, K.: Meta-analysis of the first facial expression recognition challenge. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42, 966–979 (2012)

    Google Scholar 

  3. Lisetti, C.L., Schiano, D.J.: Automatic facial expression interpretation: where human-computer interaction, artificial intelligence and cognitive science intersect. Pragmat. Cogn. 8, 185–235 (2000)

    Article  Google Scholar 

  4. Sultan Zia, M., Hussain, M., Arfan Jaffar, M.: A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier. Multimed. Tools Appl. 77, 25537–25567 (2018)

    Article  Google Scholar 

  5. Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1424–1445 (2000)

    Article  Google Scholar 

  6. Bassili, J.N.: Facial motion in the perception of faces and of emotional expression. J. Exp. Psychol. Hum. Percept. Perform. 4, 373 (1978)

    Article  Google Scholar 

  7. Vasanth, P., Nataraj, K.: Facial expression recognition using SVM classifier. Indon. J. Electr. Eng. Inform. (IJEEI) 3, 16–20 (2015)

    Google Scholar 

  8. Abdulrahman, M., Eleyan, A.: Facial expression recognition using support vector machines. In: 2015 23nd Signal Processing and Communications Applications Conference (SIU), pp. 276–279. IEEE (2015)

    Google Scholar 

  9. Xu, X., Quan, C., Ren, F.: Facial expression recognition based on Gabor wavelet transform and histogram of oriented gradients. In: 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 2117–2122. IEEE (2015)

    Google Scholar 

  10. Naghsh-Nilchi, A.R., Roshanzamir, M.: An efficient algorithm for motion detection based facial expression recognition using optical flow. Proc. World Acad. Sci. Eng. Technol. 20, 23–28 (2006)

    Google Scholar 

  11. Dhavalikar, A.S., Kulkarni, R.K.: Face detection and facial expression recognition system. In: 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1–7 (2014)

    Google Scholar 

  12. Roshanzamir, M., Naghsh Nilchi, A.R., Roshanzamir, M.: A new fuzzy rule-based approach for automatic facial expression recognition. 1st National Conference on Soft Computing. Undefined (1394)

    Google Scholar 

  13. Liliana, D.Y., Basaruddin, T., Widyanto, M.R., Oriza, I.I.D.: Fuzzy emotion: a natural approach to automatic facial expression recognition from psychological perspective using fuzzy system. Cogn. Process. 20, 391–403 (2019)

    Article  Google Scholar 

  14. Kirana, K.C., Wibawanto, S., Herwanto, H.W.: Facial emotion recognition based on viola-jones algorithm in the learning environment. In: 2018 International Seminar on Application for Technology of Information and Communication, pp. 406–410. IEEE (2018)

    Google Scholar 

  15. Happy, S., Routray, A.: Robust facial expression classification using shape and appearance features. In: 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–5. IEEE (2015)

    Google Scholar 

  16. Chołoniewski, J., Chmiel, A., Sienkiewicz, J., Hołyst, J.A., Küster, D., Kappas, A.: Temporal Taylor’s scaling of facial electromyography and electrodermal activity in the course of emotional stimulation. Chaos Solitons Fractals 90, 91–100 (2016)

    Article  MathSciNet  Google Scholar 

  17. Tuncer, T., Dogan, S., Subasi, A.: A new fractal pattern feature generation function based emotion recognition method using EEG. Chaos, Solitons Fractals 144, 110671 (2021)

    Article  MathSciNet  Google Scholar 

  18. Mehta, D., Siddiqui, M.F.H., Javaid, A.Y.: Recognition of emotion intensities using machine learning algorithms: a comparative study. Sensors 19, 1897 (2019)

    Article  Google Scholar 

  19. Varma, S., Shinde, M., Chavan, S.S.: Analysis of PCA and LDA features for facial expression recognition using SVM and hmm classifiers. In: Pawar, P.M., Ronge, B.P., Balasubramaniam, R., Vibhute, A.S., Apte, S.S. (eds.) Techno-Societal 2018, pp. 109–119. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-16848-3_11

    Chapter  Google Scholar 

  20. Islam, S.M.S., et al.: Machine learning approaches for predicting hypertension and its associated factors using population-level data from three south asian countries. Front. Cardiovasc. Med. 9, 839379 (2022)

    Article  Google Scholar 

  21. Lu, Y., Wang, S., Zhao, W., Zhao, Y.: WGAN-based robust occluded facial expression recognition. IEEE Access 7, 93594–93610 (2019)

    Article  Google Scholar 

  22. Nahavandi, D., Alizadehsani, R., Khosravi, A., Acharya, U.R.: Application of artificial intelligence in wearable devices: opportunities and challenges. Comput. Methods Programs Biomed. 213, 106541 (2022)

    Article  Google Scholar 

  23. Abdellatif, D., El Moutaouakil, K., Satori, K.: Clustering and Jarque-Bera normality test to face recognition. Procedia Comput. Sci. 127, 246–255 (2018)

    Article  Google Scholar 

  24. Malekzadeh, A., Zare, A., Yaghoobi, M., Alizadehsani, R.: Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method. Big Data Cogn. Comput. 5, 78 (2021)

    Article  Google Scholar 

  25. Abdulrazaq, M.B., Mahmood, M.R., Zeebaree, S.R., Abdulwahab, M.H., Zebari, R.R., Sallow, A.B.: An analytical appraisal for supervised classifiers’ performance on facial expression recognition based on relief-F feature selection. In: Journal of Physics: Conference Series, p. 012055. IOP Publishing (2021)

    Google Scholar 

  26. Barman, A., Dutta, P.: Facial expression recognition using distance and texture signature relevant features. Appl. Soft Comput. 77, 88–105 (2019)

    Article  Google Scholar 

  27. Wang, C., Wang, S., Liang, G.: Identity-and pose-robust facial expression recognition through adversarial feature learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 238–246 (2019)

    Google Scholar 

  28. Malekzadeh, A., Zare, A., Yaghoobi, M., Kobravi, H.-R., Alizadehsani, R.: Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features. Sensors 21, 7710 (2021)

    Article  Google Scholar 

  29. Saurav, S., Singh, S., Saini, R., Yadav, M.: Facial expression recognition using improved adaptive local ternary pattern. In: Chaudhuri, B.B., Nakagawa, M., Khanna, P., Kumar, S. (eds.) Proceedings of 3rd International Conference on Computer Vision and Image Processing. AISC, vol. 1024, pp. 39–52. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9291-8_4

    Chapter  Google Scholar 

  30. Rahul, M., Shukla, R., Goyal, P.K., Siddiqui, Z.A., Yadav, V.: Gabor filter and ICA-based facial expression recognition using two-layered hidden markov model. In: Gao, X.-Z., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds.) Advances in Computational Intelligence and Communication Technology. AISC, vol. 1086, pp. 511–518. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-1275-9_42

    Chapter  Google Scholar 

  31. Rahul, M., Kohli, N., Agarwal, R.: Facial expression recognition using local multidirectional score pattern descriptor and modified hidden Markov model. Int. J. Adv. Intell. Paradig. 18, 538–551 (2021)

    Google Scholar 

  32. Gogić, I., Manhart, M., Pandžić, I.S., Ahlberg, J.: Fast facial expression recognition using local binary features and shallow neural networks. Vis. Comput. 36, 97–112 (2020)

    Article  Google Scholar 

  33. Durmuşoğlu, A., Kahraman, Y.: Face expression recognition using a combination of local binary patterns and local phase quantization. In: 2021 International Conference on Communication, Control and Information Sciences (ICCISc), pp. 1–5. IEEE (2021)

    Google Scholar 

  34. Li, Y., Mavadati, S.M., Mahoor, M.H., Zhao, Y., Ji, Q.: Measuring the intensity of spontaneous facial action units with dynamic Bayesian network. Pattern Recogn. 48, 3417–3427 (2015)

    Article  Google Scholar 

  35. Wang, L., Wang, K., Li, R.: Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition. IET Comput. Vision 9, 655–662 (2015)

    Article  Google Scholar 

  36. Roshanzamir, M., et al.: Automatic facial expression recognition in an image sequence using conditional random field. In: 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), pp. 000271–000278 (2022)

    Google Scholar 

  37. Alizadehsani, R., et al.: Diagnosis of coronary artery disease using data mining based on lab data and echo features. J. Med. Bioeng. 1 (2012)

    Google Scholar 

  38. Alizadehsani, R., et al.: Diagnosis of coronary arteries stenosis using data mining. J. Med. Signals Sens. 2, 153 (2012)

    Article  Google Scholar 

  39. Alizadehsani, R., Hosseini, M.J., Boghrati, R., Ghandeharioun, A., Khozeimeh, F., Sani, Z.A.: Exerting cost-sensitive and feature creation algorithms for coronary artery disease diagnosis. Int. J. Knowl. Discov. Bioinform. (IJKDB) 3, 59–79 (2012)

    Article  Google Scholar 

  40. Sharifrazi, D., et al.: CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering (2020)

    Google Scholar 

  41. Asgharnezhad, H., et al.: Objective evaluation of deep uncertainty predictions for COVID-19 detection. Sci. Rep. 12, 815 (2022)

    Article  Google Scholar 

  42. Roshanzamir, M., Alizadehsani, R., Roshanzamir, M., Shoeibi, A., Gorriz, J.M., Khosrave, A., Nahavandi, S.: What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors. arXiv preprint arXiv:2109.05457 (2021)

  43. Joloudari, J.H., et al.: DNN-GFE: a deep neural network model combined with global feature extractor for COVID-19 diagnosis based on CT scan images. EasyChair 2516–2314 (2021)

    Google Scholar 

  44. Ayoobi, N., et al.: Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys. 27, 104495 (2021)

    Article  Google Scholar 

  45. Javan, A.A.K., et al.: Medical images encryption based on adaptive-robust multi-mode synchronization of Chen hyper-chaotic systems. Sensors 21, 3925 (2021)

    Article  Google Scholar 

  46. Qin, S., Zhu, Z., Zou, Y., Wang, X.: Facial expression recognition based on Gabor wavelet transform and 2-channel CNN. Int. J. Wavelets Multiresolut. Inf. Process. 18, 2050003 (2020)

    Article  Google Scholar 

  47. Shoushtarian, M., et al.: Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning. PLoS One 15, e0241695 (2020)

    Article  Google Scholar 

  48. Alizadehsani, R., et al.: Model uncertainty quantification for diagnosis of each main coronary artery stenosis. Soft. Comput. 24, 10149–10160 (2020)

    Article  Google Scholar 

  49. Zangooei, M.H., Habibi, J., Alizadehsani, R.: Disease diagnosis with a hybrid method SVR using NSGA-II. Neurocomputing 136, 14–29 (2014)

    Article  Google Scholar 

  50. Byun, S.-W., Lee, S.-P.: Human emotion recognition based on the weighted integration method using image sequences and acoustic features. Multimed. Tools Appl. 1–15 (2020)

    Google Scholar 

  51. Fernandez, P.D.M., Pena, F.A.G., Ren, T.I., Cunha, A.: FERAtt: facial expression recognition with attention net. arXiv preprint arXiv:1902.03284 3 (2019)

  52. Alenazy, W.M., Alqahtani, A.S.: Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition. J. Ambient. Intell. Humaniz. Comput. 12, 1631–1646 (2021)

    Article  Google Scholar 

  53. Alexandre, G.R., Soares, J.M., Thé, G.A.P.: Systematic review of 3D facial expression recognition methods. Pattern Recogn. 100, 107108 (2020)

    Article  Google Scholar 

  54. Li, S., Deng, W.: Deep facial expression recognition: a survey. IEEE Trans. Affect. Comput. 13, 1195–1215 (2020)

    Article  Google Scholar 

  55. Turan, C., Lam, K.-M.: Histogram-based local descriptors for facial expression recognition (FER): a comprehensive study. J. Vis. Commun. Image Represent. 55, 331–341 (2018)

    Article  Google Scholar 

  56. Abdullah, S.M.S., Abdulazeez, A.M.: Facial expression recognition based on deep learning convolution neural network: a review. J. Soft Comput. Data Min. 2, 53–65 (2021)

    Google Scholar 

  57. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 94–101 (2010)

    Google Scholar 

  58. The SAGE Encyclopedia of Theory in Psychology. SAGE Publications, Inc., Thousand Oaks (2016)

    Google Scholar 

  59. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education India (2016)

    Google Scholar 

  60. Moravvej, S.V., et al.: RLMD-PA: a reinforcement learning-based myocarditis diagnosis combined with a population-based algorithm for pretraining weights. Contrast Media Mol. Imaging 2022 (2022)

    Google Scholar 

  61. Kiss, N., et al.: Comparison of the prevalence of 21 GLIM phenotypic and etiologic criteria combinations and association with 30-day outcomes in people with cancer: a retrospective observational study. Clin. Nutr. 41, 1102–1111 (2022)

    Article  Google Scholar 

  62. Joloudari, J.H., et al.: Resource allocation optimization using artificial intelligence methods in various computing paradigms: a Review. arXiv preprint arXiv:2203.12315 (2022)

  63. Alizadehsani, R., et al.: Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19. Immun. Inflamm. Dis. 10, e561 (2022)

    Article  Google Scholar 

  64. Bishop, C.M.: Pattern recognition. Mach. Learn. 128 (2006)

    Google Scholar 

  65. Alizadehsani, R., et al.: Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries. Expert. Syst. 39, e12573 (2022)

    Article  Google Scholar 

  66. Khozeimeh, F., et al.: RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance. Sci. Rep. 12, 11178 (2022)

    Article  Google Scholar 

  67. Kakhi, K., Alizadehsani, R., Kabir, H.D., Khosravi, A., Nahavandi, S., Acharya, U.R.: The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern. Biomed. Eng. 42, 749–771 (2022)

    Article  Google Scholar 

  68. Sharifrazi, D., et al.: Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques (2021)

    Google Scholar 

  69. Alizadehsani, R., et al.: Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 17, 1–24 (2021)

    Article  Google Scholar 

  70. Shoeibi, A., et al.: Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed. Signal Process. Control 73, 103417 (2022)

    Article  Google Scholar 

  71. Hassannataj Joloudari, J., et al.: GSVMA: a genetic support vector machine ANOVA method for CAD diagnosis. Front. Cardiovasc. Med. 8, 760178 (2022)

    Article  Google Scholar 

  72. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning (2014)

    Google Scholar 

  73. Gautama, T., Hulle, M.A.V.: A phase-based approach to the estimation of the optical flow field using spatial filtering. IEEE Trans. Neural Netw. 13, 1127–1136 (2002)

    Article  Google Scholar 

  74. Li, W., Hua, Y., Liangzheng, X.: Mouth detection based on interest point. In: 2007 Chinese Control Conference, pp. 610–613 (2007)

    Google Scholar 

  75. Bao, P.T., Nguyen, H., Nhan, D.: A new approach to mouth detection using neural network. In: 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009), pp. 616–619 (2009)

    Google Scholar 

  76. Wang, Q., Yang, S., Li, X.W.: A fast mouth detection algorithm based on face organs. In: 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), pp. 250–252 (2009)

    Google Scholar 

  77. Asadifard, M., Shanbezadeh, J.: Automatic adaptive center of pupil detection using face detection and cdf analysis. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, p. 3. Citeseer (2010)

    Google Scholar 

  78. Bujlow, T., Riaz, T., Pedersen, J.M.: A method for classification of network traffic based on C5.0 machine learning algorithm. In: 2012 International Conference on Computing, Networking and Communications (ICNC), pp. 237–241 (2012)

    Google Scholar 

  79. Nahavandi, S., et al.: A Comprehensive Review on Autonomous Navigation. arXiv preprint arXiv:2212.12808 (2022)

  80. Eskandarian, R., et al.: Identification of clinical features associated with mortality in COVID-19 patients. Oper. Res. Forum 4, 16 (2023)

    Article  Google Scholar 

  81. Roshanzamir, M., et al.: Quantifying uncertainty in automated detection of Alzheimer’s patients using deep neural network (2023)

    Google Scholar 

  82. Iqbal, M.S., Ahmad, W., Alizadehsani, R., Hussain, S., Rehman, R.: Breast cancer dataset, classification and detection using deep learning. In: Healthcare, p. 2395. MDPI (2022)

    Google Scholar 

  83. Blömer, J., Otto, M., Seifert, J.-P.: A new CRT-RSA algorithm secure against bellcore attacks. In: Proceedings of the 10th ACM Conference on Computer and Communications Security, pp. 311–320. Association for Computing Machinery, Washington D.C. (2003)

    Google Scholar 

  84. Nasab, R.Z., et al.: Deep Learning in spatially resolved transcriptomics: a comprehensive technical view. arXiv preprint arXiv:2210.04453 (2022)

  85. Abedini, S.S., et al.: A critical review of the impact of candidate copy number variants on autism spectrum disorders. arXiv preprint arXiv:2302.03211 (2023)

  86. Danaei, S., et al.: Myocarditis diagnosis: a method using mutual learning-based abc and reinforcement learning. In: 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), pp. 000265–000270. IEEE (2022)

    Google Scholar 

  87. Bar-Itzhack, I.Y.: REQUEST-a recursive QUEST algorithm for sequential attitude determination. J. Guid. Control. Dyn. 19, 1034–1038 (1996)

    Article  Google Scholar 

  88. Joloudari, J.H., et al.: Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol. Meas. (2022)

    Google Scholar 

  89. Kabir, H., et al.: Uncertainty aware neural network from similarity and sensitivity. arXiv preprint arXiv:2304.14925 (2023)

  90. Khalili, H., et al.: Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci. Rep. 13, 960 (2023)

    Article  Google Scholar 

  91. Lin, C.-L., Fan, C.-L.: Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. J. Asian Archit. Build. Eng. 18, 539–553 (2019)

    Article  Google Scholar 

  92. Nematollahi, M.A., et al.: Association and predictive capability of body composition and diabetes mellitus using artificial intelligence: a cohort study (2022)

    Google Scholar 

  93. Abbasi Habashi, S., Koyuncu, M., Alizadehsani, R.: A survey of COVID-19 diagnosis using routine blood tests with the aid of artificial intelligence techniques. Diagnostics 13, 1749 (2023)

    Article  Google Scholar 

  94. Karami, M., Alizadehsani, R., Argha, A., Dehzangi, I., Alinejad-Rokny, H.: Revolutionizing genomics with reinforcement learning techniques. arXiv preprint arXiv:2302.13268 (2023)

  95. Khodatars, M., et al.: Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. arXiv preprint arXiv:2007.01285 (2020)

  96. Shoeibi, A., et al.: Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: a review. arXiv preprint arXiv:2105.04881 (2021)

  97. Mahamivanan, H., et al.: Material recognition for construction quality monitoring using deep learning methods. Constr. Innov. (2023)

    Google Scholar 

  98. Khozeimeh, F., et al.: ALEC: active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease. Comput. Biol. Med. 158, 106841 (2023)

    Article  Google Scholar 

  99. Sadeghi, Z., et al.: A brief review of explainable artificial intelligence in healthcare. arXiv preprint arXiv:2304.01543 (2023)

  100. Hong, L., et al.: GAN-LSTM-3D: An efficient method for lung tumour 3D reconstruction enhanced by attention-based LSTM. CAAI Trans. Intell. Technol. (2023)

    Google Scholar 

  101. Alizadehsani, R., et al.: A data mining approach for diagnosis of coronary artery disease. Comput. Methods Programs Biomed. 111, 52–61 (2013)

    Article  Google Scholar 

  102. Alizadehsani, R., et al.: Swarm intelligence in internet of medical things: A review. Sensors 23, 1466 (2023)

    Article  Google Scholar 

  103. Joloudari, J.H., et al.: BERT-deep CNN: state of the art for sentiment analysis of COVID-19 tweets. Soc. Netw. Anal. Min. 13, 99 (2023)

    Article  Google Scholar 

  104. Nahavandi, D., Alizadehsani, R., Khosravi, A.: Integration of machine learning with wearable technologies. Handb. Hum.-Mach. Syst. 383–396 (2023)

    Google Scholar 

  105. Kiss, N., et al.: Machine learning models to predict outcomes at 30-days using Global Leadership Initiative on Malnutrition combinations with and without muscle mass in people with cancer. J. Cachexia Sarcopenia Muscle (2023)

    Google Scholar 

  106. Park, C.H., Park, H.: A comparison of generalized linear discriminant analysis algorithms. Pattern Recogn. 41, 1083–1097 (2008)

    Article  Google Scholar 

  107. Nematollahi, M.A., et al.: Body composition predicts hypertension using machine learning methods: a cohort study. Sci. Rep. 13, 6885 (2023)

    Article  Google Scholar 

  108. Khozeimeh, F., et al.: Importance of wearable health monitoring systems using IoMT; Requirements, advantages, disadvantages and challenges. In: 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), pp. 000245–000250. IEEE (2002)

    Google Scholar 

  109. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189, Fasa, Iran

    Mohamad Roshanzamir

  2. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia

    Roohallah Alizadehsani & Abbas Khosravi

  3. Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Mahdi Roshanzamir

  4. Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain

    Afshin Shoeibi

  5. Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain

    Juan M. Gorriz

  6. Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Saeid Nahavandi

  7. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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  1. Mohamad Roshanzamir
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  2. Roohallah Alizadehsani
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  8. U. Rajendra Acharya
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Correspondence to Roohallah Alizadehsani .

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  1. Jan Evangelista Purkyně University, Ústí nad Labem-město, Czech Republic

    Hossein Moosaei

  2. Charles University, Prague, Czech Republic

    Milan Hladík

  3. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Roshanzamir, M. et al. (2024). Using Data Mining Techniques to Analyze Facial Expression Motion Vectors. In: Moosaei, H., Hladík, M., Pardalos, P.M. (eds) Dynamics of Information Systems. DIS 2023. Lecture Notes in Computer Science, vol 14321. Springer, Cham. https://doi.org/10.1007/978-3-031-50320-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-50320-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50319-1

  • Online ISBN: 978-3-031-50320-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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