Amita U. Dessai^*, Hassanali G. Virani

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710 - 13 September 2024

Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >

J. Sheril Angel¹, A. Diana Andrushia^1,*, T. Mary Neebha¹, Oussama Accouche², Louai Saker², N. Anand³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2427-2448, 2024, DOI:10.32604/cmc.2024.047326 - 15 May 2024

Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial… More >

Chili Li¹, Xinxin Zhao², Ziwen Pan³, Ting Yi⁴, Long Qian^5,6,*

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 161-175, 2024, DOI:10.32604/ijmhp.2023.029896 - 08 March 2024

Abstract While a plethora of studies has been conducted to explore demotivation and its impact on mental health in second language (L2) education, scanty research focuses on demotivation in L2 speaking learning. Particularly, little research explores the measures to quantify L2 speaking demotivation. The present two-phase study attempts to develop and validate an English Speaking Demotivation Scale (ESDS). To this end, an independent sample of 207 Chinese tertiary learners of English as a Foreign Language (EFL) participated in the development phase, and another group of 188 Chinese EFL learners was recruited for the validation of the… More >

Hasan Han¹, Oguzcan Karadeniz¹, Tugba Dalyan^2,*, Elena Battini Sonmez², Baykal Sarioglu¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1209-1224, 2023, DOI:10.32604/iasc.2023.030674 - 29 April 2023

Abstract Robot companions will soon be part of our everyday life and students in the engineering faculty must be trained to design, build, and interact with them. The two affordable robots presented in this paper have been designed and constructed by two undergraduate students; one artificial agent is based on the Nvidia Jetson Nano development board and the other one on a remote computer system. Moreover, the robots have been refined with an empathetic system, to make them more user-friendly. Since automatic facial expression recognition skills is a necessary pre-processing step for acknowledging emotions, this paper More >

Ali Alqazzaz¹, Mohammad Tabrez Quasim^1,*, Mohammed Mujib Alshahrani¹, Ibrahim Alrashdi², Mohammad Ayoub Khan¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 551-562, 2023, DOI:10.32604/csse.2023.031048 - 20 January 2023

Abstract Facebook, Twitter, Instagram, and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts, posts, comments, images, and videos that express moods, sentiments, and feelings. With this, it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes. However, the analysis of depression based on social media has gained widespread acceptance worldwide, other verticals still have yet to be discovered. The depression analysis uses Twitter data from a publicly available web source in this work. To assess the accuracy of More >

Saiyed Umer^1,*, Ranjeet Kumar Rout², Shailendra Tiwari³, Ahmad Ali AlZubi⁴, Jazem Mutared Alanazi⁴, Kulakov Yurii⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1165-1185, 2023, DOI:10.32604/cmes.2022.023312 - 27 October 2022

Abstract A deep fusion model is proposed for facial expression-based human-computer Interaction system. Initially, image preprocessing, i.e., the extraction of the facial region from the input image is utilized. Thereafter, the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial regions. To prevent overfitting, in-depth features of facial images are extracted and assigned to the proposed convolutional neural network (CNN) models. Various CNN models are then trained. Finally, the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions, i.e., fear, More >

Jodi L. Feinberg¹, Peter Sheng², Stephanie Pena², Adam J. Small¹, Susanna Wendelboe¹, Katlyn Nemani³, Vikram Agrawal⁴, Dan G. Halpern^1,*

Congenital Heart Disease, Vol.17, No.5, pp. 519-531, 2022, DOI:10.32604/chd.2022.024174 - 06 September 2022

Abstract Background: Adults with congenital heart disease (ACHD) have increased prevalence of mood and anxiety disorders. There are limited data regarding the influence of the COVID-19 pandemic on the mental health and health behaviors of these patients. Objective: The purpose is to evaluate the perceptions, emotions, and health behaviors of ACHD patients during the COVID-19 pandemic. Methods: In this cross-sectional study of ACHD patients, we administered surveys evaluating self-reported emotions, perceptions and health behaviors. Logistic regressions were performed to determine the adjusted odds of displaying each perception, emotion and health behavior based on predictor variables. Results: Ninety-seven patients… More > Graphic Abstract

Abdullah A Asiri¹, Akhtar Badshah², Fazal Muhammad^3,*, Hassan A Alshamrani¹, Khalil Ullah⁴, Khalaf A Alshamrani¹, Samar Alqhtani⁵, Muhammad Irfan⁶, Hanan Talal Halawani⁷, Khlood M Mehdar⁸

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156 - 28 July 2022

Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response… More >

Woo Hyun Park¹, Isma Farah Siddiqui², Dong Ryeol Shin¹, Nawab Muhammad Faseeh Qureshi^3,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2985-3001, 2022, DOI:10.32604/cmc.2022.028241 - 16 June 2022

Abstract For the last couple years, governments and health authorities worldwide have been focused on addressing the Covid-19 pandemic; for example, governments have implemented countermeasures, such as quarantining, pushing vaccine shots to minimize local spread, investigating and analyzing the virus’ characteristics, and conducting epidemiological investigations through patient management and tracers. Therefore, researchers worldwide require funding to achieve these goals. Furthermore, there is a need for documentation to investigate and trace disease characteristics. However, it is time consuming and resource intensive to work with documents comprising many types of unstructured data. Therefore, in this study, natural language… More >

Rongrong Ni¹, Xiaofeng Liu^1,*, Yizhou Chen¹, Xu Zhou¹, Huili Cai¹, Loo Chu Kiong²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 149-164, 2022, DOI:10.32604/iasc.2022.026813 - 15 April 2022

Abstract Lonely older adults and persons restricted in movements are apt to cause negative emotions, which is harmful to their mental health. A humanoid robot with audiovisual interactions is presented, which can correspondingly output positive facial expressions to relieve human's negative facial expressions. The negative emotions are identified through an attention-enhanced facial expression recognition (FER) network. The network is firstly trained on MMEW macro-and micro-expression databases to discover expression-related features. Then, macro-expression recognition tasks are performed by fine-tuning the trained models on several benchmarking FER databases, including CK+ and Oulu-CASIA. A transformer network is introduced to More >