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  • Open Access

    ARTICLE

    Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

    Md Hasibur Rahman, Mohammed Arif Uddin, Zinnat Fowzia Ria, Rashedur M. Rahman*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1637-1666, 2025, DOI:10.32604/cmes.2024.058329 - 27 January 2025

    Abstract The rapid growth of digital data necessitates advanced natural language processing (NLP) models like BERT (Bidirectional Encoder Representations from Transformers), known for its superior performance in text classification. However, BERT’s size and computational demands limit its practicality, especially in resource-constrained settings. This research compresses the BERT base model for Bengali emotion classification through knowledge distillation (KD), pruning, and quantization techniques. Despite Bengali being the sixth most spoken language globally, NLP research in this area is limited. Our approach addresses this gap by creating an efficient BERT-based model for Bengali text. We have explored 20 combinations… More > Graphic Abstract

    Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

  • Open Access

    REVIEW

    Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

    Amjad Rehman1, Muhammad Mujahid1, Alex Elyassih1, Bayan AlGhofaily1, Saeed Ali Omer Bahaj2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 41-72, 2025, DOI:10.32604/cmc.2024.058036 - 03 January 2025

    Abstract In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a… More >

  • Open Access

    ARTICLE

    Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

    Shi Li, Didi Sun*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1069-1086, 2025, DOI:10.32604/cmc.2024.057349 - 03 January 2025

    Abstract With the rapid expansion of social media, analyzing emotions and their causes in texts has gained significant importance. Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text, facilitating a deeper understanding of expressed sentiments and their underlying reasons. This comprehension is crucial for making informed strategic decisions in various business and societal contexts. However, recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneously model extracted features and their interactions, or inconsistencies in label prediction between emotion-cause pair extraction… More >

  • Open Access

    ARTICLE

    Occluded Gait Emotion Recognition Based on Multi-Scale Suppression Graph Convolutional Network

    Yuxiang Zou1, Ning He2,*, Jiwu Sun1, Xunrui Huang1, Wenhua Wang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1255-1276, 2025, DOI:10.32604/cmc.2024.055732 - 03 January 2025

    Abstract In recent years, gait-based emotion recognition has been widely applied in the field of computer vision. However, existing gait emotion recognition methods typically rely on complete human skeleton data, and their accuracy significantly declines when the data is occluded. To enhance the accuracy of gait emotion recognition under occlusion, this paper proposes a Multi-scale Suppression Graph Convolutional Network (MS-GCN). The MS-GCN consists of three main components: Joint Interpolation Module (JI Moudle), Multi-scale Temporal Convolution Network (MS-TCN), and Suppression Graph Convolutional Network (SGCN). The JI Module completes the spatially occluded skeletal joints using the (K-Nearest Neighbors)… More >

  • Open Access

    REVIEW

    A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

    Vajratiya Vajrobol1, Geetika Jain Saxena2, Amit Pundir2, Sanjeev Singh1, Akshat Gaurav3, Savi Bansal4,5, Razaz Waheeb Attar6, Mosiur Rahman7, Brij B. Gupta7,8,9,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 49-90, 2025, DOI:10.32604/cmes.2024.056500 - 17 December 2024

    Abstract Mental health is a significant issue worldwide, and the utilization of technology to assist mental health has seen a growing trend. This aims to alleviate the workload on healthcare professionals and aid individuals. Numerous applications have been developed to support the challenges in intelligent healthcare systems. However, because mental health data is sensitive, privacy concerns have emerged. Federated learning has gotten some attention. This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems. It explores various dimensions of federated learning in mental health, such as More >

  • Open Access

    ARTICLE

    Perspectives and Challenges of Family Members in Providing Mental Support to Cancer Patients: A Qualitative Study in Beijing, China

    Wei Wang1,2, Lan Li3,*

    Psycho-Oncologie, Vol.18, No.4, pp. 257-269, 2024, DOI:10.32604/po.2024.057004 - 04 December 2024

    Abstract This study explores the perspectives and challenges faced by family members providing mental support to cancer patients in Beijing, China. The primary objective is to understand the emotional and practical roles family members undertake and the difficulties they encounter. Utilizing a qualitative research design, data were collected through semi-structured interviews with family caregivers of cancer patients. Thematic analysis revealed several key themes: the dual burden of emotional support and caregiving responsibilities, the impact on daily life and personal well-being, the role and effectiveness of external support systems, perceptions of medical staff support, and the common More >

  • Open Access

    ARTICLE

    Understanding the Link: Emotional Attention in Italian Families and Children’s Social Development

    Catalda Corvasce1, Juan Pedro Martínez-Ramón2,*, Francisco Manuel Morales-Rodríguez3, Lidia Pellicer-García4, Inmaculada Méndez2, Cecilia Ruiz-Esteban2

    International Journal of Mental Health Promotion, Vol.26, No.9, pp. 709-718, 2024, DOI:10.32604/ijmhp.2024.053599 - 20 September 2024

    Abstract Background: Emotional attention refers to the capacity to recognize and properly respond to one’s and others’ emotional states. On another note, family is a primary source of socialization that influences the development of various social skills. In another line, adolescence is a complex stage that has been associated with emotional difficulties that could be related to competences such as prosociability and inclusion. It is inferred that through the family context and the attention that is processed, a series of competencies are transmitted to the youngsters, but this relationship is still unclear. For this reason, the… More >

  • Open Access

    ARTICLE

    Emotion Detection Using ECG Signals and a Lightweight CNN Model

    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 >

  • Open Access

    ARTICLE

    A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

    Wen Jiang1,2, Mingshu Zhang1,2,*, Xu'an Wang1,3, Wei Bin1,2, Xiong Zhang1,2, Kelan Ren1,2, Facheng Yan1,2

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2161-2179, 2024, DOI:10.32604/cmc.2024.053762 - 15 August 2024

    Abstract With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different… More >

  • Open Access

    ARTICLE

    Association between Mental Health Literacy and Workplace Well-Being of Chinese Grassroots Civil Servants: The Chain Mediating Effects of Regulatory Emotional Self-Efficacy and Resilience

    Yi Tang1, Yajun Zhao2, Zihan Jin3, Shengnan Wu1,*, Zhijun Zhang4, Ju Zhou1, Ling Zhou5

    International Journal of Mental Health Promotion, Vol.26, No.7, pp. 559-568, 2024, DOI:10.32604/ijmhp.2024.050822 - 30 July 2024

    Abstract This study aimed to investigate the relationship between mental health literacy (MHL) and workplace well-being (WWB) of Chinese grassroots civil servants, with regulatory emotional self-efficacy (RESE) and resilience as mediating variables. A questionnaire survey was conducted among Chinese grassroots civil servants, with a valid sample size of 2673 after excluding missing values and conducting relevant data processing. The PROCESS was used to examine the relationship between MHL, RESE, resilience, and WWB. The study found that MHL among grassroots civil servants was positively and significantly correlated with WWB (r = 0.73, p < 0.01). RESE partially mediated… More >

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