Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (108)
  • Open Access

    ARTICLE

    Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

    Sherly Alphonse*, Harshit Verma

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2067-2086, 2024, DOI:10.32604/cmc.2024.046070

    Abstract Although lots of research has been done in recognizing facial expressions, there is still a need to increase the accuracy of facial expression recognition, particularly under uncontrolled situations. The use of Local Directional Patterns (LDP), which has good characteristics for emotion detection has yielded encouraging results. An innovative end-to-end learnable High Response-based Local Directional Pattern (HR-LDP) network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work. By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions, this network considerably More >

  • Open Access

    ARTICLE

    Internet Use and Mental Health among Older Adults in China: Beneficial for Those Who Lack of Intergenerational Emotional Support or Suffering from Chronic Diseases?

    Yuxin Wang1,2,*, Jia Shi1,2

    International Journal of Mental Health Promotion, Vol.26, No.1, pp. 69-80, 2024, DOI:10.32604/ijmhp.2023.044641

    Abstract In the 21st century, the rapid growth of the Internet has presented a significant avenue for China to respond actively to the aging population and promote the “Healthy China” strategy in an orderly manner. This study uses panel data from the China Health and Retirement Longitudinal Study (CHARLS) to empirically investigate the influence of Internet use on the mental health of older adults, particularly those who lack intergenerational emotional support and suffer from chronic diseases. This study employs a multi-period difference-in-differences (DID) method and a two-stage instrumental variable approach to address the endogenous problem. Results… More >

  • Open Access

    ARTICLE

    Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

    Lujuan Deng, Boyi Liu*, Zuhe Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1157-1170, 2024, DOI:10.32604/cmc.2023.042150

    Abstract Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data. Concatenating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method. This fusion method does not utilize the correlation information between modalities. To solve this problem, this paper proposes a model based on a multi-head attention mechanism. First, after preprocessing the original data. Then, the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence. Next, the input coding sequence is fed into… More >

  • Open Access

    ARTICLE

    Evaluation du burnout du personnel soignant de l’Institut de Cancérologie d’Akanda

    A. C. Filankembo Kava*, B. C. Ndjengue Bengono, P. L. Nzamba Bissielou, C. Nziengui Tirogo, A. Kabena, T. Mpami, E. Belembaogo

    Psycho-Oncologie, Vol.17, No.4, pp. 267-273, 2023, DOI:10.32604/po.2023.044512

    Abstract Objectif. Le syndrome d’épuisement professionnel est fréquent chez les travailleurs de la santé en oncologie. Non diagnostiqué et incorrectement pris en charge, le burnout peut avoir un impact négatif sur le rendement professionnel. L’Institut de Cancérologie d’Akanda (ICA) est un centre hospitalier ultra-moderne qui se veut une référence en matière de prise en charge du cancer en Afrique centrale. L’objectif de l’étude est de mesurer la fréquence du burnout au sein du personnel soignant de l’Institut de Cancérologie d’Akanda et d’évaluer les principaux facteurs de risque. Patients et méthodes. Nous avons mené une étude transversale à l’Institut… More >

  • Open Access

    ARTICLE

    Improved Speech Emotion Recognition Focusing on High-Level Data Representations and Swift Feature Extraction Calculation

    Akmalbek Abdusalomov1, Alpamis Kutlimuratov2, Rashid Nasimov3, Taeg Keun Whangbo1,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2915-2933, 2023, DOI:10.32604/cmc.2023.044466

    Abstract The performance of a speech emotion recognition (SER) system is heavily influenced by the efficacy of its feature extraction techniques. The study was designed to advance the field of SER by optimizing feature extraction techniques, specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients (MFCC). This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches. Ultimately, the primary objective was to elevate both the intricacy and effectiveness of our SER model, with a focus on augmenting its proficiency in… More >

  • Open Access

    ARTICLE

    Effects of Emotion on Decision-Making of Methamphetamine Users: Based on the Emotional Iowa Gambling Task

    Xiaoqing Zeng1,2,3,*, Song Tu1,2,3, Ting Liu4

    International Journal of Mental Health Promotion, Vol.25, No.11, pp. 1229-1236, 2023, DOI:10.32604/ijmhp.2023.029903

    Abstract The relapse of methamphetamine (meth) is associated with decision-making dysfunction. The present study aims to investigate the impact of different emotions on the decision-making behavior of meth users. We used 2 (gender: male, female) × 3 (emotion: positive, negative, neutral) × 5 (block: 1, 2, 3, 4, 5) mixed experiment design. The study involved 168 meth users who were divided into three groups: positive emotion, negative emotion and neutral emotion group, and tested by the emotional Iowa Gambling Task (IGT). The IGT performance of male users exhibited a decreasing trend from Block 1 to Block More > Graphic Abstract

    Effects of Emotion on Decision-Making of Methamphetamine Users: Based on the Emotional Iowa Gambling Task

  • Open Access

    ARTICLE

    Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

    Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Ayman Yafoz4, Heba Mohsen5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2741-2757, 2023, DOI:10.32604/csse.2023.034721

    Abstract Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data… More >

  • Open Access

    ARTICLE

    Using Speaker-Specific Emotion Representations in Wav2vec 2.0-Based Modules for Speech Emotion Recognition

    Somin Park1, Mpabulungi Mark1, Bogyung Park2, Hyunki Hong1,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1009-1030, 2023, DOI:10.32604/cmc.2023.041332

    Abstract Speech emotion recognition is essential for frictionless human-machine interaction, where machines respond to human instructions with context-aware actions. The properties of individuals’ voices vary with culture, language, gender, and personality. These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition (SER). This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models. In the proposed approach, two wav2vec-based modules (a speaker-identification network and an emotion classification network) are trained with the Arcface loss.… More >

  • Open Access

    ARTICLE

    Break Free from Depression: Implementation and Outcomes of a School-Based Depression Awareness Program

    Amy J. Kaye1,*, Vanessa Prosper2, Kathryn Moffa1, Vanja Pejic1, Karen Capraro1, Georgios D. Sideridis1, Abigail Ross1,3, Kristine M. Dennery1, David R. DeMaso1

    International Journal of Mental Health Promotion, Vol.25, No.10, pp. 1103-1115, 2023, DOI:10.32604/ijmhp.2023.030185

    Abstract The objective of this study was to evaluate the impact of Break Free from Depression (BFFD), a school-based depression awareness curriculum, in comparison to a wait list control group. A total of 13 eighth grade classrooms participated in either an intervention or control group and completed pre-, post-, and three-month follow-up surveys. Students participating in BFFD (N = 6 classrooms, 166 students) demonstrated enhanced knowledge of and more adaptive attitudes towards depression compared to the control group (N = 7 classrooms, 155 students). Participants in the BFFD intervention also demonstrated increases in their confidence in… More >

  • Open Access

    ARTICLE

    Text Augmentation-Based Model for Emotion Recognition Using Transformers

    Fida Mohammad1,*, Mukhtaj Khan1, Safdar Nawaz Khan Marwat2, Naveed Jan3, Neelam Gohar4, Muhammad Bilal3, Amal Al-Rasheed5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202

    Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep… More >

Displaying 11-20 on page 2 of 108. Per Page