Yue Yuan^1,*, Aibao Zhou^1,*, Tinghao Tang¹, Manying Kang², Haiyan Zhao¹, Zhi Wang³

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 563-577, 2023, DOI:10.32604/ijmhp.2023.022161

Abstract Objective: In this research, we tried to explore how short-term mindfulness (STM) intervention affects adolescents’ anxiety, depression, and negative and positive emotion during the COVID-19 pandemic. Design: 10 classes were divided into experiment groups (5 classes; n = 238) and control (5 classes; n = 244) randomly. Hospital Anxiety and Depression Scale (HADS) and Positive and Negative Affect Schedule (PANAS) were used to measure participants’ dependent variables. In the experiment group, we conducted STM practice interventions every morning in their first class from March to November 2020. No interventions were conducted in the control group. Methods: Paired-sample t-tests were used to identify if a… More >

Regina Lai-Tong Lee^1,2,*, Anson Chiu-Yan Tang³, Ho-Yu Cheng¹, Connie Yuen-Yu Chong¹, Wilson Wai-San Tam⁴, Wai-Tong Chien¹, Sally Wai-Chi Chan⁵

International Journal of Mental Health Promotion, Vol.25, No.3, pp. 327-342, 2023, DOI:10.32604/ijmhp.2022.026388

Abstract The present study aimed to examine work environment related factors and frontline primary healthcare professionals’ mental-emotional wellbeing during the COVID-19 pandemic in school communities of Hong Kong. A total of 61 (20%) school health nurses (frontline primary healthcare professionals) participated in a cross-sectional online survey from March to June 2020. Outcomes of mental-emotional health were measured using the Mental Health Continuum-Short Form (14-item scale with three subscales related to emotional, social and psychological wellbeing); the Perceived Stress Scale (10-item scale with two subscales related to perceived helplessness and lack of self-efficacy; and the Coping Orientation to Problems Experienced Inventory (Brief… More >

Chenhao Sun, Shaobei Xiao, Ting Liu, Xiaolin Yuan^*

International Journal of Mental Health Promotion, Vol.25, No.2, pp. 275-286, 2023, DOI:10.32604/ijmhp.2022.017934

Abstract Objective: To study the influence of cognition and emotion on moral judgment of college students under the circumstance of whether the cognitive resources are occupied and whether the emotion is induced. Methods: This experiment uses a multi-factor mixed experiment method to divide experiments and groups. Experiment 1 uses a two-factor mixed experimental design of 2 (cognitive resource occupancy group, cognitive resource non-occupied group) × 3 (difficult situation type). Experiment 2 uses a two-factor mixed experimental design of 2 (emotion induction group, emotion induction and cognitive resource occupation group) × 3 (three types of dilemma situation types) is adopted. The dependent… More >

Osman M. Karatepe^1,*, Raheleh Hassannia¹, Tuna Karatepe¹, Constanţa Enea², Hamed Rezapouraghdam¹

International Journal of Mental Health Promotion, Vol.25, No.2, pp. 287-307, 2023, DOI:10.32604/ijmhp.2022.025706

Abstract There are a plethora of empirical pieces about employees’ pro-environmental behaviors. However, the extant literature has either ignored or not fully examined various factors (e.g., negative or positive non-green workplace factors) that might affect employees’ pro-environmental behaviors. Realizing these voids, the present paper proposes and tests a serial mediation model that examines the interrelationships of job insecurity, emotional exhaustion, met expectations, and proactive pro-environmental behavior. We used data gathered from hotel customer-contact employees with a time lag of one week and their direct supervisors in China. After presenting support for the psychometric properties of the measures via confirmatory analysis in… 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

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 depression detection, long-short-term memory (LSTM)… More >

H. M. Shahzad^1,3, Sohail Masood Bhatti^1,3,*, Arfan Jaffar^1,3, Muhammad Rashid²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525

Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions. The multimodal has been… More >

Ayan Kumar¹, Abdul Quadir Md¹, J. Christy Jackson^1,*, Celestine Iwendi²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.033431

Abstract In today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which can share data with a… More >

K. Anuratha^1,*, M. Parvathy²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553

Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network… More >

Eun-Joo Choi¹, Yun-Jung Choi^2,*

International Journal of Mental Health Promotion, Vol.25, No.1, pp. 21-29, 2023, DOI:10.32604/ijmhp.2022.022641

Abstract This study aimed to explore citizens’ emotional responses and issues of interest in the context of the coronavirus disease 2019 (COVID-19) pandemic. The dataset comprised 65,313 tweets with the location marked as New York State. The data collection period was four days of tweets when New York City imposed a lockdown order due to an increase in confirmed cases. Data analysis was performed using R Studio. The emotional responses in tweets were analyzed using the Bing and NRC (National Research Council Canada) dictionaries. The tweets’ central issue was identified by Text Network Analysis. When tweets were classified as either positive… More > Graphic Abstract

Peizhu Gong¹, Jin Liu¹, Zhongdai Wu², Bing Han², Y. Ken Wang³, Huihua He^4,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4203-4220, 2023, DOI:10.32604/cmc.2023.028291

Abstract Speech emotion recognition, as an important component of human-computer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) for multimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a more powerful representation of the… More >