Zhiqiao Ji^1,2, Shuhua Wei^1,*, Hejuan Ding¹

International Journal of Mental Health Promotion, Vol.26, No.7, pp. 547-558, 2024, DOI:10.32604/ijmhp.2024.049867 - 30 July 2024

Abstract Internet gaming disorder (IGD) among junior high school students is an increasingly prominent mental health concern. It is important to look for influences behind internet gaming disorder tendency (IGDT) in the junior high school student population. The present study aimed to reveal the explanatory mechanisms underlying the association between parental psychological control (PPC) and internet gaming disorder tendency among junior high school students by testing the mediating role of core self-evaluation (CSE) and the moderating role of intentional self-regulation (ISR). Participants in present study were 735 Chinese junior high school students who completed offline self-report… More >

Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842 - 18 July 2024

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >

Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255 - 30 December 2023

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep More >

Liuyi Ling^1,2,3, Yiwen Wang^1,*, Fan Ding⁴, Li Jin¹, Bin Feng³, Weixiao Li³, Chengjun Wang¹, Xianhua Li¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383 - 26 December 2023

Abstract Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion More >

Jian Feng^*, Yuwen Wang, Shaojian Chen

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 397-413, 2023, DOI:10.32604/cmc.2023.038741 - 08 June 2023

Abstract Session-based Recommendation (SBR) aims to accurately recommend a list of items to users based on anonymous historical session sequences. Existing methods for SBR suffer from several limitations: SBR based on Graph Neural Network often has information loss when constructing session graphs; Inadequate consideration is given to influencing factors, such as item price, and users’ dynamic interest evolution is not taken into account. A new session recommendation model called Price-aware Session-based Recommendation (PASBR) is proposed to address these limitations. PASBR constructs session graphs by information lossless approaches to fully encode the original session information, then introduces More >

Heesup Han¹, Hong Ngoc Nguyen², Hyerin Lee³, Sanghyeop Lee^4,*

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 449-469, 2023, DOI:10.32604/ijmhp.2023.025379 - 01 March 2023

Abstract Since the outbreak of COVID-19, tourists have been increasingly concerned over various risks of international travel, while knowledge of the pandemic appears to vary significantly. In addition, as travel restrictions continue to impact adversely on international tourism, tourism efforts should be placed more on the domestic markets. Via structural equation modeling, this study unearthed different risk factors impacting Korean travelers’ choices of alternative local destinations in the post-pandemic era. In addition, this study extended the goal-directed behavior framework with the acquisition of perceived risk and knowledge of COVID-19, which was proven to hold a significantly More >

Jingjing Wang¹, Xiangli Guan^1,*, Yue Zhang², Yang Li¹, Md Zahir Ahmed³, Mary C. Jobe⁴, Oli Ahmed⁵

International Journal of Mental Health Promotion, Vol.25, No.2, pp. 223-235, 2023, DOI:10.32604/ijmhp.2022.025570 - 02 February 2023

Abstract Individuals’ perceptions, attitudes, and patterns of getting along with family members are important factors influencing Chinese people’s self-evaluation. The aim of this study was to investigate the effect of family cohesion on depression and the role of perceived social support and intentional self-regulation in this association. A hypothesized model of the association of family cohesion, perceived social support, intentional self-regulation, and depression was examined. A convenience sampling method was used to survey 1,180 college students in Yunnan Province using self-report. Data were collected using the Family Cohesion Scale, the Perceived Social Support Scale, the Intentional More >

X. Zhang¹, H. Liu¹, Z. H. Liu¹, J. R. Ming^1,*, Y. Zhou²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 357-369, 2023, DOI:10.32604/csse.2023.033251 - 20 January 2023

Abstract To meet the needs of today’s library users, institutions are developing library mobile apps (LMAs), as their libraries are increasingly intelligent and rely on deep learning. This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA. A research model was constructed based on the technology acceptance model. A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data. The analysis was based on structural equation modeling. The empirical results show that the… More >

Ching-Bang Yao¹, Chang-Yi Kao^2,*, Jiong-Ting Lin³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2233-2252, 2023, DOI:10.32604/iasc.2023.034488 - 05 January 2023

Abstract Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment. When finding a suspicious person, the tracked object cannot be locked in time for tracking. This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person. This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.… More >

Choon-Hong Tan¹, Ah-Choo Koo^1,*, Hawa Rahmat², Wei-Fern Siew³, Alexius Weng-Onn Cheang³, Elyna Amir Sharji¹

International Journal of Mental Health Promotion, Vol.25, No.1, pp. 99-126, 2023, DOI:10.32604/ijmhp.2022.025004 - 29 November 2022

Abstract The prevalence of mental health problems in both Malaysian and global workplaces has significantly increased due to the presence of the coronavirus disease (COVID-19) pandemic, globalization, technology advancement in Industry 4.0, and other contributing factors. The pervasiveness of the issue poses a huge challenge to improving the occupational safety and health (OSH) of workers in various industries, especially in the digital industry. The emergence of the innovative industry is evident mainly due to the rapid development of Industry 4.0 and the relevant demands of multiple businesses in the digital transformation. Nonetheless, limited studies and academic… More > Graphic Abstract