Parul Dubey^1,*, Pushkar Dubey², Mohammed Zakariah^3,4,*, Abdulaziz S. Almazyad⁴, Deema Mohammed Alsekait⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3897-3919, 2025, DOI:10.32604/cmc.2025.066882 - 23 September 2025

Abstract Psychological distress detection plays a critical role in modern healthcare, especially in ambient environments where continuous monitoring is essential for timely intervention. Advances in sensor technology and artificial intelligence (AI) have enabled the development of systems capable of mental health monitoring using multi-modal data. However, existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings. This paper addresses these challenges by proposing TRANS-HEALTH, a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention (BDI) reasoning for real-time psychological distress detection. The framework utilizes a multimodal dataset containing EEG, GSR, heart rate, and activity… More >

Yubin Qu^1,2, Song Huang^2,*, Long Li³, Peng Nie², Yongming Yao²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 249-300, 2025, DOI:10.32604/cmc.2025.067750 - 29 August 2025

Abstract Large language models (LLMs) represent significant advancements in artificial intelligence. However, their increasing capabilities come with a serious challenge: misalignment, which refers to the deviation of model behavior from the designers’ intentions and human values. This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview. We define the concept of misalignment and elaborate on its various manifestations, including generating harmful content, factual errors (hallucinations), propagating biases, failing to follow instructions, emerging deceptive behaviors, and emergent misalignment. We explore the multifaceted causes of misalignment,… More >

Xin-Xin Chen^1,*, Young-Sup Hyun^2,*, Wen-Hao Chen³

Journal of Psychology in Africa, Vol.35, No.3, pp. 393-401, 2025, DOI:10.32604/jpa.2025.068038 - 31 July 2025

Abstract This study utilized a sequential mediating model to examine the role of motivation to learn and transfer self-efficacy in the relationships between perceived content validity, mentoring function, continuous learning work culture and intention to transfer learning. The sample comprized 429 final-year apprentices in Guangdong province, China (females = 69.9%, Engineering & Medicine = 69%, mean age = 20.99, SD = 1.60). The apprentices completed standardized measures of motivation to learn, transfer self-efficacy perceived content validity, mentoring function, and continuous learning work culture. Structural equation modeling was used to analyze the data. Results showed perceived content… More >

Yinuo Chen¹, Xu Wu¹, Jiaxin Fan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

Mengjun Zhu^1,#, Xing’an Yao^2,*,#, Mansor Bin Abu Talib^1,*

Journal of Psychology in Africa, Vol.35, No.1, pp. 83-90, 2025, DOI:10.32604/jpa.2025.065784 - 30 April 2025

Abstract This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement. The study sample comprised 540 Chinese senior secondary school students between the ages of 15–18 (51.67% boys; M_age = 16.56 years; SD_age = 0.90). They completed surveys on the Teacher-Student Relationship Scale, the Selection, Optimization, and Compensation (SOC) Scale, the Peer Relationship Scale for Children and Adolescents, and the Learning Engagement Scale. The results following regression analysis showed that teacher-student relationship predicted higher learning engagement among senior secondary school students. Intentional self-regulation More >

Di Wu^*, Liming Feng, Xiaoyu Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 977-999, 2025, DOI:10.32604/cmc.2025.060319 - 26 March 2025

Abstract Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model (SNID-ENSEF) is proposed. Syntactic elimination contrast learning leverages verb-dominant syntactic features, systematically replacing specific words to enhance data diversity. The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency. A neighborhood sample fusion strategy, based on sample distribution patterns, dynamically adjusts neighborhood size and fuses sample More >

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 >