Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-35, 2026, DOI:10.32604/cmc.2025.071210 - 09 December 2025

Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >

Sabina-Cristiana Necula^*, Napoleon-Alexandru Sireteanu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070861 - 09 December 2025

Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >

Illia Khurtin^*, Mukesh Prasad

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.067481 - 09 December 2025

Abstract The field of artificial intelligence has advanced significantly in recent years, but achieving a human-like or Artificial General Intelligence (AGI) remains a theoretical challenge. One hypothesis suggests that a key issue is the formalisation of extracting meaning from information. Meaning emerges through a three-stage interpretative process, where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context. However, this approach currently lacks practical grounding. In this research, we developed a model based on contexts, which applies interpretation principles to the visual information to address this gap. The field of computer… More >

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Qiang Zhang¹, Shuo Feng¹, Shanxiong Chen², Teng Wan¹, Ying Qi^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068247 - 10 November 2025

Abstract Human motion modeling is a core technology in computer animation, game development, and human-computer interaction. In particular, generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge. Existing methods typically rely on dense keyframe inputs or complex prior structures, making it difficult to balance motion quality and plausibility under conditions such as sparse constraints, long-term dependencies, and diverse motion styles. To address this, we propose a motion generation framework based on a frequency-domain diffusion model, which aims to better model complex motion distributions and enhance generation… More >

Shu-Hsuan Chang¹, Der-Fa Chen¹, Jing-Tang Sie¹, Kai-Jie Chen², Zhe-Wei Liao¹, Tai-Lung Chen¹, Yao-Chung Cheng^3,4,*

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1907-1927, 2025, DOI:10.32604/ijmhp.2025.068264 - 31 December 2025

Abstract Background: Interpersonal alienation has increasingly been recognized as a salient risk factor affecting university students’ psychological adjustment and life satisfaction. Guided by Social Identity and Self-Categorization theories, this study examines how alienation influences life satisfaction through the mediating roles of social identity and hope. Methods: This study surveyed 492 Taiwanese undergraduate students (53.7 percent female, mean age 21.08 years) from 60 universities using convenience sampling in May 2023. Data were collected through an online questionnaire distributed via faculty-managed teaching media platforms. Measures included perceived social identity, state hope, interpersonal alienation, and life satisfaction. All instruments were… More >

Yungui Guo^*, Xuan Fan

Journal of Psychology in Africa, Vol.35, No.6, pp. 791-796, 2025, DOI:10.32604/jpa.2025.067238 - 30 December 2025

Abstract This study examined the moderating role of employees’ learning orientation on the relationship between leaders’ artificial intelligence symbolization behavior (LAISB), employees’ attitude towards digital transformation (ATDT), and enterprise digital transformation. The sample consisted of 261 employees from five enterprises in China (female = 34.5%; primary industry includes the internet and transportation; mean age = 42.51 years, SD = 8.63 years; bachelor’s degree or above = 72.8%). The results of structural equation modeling and simple slope test indicated that LAISB predicted higher enterprise digital transformation, with ATDT partial mediation. Furthermore, employees’ learning orientation weakened the relationship More >

Wenjun Hu, Liquan Zhang^*

Journal of Polymer Materials, Vol.42, No.4, pp. 1159-1179, 2025, DOI:10.32604/jpm.2025.072450 - 26 December 2025

Abstract Addressing the persistent challenge of shale hydration and swelling in water-based drilling fluids (WBDFs), this study developed a smart thermo-responsive shale inhibitor, Hyperbranched Polyethyleneimine-Propylene Oxide-N-isopropylacrylamide (HPN). It was synthesized by grafting hyperbranched polyethyleneimine (HPEI) with propylene oxide (PO) and N-isopropylacrylamide (NIPAM), creating a synergistic hydration barrier through hydrophobic association and temperature-triggered pore plugging. Structural characterization by Fourier-Transform Infrared (FTIR) spectroscopy and gel permeation chromatography (GPC) confirmed the successful formation of the HPN terpolymer, revealing a unique “cationic–nonionic” amphiphilic architecture with temperature-responsive properties. Performance evaluation demonstrated that HPN significantly outperforms conventional inhibitors, including potassium chloride (KCl),… More >