Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Zengyao Tian^1,2, Li Lv^1,*, Wenchen Deng¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5359-5376, 2025, DOI:10.32604/cmc.2025.069427 - 23 October 2025

Abstract Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a… More >

Qin Qin¹, Xuan Jiang^1,*, Jinhua Jiang¹, Dongfang Zhao¹, Zimei Tu¹, Zhiwei Shen²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4849-4867, 2025, DOI:10.32604/cmc.2025.067919 - 23 October 2025

Abstract The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion, surface defects, and low contrast, which hinder the extraction of reliable visual information. The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments. This makes conventional OCR techniques and standard deep learning models unreliable. To address these limitations, this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs. A Biorthogonal 4.4 (bior4.4) wavelet transform is adaptively tuned using DE to enhance character… More >

Elyson De La Cruz^*, Hanh Le, Karthik Meduri, Geeta Sandeep Nadella^*, Hari Gonaygunta

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3569-3582, 2025, DOI:10.32604/cmc.2025.068137 - 23 September 2025

Abstract The rapid integration of artificial intelligence (AI) into software development, driven by large language models (LLMs), is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems. This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology (IT) professionals as they navigate a dynamic technological landscape marked by intelligent automation, shifting professional identities, and emerging ethical concerns. Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling, prompt engineering, interdisciplinary collaboration, and heightened ethical awareness. However, participants also voiced… More >

Chunhong Guo¹, Huifang Liu², Jingfu Guo^3,*

Journal of Psychology in Africa, Vol.35, No.3, pp. 411-418, 2025, DOI:10.32604/jpa.2025.065763 - 31 July 2025

Abstract We explored the effects of algorithmic opacity on employees’ playing dumb and evasive hiding rather than rationalized hiding. We examined the mediating role of job insecurity and the moderating role of employee-AI collaboration. Participants were 421 full-time employees (female = 46.32%, junior employees = 31.83%) from a variety of organizations and industries that interact with AI. Employees filled out data on algorithm opacity, job insecurity, knowledge hiding, employee-AI collaboration, and control variables. The results of the structural equation modeling indicated that algorithm opacity exacerbated employees’ job insecurity, and job insecurity mediated between algorithm opacity and More >

Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma

Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >

Jing Qu¹, Yanmei Li^1,2, Changrong Liu¹, Wen Wang¹, Weiping Fu^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3787-3810, 2025, DOI:10.32604/cmc.2025.065895 - 03 July 2025

Abstract Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly (HRCA), challenges remain in the robot’s ability to understand and predict human assembly intentions. This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements. We propose a video feature extraction method based on the Temporal Shift Module Network (TSM-ResNet50) to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames. Furthermore, we construct an action recognition and segmentation model based on the Refined-Multi-Scale… More >

Hao Jiao¹, Xinyu Liu², Chen Wu¹, Chunlei Xu¹, Zhijun Zhou³, Ye Chen³, Guoqiang Sun^2,*

Energy Engineering, Vol.122, No.7, pp. 2977-2992, 2025, DOI:10.32604/ee.2025.064206 - 27 June 2025

Abstract With the continuous expansion of the power system scale and the increasing complexity of operational mode, the interaction between transmission and distribution systems is becoming more and more significant, placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods, this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification. To resolve the scalability issue of coupled transmission and distribution power systems, clustering… More >

Jiahao Liu^1,#, Xinyu Zhang^1,#, Huiwei Wang¹, Yupeng Li¹, Shan Jin¹, Guanxian Qiu¹, Ce Sun^1,2,*, Haiyan Tan¹, Yanhua Zhang^1,2,*

Journal of Renewable Materials, Vol.13, No.4, pp. 669-685, 2025, DOI:10.32604/jrm.2025.02025-0042 - 21 April 2025

Abstract In recent years, degradable materials to replace petroleum-based materials in preparing high-performance foams have received much research attention. Degradable polymer foaming mostly uses supercritical fluids, especially carbon dioxide (Sc-CO₂). The main reason is that the foams obtained by Sc-CO₂ foaming have excellent performance, and the foaming agent is green and pollution-free. In current research, Poly (butylene adipate-co-terephthalate) (PBAT), poly (lactic acid) (PLA), and other degradable polymers are generally used as the main foaming materials, but the foaming performance of these degradable polyesters is poor and requires modification. In this work, 10% PLA was added to PBAT to… More > Graphic Abstract

Chanhyeong Cho¹, Byeori Kim², Haehyun Cho², Taek-Young Youn^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 813-834, 2025, DOI:10.32604/cmes.2024.056952 - 17 December 2024

Abstract With the rise of remote collaboration, the demand for advanced storage and collaboration tools has rapidly increased. However, traditional collaboration tools primarily rely on access control, leaving data stored on cloud servers vulnerable due to insufficient encryption. This paper introduces a novel mechanism that encrypts data in ‘bundle’ units, designed to meet the dual requirements of efficiency and security for frequently updated collaborative data. Each bundle includes updated information, allowing only the updated portions to be re-encrypted when changes occur. The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes, such… More >