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 >

Haochen Bao¹, Lingyun Yuan^1,2,*, Tianyu Xie^1,2, Han Chen¹, Hui Dai¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069240 - 10 November 2025

Abstract In the age of big data, ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge. Traditional searchable encryption schemes face difficulties in handling complex semantic queries. Additionally, they typically rely on honest but curious cloud servers, which introduces the risk of repudiation. Furthermore, the combined operations of search and verification increase system load, thereby reducing performance. Traditional verification mechanisms, which rely on complex hash constructions, suffer from low verification efficiency. To address these challenges, this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification. Building on existing… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… More >

Jiming Lan¹, Hongwei Fu^1,*, Yadong Wu^1,2, Yaxian Liu^1,3, Jianhua Dong^1,2, Wei Liu^1,2, Huaqiang Chen^1,2

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4645-4665, 2025, DOI:10.32604/cmc.2025.067289 - 23 October 2025

Abstract In the research and production of fluorinated materials, large volumes of unstructured textual data are generated, characterized by high heterogeneity and fragmentation. These issues hinder systematic knowledge integration and efficient utilization. Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications. Among its core components, Named Entity Recognition (NER) plays an essential role, as its accuracy directly impacts relation extraction and semantic modeling, which ultimately affects the knowledge graph construction for fluorinated materials. However, NER in this domain faces challenges such as fuzzy entity boundaries, inconsistent terminology,… More >

Jiayi Geng¹, Yuxuan Wu¹, Wenbo Lu², Pengxiang Su^1,*, Amel Ksibi³, Wei Li¹, Zaffar Ahmed Shaikh^4,5, Di Gai⁶

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3689-3707, 2025, DOI:10.32604/cmc.2025.066944 - 23 September 2025

Abstract Predicting human motion based on historical motion sequences is a fundamental problem in computer vision, which is at the core of many applications. Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames. These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns. To address the above problems, we proposed a novel multi-level spatial and temporal learning model, which consists of a Cross Spatial Dependencies Encoding Module (CSM) and a Dynamic… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >

Zhiying Sun¹, Guolong Zhao^2,3, Feng Hu^1,*

Journal of Psychology in Africa, Vol.35, No.4, pp. 513-521, 2025, DOI:10.32604/jpa.2025.070126 - 17 August 2025

Abstract This study examined the relationship between transformational leadership and employee bootleg innovation through the mediating role of creative self-efficacy and the moderating role of leader-member exchange (LMX). Data were collected from 279 employees and 56 matched supervisors within a large Chinese digital transformation group. A moderated mediation model analysis found that transformational leadership predicts higher employee bootleg innovation. Creative self-efficacy mediated the positive relationship between transformational leadership and employee bootleg. Additionally, leader-member exchange significantly moderated the relationship between transformational leadership and creative self-efficacy, for a further enhancement of bootleg innovation. These findings underscore the importance More >

Gumashvili Megi^1,2, Yiping Mu¹, Kulbo Nora Bakabbey³, Addo Prince Clement^2,3,4,*, Kiti Kanokon¹, Menezes Dalila Batista de Sousa de¹, Baidoo Bernard Ekow¹

Journal of Psychology in Africa, Vol.35, No.3, pp. 287-298, 2025, DOI:10.32604/jpa.2025.067165 - 31 July 2025

Abstract This study investigated the influence of human capital development and technological, strategic, cognitive, and environmental factors on Industry 4.0 readiness, as well as cultural factors acting as a mediator. Respondents were 478 employees from across eight regions in Russia. Survey data were collected on employee technological readiness, human capital development, strategic planning, cognitive perceptions, and environmental and cultural factors influencing the adoption of Industry 4.0 technologies, with cultural factors mediating. These findings from the structure equation analysis show that technological factors and human capital development are the strongest predictors of readiness, suggesting that robust digital More >

Ze Wang, Jin Qin, Chuhua Huang^*, Yongjun Zhang^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.064537 - 30 July 2025

Abstract Semantic segmentation has made significant breakthroughs in various application fields, but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge. To this end, we propose CGMISeg, an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy, aiming to significantly reduce computational overhead while maintaining segmentation accuracy. CGMISeg consists of three core components: context-aware attention modulation, feature reconstruction, and cross-information fusion. Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms. The feature reconstruction module reconstructs contextual information from different scales, modeling… More >

Anand Deva Durai Chelladurai¹, Theena Jemima Jebaseeli², Omar Alqahtani¹, Prasanalakshmi Balaji^1,*, Jeniffer John Simon Christopher³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1101-1122, 2025, DOI:10.32604/cmc.2025.062278 - 09 June 2025

Abstract Optical Coherence Tomography (OCT) provides cross-sectional and three-dimensional reconstructions of the target tissue, allowing precise imaging and quantitative analysis of individual retinal layers. These images, based on optical inhomogeneities, reveal intricate cellular structures and are vital for tasks like retinal segmentation. The proposed study uses OCT images to identify significant differences in peripapillary retinal nerve fiber layer thickness. Incorporating spectral-domain analysis of OCT images significantly enhances the evaluation of Purtcher Retinopathy. To streamline this process, the study introduces a Context Encoding Deep Neural Network (CEDNN), which eliminates the time-consuming manual segmentation process while improving the… More >