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 >

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 >

Gozde Karatas Baydogmus^*, Onder Demir

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3393-3406, 2025, DOI:10.32604/cmc.2025.062643 - 03 July 2025

Abstract Detection of Autism Spectrum Disorder (ASD) is a crucial area of research, representing a foundational aspect of psychological studies. The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years. Interdisciplinary efforts have further propelled research into detection methods. Consequently, this study aims to contribute to both the fields of psychology and computer science. Specifically, the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder. This study is structured into two distinct phases: data preprocessing and… More >

Kagiso Komane^1,*, Lucas Khoza², Fani Radebe¹

Journal of Cyber Security, Vol.7, pp. 79-108, 2025, DOI:10.32604/jcs.2025.059712 - 20 May 2025

Abstract Financial support, government support, cyber hygiene, and ongoing education and training as well as parental guidance and supervision are all essential components of cybersecurity awareness (CSA) identified in this study among the youth. It’s critical to realize that adequate funding is needed to effectively increase CSA, particularly among South African youth. Previous studies have demonstrated several ways to address inadequate CSA by utilizing various cybersecurity frameworks, ideas, and models. To increase CSA, this literature review seeks to emphasize the significance of integrating cybersecurity education throughout the entire school curriculum. This paper identified ethical issues, protection… More >

Longgang Zhao¹, Seok-Won Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4447-4476, 2025, DOI:10.32604/cmc.2025.063691 - 19 May 2025

Abstract This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. More >

Daniel Lesiba Letsoalo^1,*, Mahlatsi Venolia Semenya², Anastasia Julia Ngobe¹, Joy Katlego Hlokwe¹

Journal of Psychology in Africa, Vol.35, No.1, pp. 159-165, 2025, DOI:10.32604/jpa.2025.065764 - 30 April 2025

Abstract There has been an increase in mental health problems in Sub-Saharan Africa. Considering this, it is critical to track the region’s level of mental health literacy (MHL) to identify key mental health priorities and to direct the most effective interventions. The purpose of this study was to review the existing literature on MHL in sub-Saharan Africa. EBSCOhost (inclusive of Academic Search Ultimate, MEDLINE, APA PsycINFO, APA Psych Articles, and Global Health), CINAHL with full text, Wiley Online Library, Taylor and Francis Online Journals and Google Scholar databases were searched to retrieve relevant articles. The study… More >

Guojun Chen^*, Tao Cui, Yongjie Hou, Huihui Li

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3969-3987, 2024, DOI:10.32604/cmc.2024.056093 - 19 December 2024

Abstract Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance… More >

Hao Bai¹, Beiyuan Liu^2,*, Hongwen Liu³, Jupeng Zeng², Jian Ouyang⁴, Yipeng Liu¹

Energy Engineering, Vol.121, No.8, pp. 2191-2211, 2024, DOI:10.32604/ee.2024.049848 - 19 July 2024

Abstract Most ground faults in distribution network are caused by insulation deterioration of power equipment. It is difficult to find the insulation deterioration of the distribution network in time, and the development trend of the initial insulation fault is unknown, which brings difficulties to the distribution inspection. In order to solve the above problems, a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed. Firstly, the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network, and the… More >

Kranthi Alluri, S. Gopikrishnan^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621 - 25 April 2024

Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a… More >

Amy J. Kaye^1,*, Vanessa Prosper², Kathryn Moffa¹, Vanja Pejic¹, Karen Capraro¹, Georgios D. Sideridis¹, Abigail Ross^1,3, Kristine M. Dennery¹, David R. DeMaso¹

International Journal of Mental Health Promotion, Vol.25, No.10, pp. 1103-1115, 2023, DOI:10.32604/ijmhp.2023.030185 - 03 November 2023

Abstract The objective of this study was to evaluate the impact of Break Free from Depression (BFFD), a school-based depression awareness curriculum, in comparison to a wait list control group. A total of 13 eighth grade classrooms participated in either an intervention or control group and completed pre-, post-, and three-month follow-up surveys. Students participating in BFFD (N = 6 classrooms, 166 students) demonstrated enhanced knowledge of and more adaptive attitudes towards depression compared to the control group (N = 7 classrooms, 155 students). Participants in the BFFD intervention also demonstrated increases in their confidence in… More >