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  • Open Access

    REVIEW

    A Systematic Review of Frameworks for the Detection and Prevention of Card-Not-Present (CNP) Fraud

    Kwabena Owusu-Mensah*, Edward Danso Ansong , Kofi Sarpong Adu-Manu, Winfred Yaokumah

    Journal of Cyber Security, Vol.8, pp. 33-92, 2026, DOI:10.32604/jcs.2026.074265 - 20 January 2026

    Abstract The rapid growth of digital payment systems and remote financial services has led to a significant increase in Card-Not-Present (CNP) fraud, which is now the primary source of card-related losses worldwide. Traditional rule-based fraud detection methods are becoming insufficient due to several challenges, including data imbalance, concept drift, privacy concerns, and limited interpretability. In response to these issues, a systematic review of twenty-four CNP fraud detection frameworks developed between 2014 and 2025 was conducted. This review aimed to identify the technologies, strategies, and design considerations necessary for adaptive solutions that align with evolving regulatory standards.… More >

  • Open Access

    REVIEW

    AI-Powered Digital Twin Frameworks for Smart Grid Optimization and Real-Time Energy Management in Smart Buildings: A Survey

    Saeed Asadi1, Hajar Kazemi Naeini1, Delaram Hassanlou2, Abolhassan Pishahang3, Saeid Aghasoleymani Najafabadi4, Abbas Sharifi5, Mohsen Ahmadi6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

    Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

  • Open Access

    ARTICLE

    Relationship between Chinese Medical Students’ Perceived Stress and Short-Form Video Addiction: A Perspective Based on the Multiple Theoretical Frameworks

    Zhi-Yun Zhang1,*, Yaqiong Wu1, Chenshi Deng2, Peng Wang3, Weiguaju Nong4,*

    International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1533-1551, 2025, DOI:10.32604/ijmhp.2025.070883 - 31 October 2025

    Abstract Objectives: Medical students often rely on recreational internet media to relieve the stress caused by immense academic and life pressures, and among these media, short-form videos, which are an emerging digital medium, have gradually become the mainstream choice of students to relieve their stress. However, the addiction caused by their usage has attracted the widespread attention of both academia and society, which is why the purpose of this study is to systematically explore the underlying mechanisms that link perceived stress, entertainment gratification, emotional gratification, short-form video usage intensity, and short-form video addiction based on multiple… More >

  • Open Access

    ARTICLE

    Topological Characterization and Predictive Modeling of Graph Energy in Ionic Covalent Organic Frameworks

    Micheal Arockiaraj1,*, Aravindan Maaran2, C. I. Arokiya Doss2

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 637-655, 2025, DOI:10.32604/cmc.2025.065674 - 29 August 2025

    Abstract Covalent organic frameworks (COFs) are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures. Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks. Ionic covalent organic frameworks (ICOFs) are a subclass that incorporates ions in positive, negative, or zwitterionic forms into the frameworks. In particular, spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs. Unlike electroneutral COFs, ICOFs can be tailored by adjusting the types and arrangements of ions, influencing their formation mechanisms and physical properties. This study focuses on analyzing the graph-based structural… More >

  • Open Access

    REVIEW

    Ethical Implications of AI-Driven Ethical Hacking: A Systematic Review and Governance Framework

    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 >

  • Open Access

    ARTICLE

    Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

    Danial Jahed Armaghani1,*, Zida Liu2, Hadi Khabbaz1, Hadi Fattahi3, Diyuan Li2, Mohammad Afrazi4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2421-2451, 2024, DOI:10.32604/cmes.2024.052210 - 31 October 2024

    Abstract Tunnel Boring Machines (TBMs) are vital for tunnel and underground construction due to their high safety and efficiency. Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs. This study investigates the effectiveness of tree-based machine learning models, including Random Forest, Extremely Randomized Trees, Adaptive Boosting Machine, Gradient Boosting Machine, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and CatBoost, in predicting the Penetration Rate (PR) of TBMs by considering rock mass and material characteristics. These techniques are able to provide a good relationship between input(s)… More >

  • Open Access

    ARTICLE

    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer1,*, Ahmed H. Samak2, Ahmed I. Taloba3,4, Rasha M. Abd El-Aziz3,5

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265 - 26 January 2024

    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of… More >

  • Open Access

    ARTICLE

    Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

    Manas Ranjan Prusty1, Rishi Dinesh2, Hariket Sukesh Kumar Sheth2, Alapati Lakshmi Viswanath2, Sandeep Kumar Satapathy2,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718 - 26 December 2023

    Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time… More >

  • Open Access

    ARTICLE

    Unmanned Aerial Vehicle Multi-Access Edge Computing as Security Enabler for Next-Gen 5G Security Frameworks

    Jaime Ortiz Córdoba, Alejandro Molina Zarca*, Antonio Skármeta

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2307-2333, 2023, DOI:10.32604/iasc.2023.039607 - 21 June 2023

    Abstract 5G/Beyond 5G (B5G) networks provide connectivity to many heterogeneous devices, raising significant security and operational issues and making traditional infrastructure management increasingly complex. In this regard, new frameworks such as Anastacia-H2020 or INSPIRE-5GPlus automate the management of next-generation infrastructures, especially regarding policy-based security, abstraction, flexibility, and extensibility. This paper presents the design, workflow, and implementation of a security solution based on Unmanned Aerial Vehicles (UAVs), able to extend 5G/B5G security framework capabilities with UAV features like dynamic service provisioning in specific geographic areas. The proposed solution allows enforcing UAV security policies in proactive and reactive… More >

  • Open Access

    ARTICLE

    Comparative Analysis of COVID-19 Detection Methods Based on Neural Network

    Inès Hilali-Jaghdam1,*, Azhari A Elhag2, Anis Ben Ishak3, Bushra M. Elamin Elnaim4, Omer Eltag Mohammed Elhag5, Feda Muhammed Abuhaimed1, S. Abdel-Khalek2,6

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1127-1150, 2023, DOI:10.32604/cmc.2023.038915 - 08 June 2023

    Abstract In 2019, the novel coronavirus disease 2019 (COVID-19) ravaged the world. As of July 2021, there are about 192 million infected people worldwide and 4.1365 million deaths. At present, the new coronavirus is still spreading and circulating in many places around the world, especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic again. The symptoms of COVID-19 are diverse, and most patients have mild symptoms, with fever, dry cough, and fatigue as the main manifestations, and about 15.7% to 32.0% of patients will develop severe symptoms. Patients are… More >

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