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  • Open Access

    ARTICLE

    YOLOv10-HQGNN: A Hybrid Quantum Graph Learning Framework for Real-Time Faulty Insulator Detection

    Nghia Dinh1, Vinh Truong Hoang1,*, Viet-Tuan Le1, Kiet Tran-Trung1, Ha Duong Thi Hong1, Bay Nguyen Van1, Hau Nguyen Trung1, Thien Ho Huong1, Kittikhun Meethongjan2,*

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069587 - 12 January 2026

    Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >

  • Open Access

    ARTICLE

    Lightweight Hash-Based Post-Quantum Signature Scheme for Industrial Internet of Things

    Chia-Hui Liu*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.072887 - 09 December 2025

    Abstract The Industrial Internet of Things (IIoT) has emerged as a cornerstone of Industry 4.0, enabling large-scale automation and data-driven decision-making across factories, supply chains, and critical infrastructures. However, the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping, data tampering, and device impersonation. While digital signatures are indispensable for ensuring authenticity and non-repudiation, conventional schemes such as RSA and ECC are vulnerable to quantum algorithms, jeopardizing long-term trust in IIoT deployments. This study proposes a lightweight, stateless, hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT. The… More >

  • Open Access

    ARTICLE

    Laser-Ablated CdS and Ag2O Nanomaterials for High-Sensitivity Photodetectors

    Hameed H. Ahmed1, Thaer A. Mezher2,*, Marwan R. Rashid3

    Chalcogenide Letters, Vol.22, No.12, pp. 1055-1066, 2025, DOI:10.15251/CL.2025.2212.1055 - 10 December 2025

    Abstract Laser ablation in liquids (LAL), a hygienic and effective method for creating high-purity nanomaterials, was used in this study to create cadmium sulfide (CdS) and silver oxide (Ag2O) nanoparticles. The sputtering process was used to deposit the produced nanomaterials on porous silicon (PSi) substrates, and a number of assays were used to examine the samples’ structural, optical, and electrical characteristics. The CdS sample had a hexagonal crystal structure, according to X-ray diffraction (XRD) data, whereas the AgO sample had a cubic structure. The diameters of the nanoparticles in the two samples ranged from 22.64 nm for… More >

  • Open Access

    ARTICLE

    Novel Quantum-Integrated CNN Model for Improved Human Activity Recognition in Smart Surveillance

    Tanvir Fatima Naik Bukht1,2, Yanfeng Wu1, Nouf Abdullah Almujally3, Shuoa S. AItarbi4, Hameedur Rahman2, Ahmad Jalal2,5,*, Hui Liu1,6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

    Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

  • Open Access

    ARTICLE

    Nanostructured Self-Organization of Lead Sulphide Quantum Dots by Electrophoretic Deposition (EPD) Technique

    R. Yoga Indra Eniya1, K. Vijayakumar2, B. Vigneashwari3,*

    Chalcogenide Letters, Vol.22, No.11, pp. 971-985, 2025, DOI:10.15251/CL.2025.2211.971

    Abstract Nanocrystals (~16 nm) of semiconducting lead sulphide (PbS) were synthesized using the coprecipitation method, which was characterized for phase and compositional purity. These ultrafine particles of PbS exhibited quantum confinement characteristics, which were revealed by blue-shifting in optical absorption using UV-DRS analysis. These QDs of PbS were driven under the influence of the applied electric field using monodispersed colloidal suspension on the Indium-Tin-Oxide (ITO) substrate using the electrophoretic deposition technique (EPD). The formation of self-organized arrays of PbS quantum dots (QDs) and their stacked assemblies was achieved through EPD. Interestingly, neither complexing agents nor templates More >

  • Open Access

    ARTICLE

    State-Space Reduction Techniques Exploiting Specific Constraints for Quantum Search Initialization, Application to an Outage Planning Problem

    Rodolphe Griset1,#,*, Ioannis Lavdas2,§, Jiří Guth Jarkovský3

    Journal of Quantum Computing, Vol.7, pp. 81-105, 2025, DOI:10.32604/jqc.2025.066064 - 08 December 2025

    Abstract Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution. These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces. Unfortunately, the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure, rendering this kind of… More >

  • Open Access

    ARTICLE

    Exploring Efficiency of Silicon Carbide for Next Generation of Alkali & Alkaline Earth Metals-Ion Batteries Using Quantum Mechanic Method

    Fatemeh Mollaamin1,*, Majid Monajjemi2

    Energy Engineering, Vol.122, No.12, pp. 4971-4986, 2025, DOI:10.32604/ee.2025.069945 - 27 November 2025

    Abstract Delving alternative high-performance anodes for lithium-ion batteries have always attracted scientist attention. A wide-bandgap semiconductor with excellent mechanical properties, “silicon carbide (SiC)”, has been introduced as the anode electrode. Two-dimensional SiC has special hybridization which can build it as an appropriate substitution for graphene. Energy storage technologies are keys in the extension and function of electric devices. To keep up with steady innovations in saving energy technologies, it is essential to progress corresponding practical strategies. In this research article, SiC has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), beryllium… More > Graphic Abstract

    Exploring Efficiency of Silicon Carbide for Next Generation of Alkali & Alkaline Earth Metals-Ion Batteries Using Quantum Mechanic Method

  • Open Access

    ARTICLE

    Quantum Genetic Algorithm Based Ensemble Learning for Detection of Atrial Fibrillation Using ECG Signals

    Yazeed Alkhrijah1, Marwa Fahim2, Syed Muhammad Usman3, Qasim Mehmood3, Shehzad Khalid4,5,*, Mohamad A. Alawad1, Haya Aldossary6

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512 - 26 November 2025

    Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

  • Open Access

    ARTICLE

    A Quantum-Enhanced Biometric Fusion Network for Cybersecurity Using Face and Voice Recognition

    Abrar M. Alajlan1,*, Abdul Razaque2

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 919-946, 2025, DOI:10.32604/cmes.2025.071996 - 30 October 2025

    Abstract Biometric authentication provides a reliable, user-specific approach for identity verification, significantly enhancing access control and security against unauthorized intrusions in cybersecurity. Unimodal biometric systems that rely on either face or voice recognition encounter several challenges, including inconsistent data quality, environmental noise, and susceptibility to spoofing attacks. To address these limitations, this research introduces a robust multi-modal biometric recognition framework, namely Quantum-Enhanced Biometric Fusion Network. The proposed model strengthens security and boosts recognition accuracy through the fusion of facial and voice features. Furthermore, the model employs advanced pre-processing techniques to generate high-quality facial images and voice… More >

  • Open Access

    REVIEW

    Federated Learning in Convergence ICT: A Systematic Review on Recent Advancements, Challenges, and Future Directions

    Imran Ahmed1,#, Misbah Ahmad2,3,#, Gwanggil Jeon4,5,*

    CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

    Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

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