Minsu Kim¹, Daeho Choi¹, Younghyo Cho¹, Yeog Kim², Jun Lee³, Changhoon Lee¹, Kiwook Sohn^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084041 - 27 July 2026

Abstract Future mobile Internet and convergence applications increasingly rely on encrypted protocols, making security monitoring difficult because payload inspection is unavailable while traffic classes and threats evolve continuously. Encrypted traffic classification models must therefore adapt to newly emerging traffic classes without repeatedly overwriting or fully retraining large Transformer backbones. This study presents and extends an ET-BERT Adapter Fusion framework for AI/ML-driven encrypted-traffic security monitoring in future mobile Internet and convergence applications. The framework keeps the ET-BERT backbone frozen, trains a Base Adapter on USTC-TFC2016 classes 0–9, trains an Incremental Adapter for class 10, and composes them… More >

Daniel Martín-Pérez¹, Francesc Rodríguez-Díaz¹, David Gutiérrez-Avilés², Alicia Troncoso¹, Francisco Martínez-Álvarez^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082712 - 27 July 2026

Abstract Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum… More >

Rashid Jahangir^1,*, Nazik Alturki², Muhammad Zubair Khan¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080593 - 23 July 2026

Abstract Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication. Its realism has raised serious concerns in different applications such as digital forensics, cybersecurity, media authentication and voice-based security systems. However, deepfake audio detection still remains difficult. Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely. Variations in speakers, recording conditions and background noise make the task more complex. In addition, dataset imbalance and low diversity in training samples could lead to low robustness in the model. To… More >

Zheng Xu^1,2, Zihao Pan¹, Ning Yang¹, Daoxing Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080559 - 15 June 2026

Abstract Unmanned Aerial Vehicle (UAV) communications in complex electromagnetic environments face challenges such as strong interference, high dynamic Doppler shifts, and limited onboard computing power. In these scenarios, traditional blind beamforming algorithms suffer from slow convergence and difficulty in handling Gaussian-like signals (e.g., Orthogonal Frequency Division Multiplexing (OFDM)). To address these issues, this paper proposes a Lightweight Robust Transfer learning-based Blind Beam Forming method (LRT-BF). This method constructs a self-supervised optimization framework centered on a pre-trained signal classifier and innovatively introduces a joint loss function combining classification confidence guidance with output power minimization, achieving fully blind… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Muhammad Shaheer¹, Feng Zeng^1,*, Aqsa Yasmeen², Mudasir Ahmad Wani^3,*, Kashish Ara Shakil⁴, Muhammad Asim⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079701 - 08 May 2026

Abstract Malware remains a persistent and evolving threat to digital security, highlighting the need for advanced and resilient detection frameworks capable of mitigating increasingly sophisticated and evasive cyberattacks. Although deep learning ensembles have been explored, many existing approaches fail to balance computational efficiency with the diverse feature extraction capabilities needed for complex variants. To address this gap, this study proposes a novel stacking ensemble framework, MalDetect-IoT, which specifically eliminates the requirement for manual feature engineering and domain specific preprocessing traditionally required in malware classification. By fine-tuning two pre-trained models MobileNetV3 for its lightweight efficiency and Xception… More >

Kiseok Kim^#, Taehoon Yoo^#, Sangmin Lee, Hwangnam Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077415 - 09 April 2026

Abstract Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold.… More >

Zhidong Wang, Yanping Lian^*, Mingjian Li, Jiawei Chen, Ruxin Gao

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026

Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >

Samia Allaoua Chelloug^1,*, Ahmed A. Abd El-Latif^2,3,*, Samah AlShathri¹, Mohamed Hammad^2,4

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074485 - 12 March 2026

Abstract Scene recognition is a critical component of computer vision, powering applications from autonomous vehicles to surveillance systems. However, its development is often constrained by a heavy reliance on large, expensively annotated datasets. This research presents a novel, efficient approach that leverages multi-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group (VGG)—to overcome this limitation. Our method significantly reduces dependency on vast labeled data while achieving high accuracy. Evaluated on the Aerial Image Dataset (AID) dataset, the model attained a validation accuracy of 93.6% with a loss of 0.35, demonstrating robust performance More >

Syed Nisar Hussain Bukhari^*, Sehar Altaf

Journal on Artificial Intelligence, Vol.8, pp. 137-152, 2026, DOI:10.32604/jai.2026.077943 - 11 March 2026

Abstract Knee osteoarthritis is a progressive degenerative joint disorder that leads to pain, stiffness, and reduced mobility, significantly affecting quality of life. Early and reliable diagnosis is essential for effective disease management, yet conventional radiographic assessment remains time-consuming and subject to inter-observer variability. This study presents a comparative deep learning (DL) based approach for automated severity classification of knee osteoarthritis using plain radiographic images. Multiple pretrained convolutional neural network architectures, including EfficientNetB3, InceptionNet, VGG19, ResNet, and EfficientNetV2S, were evaluated within a transfer learning paradigm. All models were trained and assessed on a publicly available dataset to More >