Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

Amjad Hussain^1,*, Ayesha Saadia^2,*, Chihhsiong Shih³, Nazish Nawaz², Amir H. Gandomi^4,*, Khursheed Aurangzeb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078451 - 27 May 2026

Abstract Malware has evolved from the early Creeper virus into highly sophisticated and organized cyber threats. Over time, it grew in sophistication, adopting advanced techniques, stealth tactics, and autonomous propagation. Modern malware leverages encryption, obfuscation, zero-day exploits, and AI-assisted techniques to conduct stealthy and persistent attacks. Classification of its exact family is the end goal to defend and mitigate the latest attacks. Researchers have contributed significantly and introduced many techniques to tackle malware threats. Binary detection is performed at a large scale, but very little in multi-class classification. In this research, a hybrid technique is proposed… More >

Soukaina Mjahed¹, Ouail Mjahed^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.081953 - 08 May 2026

Abstract The rapid emergence of sophisticated, dynamic, and rare or previously unseen attack pattern exposes fundamental limitations of conventional intrusion detection systems (IDS) based on static learning architectures. While deep learning (DL) models have demonstrated strong performance by capturing complex spatial and temporal traffic patterns, existing DL-based IDS largely rely on fixed decision structures, restricting adaptability to evolving threats. Furthermore, current hybrid DL-metaheuristic approaches typically use such metaheuristics as offline or auxiliary optimizers, without interacting with the deep model’s internal latent representations. This paper introduces a novel co-evolutionary IDS that establishes a tight, bidirectional coupling between… 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 >

Óscar Mogollón-Gutiérrez^*, José Carlos Sancho Núñez, Mar Ávila, MohammadHossein Homaei, Andrés Caro

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079694 - 08 May 2026

Abstract Cyberattacks have increased in frequency and complexity in recent years, resulting in significant consequences for organizations. The negative consequences of cyberattacks force organizations to implement adequate cybersecurity measures to prevent and mitigate the impact of these attacks. Analysis of network traffic is essential for determining whether a cyberattack has been conducted. Intrusion detection systems (IDS) are used to detect malicious actions or irregularities in information systems. In conjunction with artificial intelligence (AI), they enable the development of intelligent intrusion detection systems. This paper presents an intelligent method of network traffic classification for securing systems with… More >

Duong Do The^1,2, Duc-Nghia Tran³, Hoang-Dieu Vu⁴, Manh-Tuyen Vi^4,*, Duc-Tan Tran^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079056 - 08 May 2026

Abstract Road-surface identification is important for transportation monitoring and maintenance. However, this task is challenging due to the complexity of vibration signals, feature overlap among different surface types, and variations in real-world operating conditions. These challenges become more significant in time-series classification, where models must achieve high accuracy while remaining computationally efficient and suitable for low-cost hardware. This study investigates the design and evaluation of an automatic road-surface classification system using motion data collected from inertial sensors mounted on a vehicle, including accelerometers and gyroscopes. The system segments synchronized IMU signals into fixed-length windows and assigns… More >

Bing-Zhou Chen^1,2, Hai-Ying Zheng^1,2, Ao-Wen Wang^1,3, Ke-Lei Xia^1,2, Li-Feng Fan^1,3, Zhong-Yi Wang^1,3, Lan Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078894 - 08 May 2026

Abstract Many sensing and imaging modalities naturally yield complex-valued signals, where magnitude and phase jointly convey information. Complex-valued neural networks (CVNNs) possess unique advantages in processing phase-sensitive data (e.g., synthetic aperture radar (SAR) and magnetic resonance imaging (MRI)), yet their widespread adoption is hindered by significant computational overhead and training instability. To address these challenges, this paper presents the Wirtinger Derivative Complete Complex Network (WCCN), a unified and efficient framework for complex-valued deep learning. The proposed framework systematically addresses three key challenges in CVNNs: computational efficiency, parameter redundancy, and training stability. WCCN integrates three core components.… More >

Chin Ta Wu^1,2, Shing Han Li^3,*, Ching Shih Tsou⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078762 - 08 May 2026

Abstract In semiconductor packaging processes, the wire bonding procedure, which connects chips to substrate lead frames using metal wires, is a crucial step. The quality of the bonding joints significantly affects product performance, including signal integrity and reliability, and is challenging to verify after subsequent processes. To mitigate the risk of defective bonding joints entering the assembly packaging stages of production, this study integrates the concepts of Fault Detection and Classification (FDC) and machine learning into the wire bonding process for enhanced anomaly detection. Production data from the machines were collected and analyzed using statistical methods More >

Rabie Mezouar^1,2, Fouad Okba³, Dejan Zagorac^4,5,*, Salah Daoud², Abdelfateh Benmakhlouf ²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077724 - 08 May 2026

Abstract The compression effects on the thermoelastic and thermodynamic properties of cubic zincblende silicon–tin alloy (SiSn) were explored using a multi-methodological approach, deploying data mining methods, theoretical equation-of-state parameters, and the Quasi-Harmonic Debye Model. We analyze the relative volume, isothermal bulk modulus, thermal expansion coefficient, Debye temperature, sound velocity, and microhardness of the SiSn compound under pressures up to 8 GPa. The study commences with the data mining-based searches for a structural model and continues with an analysis of the pressure dependence of the relative volume using the Vinet equation of state, followed by an investigation… More >

Mustafa Qaid Khamisi¹, Zuping Zhang^1,*, Mohammed Al-Habib¹, Muhammad Asim², Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077556 - 08 May 2026

Abstract Large Vision-Language models (VLMs) such as Contrastive Language-Image Pretraining (CLIP) have transformed open world image recognition. Nevertheless, few-shot classification, particularly in the extremely low-shot regime, requires not only high accuracy but also reliably calibrated uncertainty for decisions with high confidence. Existing training-free CLIP adapters are primarily designed to increase accuracy and efficiency; integrate the zero-shot text logits with the few-shot feature caches, but not definitely model predictive uncertainty and therefore often exhibit considerable miscalibration and weak selective performance. Bayesian adapters move in the direction of probabilistic modeling by placing priors over adapter parameters and employing… More >