Ho Hai Quan^1,2, Le Huu Binh^1,*, Nguyen Dinh Hoa Cuong³, Le Duc Huy⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075549

Abstract Wireless Sensor Networks (WSNs) play a crucial role in numerous Internet of Things (IoT) applications and next-generation communication systems, yet they continue to face challenges in balancing energy efficiency and reliable connectivity. This study proposes SAC-HTC (Soft Actor-Critic-based High-performance Topology Control), a deep reinforcement learning (DRL) method based on the Actor-Critic framework, implemented within a Software Defined Wireless Sensor Network (SDWSN) architecture. In this approach, sensor nodes periodically transmit state information, including coordinates, node degree, transmission power, and neighbor lists, to a centralized controller. The controller acts as the reinforcement learning (RL) agent, with the… More >

Chih-Yung Huang^*, Hong-Ru Shi, Min-Yan Xie

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075398

Abstract Quality control plays a critical role in modern manufacturing. With the rapid development of electric vehicles, 5G communications, and the semiconductor industry, high-speed and high-precision detection of surface defects on silicon carbide (SiC) wafers has become essential. This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage. The complex machining textures on wafer surfaces hinder conventional machine vision models, often leading to misjudgment. To address this, deep learning algorithms were applied for defect classification. Because defects are rare and imbalanced across categories, data augmentation was performed… More >

HeeSeok Choi¹, Joon-Min Gil^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074511

Abstract Given the growing number of vehicle accidents caused by unintended acceleration and braking failure, verifying Sudden Unintended Acceleration (SUA) incidents has become a persistent challenge. A central issue of debate is whether such events stem from mechanical malfunctions or driver pedal misapplications. However, existing verification procedures implemented by vehicle manufacturers often involve closed tests after vehicle recalls; thus raising ongoing concerns about reliability and transparency. Consequently, there is a growing need for a user-driven framework that enables independent data acquisition and verification. Although previous studies have addressed SUA detection using deep learning, few have explored… More >

Abdallah Ghourabi^1,*, Hassen Chouaib²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074505

Abstract In recent years, ransomware attacks have become one of the most common and destructive types of cyberattacks. Their impact is significant on the operations, finances and reputation of affected companies. Despite the efforts of researchers and security experts to protect information systems from these attacks, the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks. The latest remarkable achievements of large language models (LLMs) in NLP tasks have caught the attention of cybersecurity researchers to integrate these models into security threat detection. These models offer high embedding… More >

Lin-Hui Liu¹, Dong-Jie Liu^1,*, Yin-Yan Zhang¹, Xiao-Bo Jin², Xiu-Cheng Wu³, Guang-Gang Geng¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074253

Abstract Phishing email detection represents a critical research challenge in cybersecurity. To address this, this paper proposes a novel Double-S (statistical-semantic) feature model based on three core entities involved in email communication: the sender, recipient, and email content. We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors, extracting statistical features from these entities. We also leverage the Qwen large language model to excavate implicit semantic features (e.g., emotional manipulation and social engineering tactics) from email content. By integrating statistical and semantic features, our model achieves a robust More >

Atef Gharbi¹, Mohamed Ayari², Nasser Albalawi³, Ahmad Alshammari³, Nadhir Ben Halima^4,*, Zeineb Klai³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075474

Abstract This paper presents Dual Adaptive Neural Topology (Dual ANT), a distributed dual-network meta-adaptive framework that enhances ant-colony-based multi-agent coordination with online introspection, adaptive parameter control, and privacy-preserving interactions. This approach improves standard Ant Colony Optimization (ACO) with two lightweight neural components: a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations. To preserve the privacy of individual trajectories in shared pheromone maps, we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy More >

Dina S. M. Hassan^*, Reem Ibrahim Alkanhel, Thuraya Alrumaih, Shiyam Alalmaei

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074751

Abstract The sustainability of the Internet of Things (IoT) involves various issues, such as poor connectivity, scalability problems, interoperability issues, and energy inefficiency. Although the Sixth Generation of mobile networks (6G) allows for Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC) services, it faces deployment challenges such as the short range of sub-THz and THz frequency bands, low capability to penetrate obstacles, and very high path loss. This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies. In this… More >

Rui Yao^1,2, Yuye Wang^1,2,*, Fei Yu^1,2,3,*, Hongrun Wu^1,2, Zhenya Diao^1,2

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.073861

Abstract Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based More >

Rama Sugavanam^*, Mythili Ramu

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.071469

Abstract When performing English-to-Tamil Neural Machine Translation (NMT), end users face several challenges due to Tamil’s rich morphology, free word order, and limited annotated corpora. Although available transformer-based models offer strong baselines, they compromise syntactic awareness and the detection and management of offensive content in cluttered, noisy, and informal text. In this paper, we present POSDEP-Offense-Trans, a multi-task NMT framework that combines Part-of-Speech (POS) and Dependency Parsing (DEP) methods with a robust offensive language classification module. Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms. The architecture incorporates a structure-aware contrastive… More >

Hoyoon Lee¹, Jeonghoon Jee¹, Hoseon Kim², Cheol Oh^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076980

Abstract Analyzing the driving behavior of autonomous vehicles (AV) in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design, providing infrastructure-based guidance information, and developing capability-enhanced AV perception systems. This study investigated the contributing factors affecting AV driving behavior using the Waymo Open Dataset. Binarized autonomous driving stability metrics, derived via a kernel density estimation, served as the target variables for a random forest classification model. The model’s input variables included 15 factors divided into four types: intersection-related, surrounding object-related, road infrastructure-related, and time-of-day-related types. The random forest classification model was… More >