Computers, Materials & Continua Editorial Office

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.083417 - 08 May 2026

Abstract This article has no abstract. More >

Computers, Materials & Continua Editorial Office

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.083414 - 08 May 2026

Abstract This article has no abstract. 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 >

S. M. Anas^1,*, Rayeh Nasr Al-Dala’ien^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.081439 - 08 May 2026

Abstract This article has no abstract. More >

Yingying Yu^1,2,#,*, Jun Yuan^3,#, Tong Liu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080767 - 08 May 2026

Abstract Accurate grasp detection is fundamental to successful robotic manipulation. Existing methods achieve reliable performance under good light conditions. However, their performance in low-light environments suffers from severe degradation due to the diminishing discriminative ability of visual features. In this paper, a novel low-light aware hybrid network LAH-Net is proposed. It comprises an alternating transformer-CNN module (ATCM) between the encoder and decoder, and a knowledge distillation-guided low-light enhancement module (KDLEM) before the encoder, which is activated by an illumination gate under low-light conditions. To generate highly robust and synergistic features, the ATCM module facilitates the iterative… More >

Emir Kuanyshev, Hashim Ali^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080737 - 08 May 2026

Abstract Refactoring improves maintainability without altering externally observable behavior, yet it remains costly and error-prone when applied manually at scale. While large language models (LLMs) can generate plausible refactorings, practical adoption is limited by uncontrolled edit scope, inconsistent outputs under stochastic decoding, and weak traceability of why a change was produced. This paper proposes a smell-targeted, scope-bound refactoring framework for JavaScript that couples deterministic AST-based smell detection with constrained LLM transformation. The key design principle is to bind generation to explicitly detected smell instances, enforce a structured output contract (refactored code plus per-smell rationale), and log… More >

Kang Yang^1,2, Lizhi Cai^1,2,*, Jianhua Wu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080632 - 08 May 2026

Abstract Accurate malware identification and family categorization remain significant challenges in large-scale Android software analysis. Although deep learning has surpassed traditional machine learning in performance, its widespread adoption is hindered by the computational overhead stemming from feature redundancy and the lack of interpretability inherent in its black-box nature. To address these issues, this paper proposes DroidNTA, a DL-based detection model that fuses network traffic and API features. The model first constructs a simplified API Call Graph by extracting the intrinsic structural attributes of applications, and subsequently generates API feature vectors from invocation sequences using a Markov More >

Jesús Rosa-Bilbao^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080490 - 08 May 2026

Abstract The integration of Internet of Things (IoT) infrastructures with Distributed Ledger Technologies (DLT) remains challenging due to the reliance on complex, tightly coupled back-end systems or centralized oracle services that hinder scalability, maintainability, and trust. This paper introduces a lightweight middleware architecture based on a Low-Code Development Platform (LCDP) that enables flexible and secure IoT-to-blockchain orchestration. We develop a custom workflow extension for the n8n platform that supports direct interaction with smart contracts, thereby removing the need for third-party oracle intermediaries. The proposed system was evaluated in a real-world deployment involving a network of Netatmo More >

Akmalbek Abdusalomov¹, Mukhriddin Mukhiddinov^2,3, Fakhriddin Abdirazakov⁴, Alpamis Kutlimuratov⁵, Nodira Alimova⁶, Ilyos Kalandarov⁷, Ayhan Istanbullu⁸, Rashid Nasimov⁹, Young-Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080444 - 08 May 2026

Abstract Recent advancements in AI-synthesized speech have resulted in highly realistic deepfake audio, posing severe threats to authentication systems and digital media trust. Existing detection models struggle to generalize across diverse synthesis methods, especially those involving neural codec-based Audio Language Models (ALMs). In this work, we propose UniTector++, a novel prosody-aware, multi-stream detection architecture that generalizes across vocoder- and codec-based synthesis. UniTector++ incorporates three complementary streams—Whisper-based semantic embeddings, high-level prosodic features, and codec artifact representations—fused through a Multi-Domain Adaptive Graph Attention Fusion (MAGAF) module. Furthermore, an Emotion-Consistency Verification Module (ECVM) reinforces alignment between speech style and More >

Jigar Sarda¹, Rohan Vaghela¹, Akash Kumar Bhoi², Chang-Won Yoon^3,*, Mangal Sain^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080112 - 08 May 2026

Abstract Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset of bare PCBs was developed and carefully annotated with six defect categories: open circuits, missing holes, spurs, mouse bites, short… More >