Shuangyuan Li^1,*, Zhengwei Wang², Jiaming Liang³, Yichen Wang⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073896

Abstract Object detection plays a crucial role in the field of computer vision, and small object detection has long been a challenging issue within this domain. In order to improve the performance of object detection on small targets, this paper proposes an enhanced structure for YOLOv5, termed ATC-YOLOv5. Firstly, a novel structure, AdaptiveTrans, is introduced into YOLOv5 to facilitate efficient communication between the encoder and the detector. Consequently, the network can better address the adaptability challenge posed by objects of different sizes in object detection. Additionally, the paper incorporates the CBAM (Convolutional Block Attention Module) attention More >

José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074566

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… More >

Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074416

Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >

Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073883

Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073700

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073492

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… More >

Jiaxi Wang¹, Wenbo Sun², Ziyuan Zhou¹, Shihua Wu¹, Jiang Xu¹, Shan Ji^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073237

Abstract Lightweight nodes are crucial for blockchain scalability, but verifying the availability of complete block data puts significant strain on bandwidth and latency. Existing data availability sampling (DAS) schemes either require trusted setups or suffer from high communication overhead and low verification efficiency. This paper presents ISTIRDA, a DAS scheme that lets light clients certify availability by sampling small random codeword symbols. Built on ISTIR, an improved Reed–Solomon interactive oracle proof of proximity, ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication. This paper formalizes opening consistency and prove security… More >

Xun Chen¹, Jinbao Lin^1,2,*, Zai Wang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072495

Abstract This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension, combining experimental characterization with Visco-Plastic Self Consistent (VPSC) modeling. The research focuses particularly on anisotropic mechanical responses along transverse direction (TD) and rolling direction (RD). Experimental measurements and computational simulations consistently demonstrate that prismatic slip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation. By suppressing the activation of specific deformation mechanisms along RD and TD, the tensile mechanical behavior of the magnesium alloy was further investigated. The More >

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070224

Abstract Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for More >

Erdem Öztürk, Tuncel Uzel, Mustafa Işikdoğan^*, İsa Dağli, Nurullah Hamİdİ, Halil Başar

Canadian Journal of Urology, DOI:10.32604/cju.2025.071101

Abstract Background: The European Association of Urology (EAU) recommends transperineal biopsy (TPBx) due to its lower infection risk and higher diagnostic rate for anterior zone tumors. This study aims to assess the learning curve of TPBx using the Perino-Flex^® angle-adjustable needle guide under local anesthesia. Methods: A retrospective observational analysis was conducted from November 2023 to March 2024, involving 100 patients who underwent TPBx with coaxial technique under local anesthesia. Data collected included patient demographics, procedure and room times, pain levels, anxiety scores, and complications. The study focused on comparing procedure times, pain scores, and complication rates… More >