Lu Rong^1,#, Manyu Xu^2,3,#, Wenbo Zhu^2,*, Zhihao Yang^2,3, Chao Dong^1,4,5, Yunzhi Zhang^2,3, Kai Wang^1,2, Bing Zheng^1,4,5

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073442 - 10 February 2026

Abstract Deep learning has become integral to robotics, particularly in tasks such as robotic grasping, where objects often exhibit diverse shapes, textures, and physical properties. In robotic grasping tasks, due to the diverse characteristics of the targets, frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy, which presents a significant challenge for non-experts. Neural Architecture Search (NAS) provides a compelling method through the automated generation of network architectures, enabling the discovery of models that achieve high accuracy through efficient search algorithms. Compared to manually designed networks, NAS methods… More >

Abdu Salam¹, Fahd M. Aldosari², Donia Y. Badawood³, Farhan Amin^4,*, Isabel de la Torre^5,*, Gerardo Mendez Mezquita⁶, Henry Fabian Gongora⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073342 - 10 February 2026

Abstract Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple… More >

Chiwan Ahn¹, Daehee Kim^2,*, Seongkeun Park^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073330 - 10 February 2026

Abstract This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles. To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expert-driven LiDAR processing techniques into the deep neural network. Traditional 3D LiDAR processing methods typically remove ground planes and apply distance- or density-based clustering for object detection. In this work, such expert knowledge is encoded as feature-level inputs and fused with the deep network, thereby mitigating the data dependency issue of conventional learning-based… More >

Ping Fang, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073272 - 10 February 2026

Abstract Defect detection in printed circuit boards (PCB) remains challenging due to the difficulty of identifying small-scale defects, the inefficiency of conventional approaches, and the interference from complex backgrounds. To address these issues, this paper proposes SIM-Net, an enhanced detection framework derived from YOLOv11. The model integrates SPDConv to preserve fine-grained features for small object detection, introduces a novel convolutional partial attention module (C2PAM) to suppress redundant background information and highlight salient regions, and employs a multi-scale fusion network (MFN) with a multi-grain contextual module (MGCT) to strengthen contextual representation and accelerate inference. Experimental evaluations demonstrate More >

Noor Mueen Mohammed Ali Hayder^1,2, Seyed Amin Hosseini Seno^2,*, Hamid Noori², Davood Zabihzadeh³, Mehdi Ebady Manaa^4,5

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073236 - 10 February 2026

Abstract Distributed Denial of Service (DDoS) attacks are one of the severe threats to network infrastructure, sometimes bypassing traditional diagnosis algorithms because of their evolving complexity. Present Machine Learning (ML) techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times. However, such techniques sometimes fail to capture complicated relations among various traffic flows. In this paper, we present a new multi-scale ensemble strategy given the Graph Neural Networks (GNNs) for improving DDoS detection. Our technique divides traffic into macro- and micro-level elements, letting various GNN models to get… More >

Tianze Yu, Jianxun Zhang^*, Hongji Chen

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073119 - 10 February 2026

Abstract Camouflaged Object Detection (COD) aims to identify objects that share highly similar patterns—such as texture, intensity, and color—with their surrounding environment. Due to their intrinsic resemblance to the background, camouflaged objects often exhibit vague boundaries and varying scales, making it challenging to accurately locate targets and delineate their indistinct edges. To address this, we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network (EGMFNet), which leverages edge-guided multi-scale integration for enhanced performance. The model incorporates two innovative components: a Multi-scale Fusion Module (MSFM) and an Edge-Guided Attention Module (EGA). These designs… More >

Jinhuo Liang, Jinan Shen^*, Pengfei Wang, Fang Liang, Xuejian Deng

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073076 - 10 February 2026

Abstract The method for malware detection based on Application Programming Interface (API) call sequences, as a primary research focus within dynamic detection technologies, currently lacks attention to subsequences of API calls, the variety of API call types, and the length of sequences. This oversight leads to overly complex call sequences. To address this issue, a dynamic malware detection approach based on multiple subsequences is proposed. Initially, APIs are remapped and encoded, with the introduction of percentile lengths to process sequences. Subsequently, a combination of One-Dimensional Convolutional Neural Network (1D-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, More >

Longcang Wang, Yongbing Gao^*, Xinguang Wang, Xin Liu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072889 - 10 February 2026

Abstract Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods, this paper proposes the IPKE-MoE framework, which consists of three parts, namely, a sensitive word variant extraction framework, a sensitive word variant knowledge enhancement layer and a mixture-of-experts (MoE) classification layer. First, sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models (LLMs). Next, the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models. Specifically, after locating variants via n-gram… More >

Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072840 - 10 February 2026

Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

Yingkai Ge, Jiasheng Zhang, Jiale Zhang, Zhenguo Ma, Yu Liu, Lihua Wang^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072799 - 10 February 2026

Abstract With the continuous development of artificial intelligence and computer vision technology, numerous deep learning-based lane line detection methods have emerged. DeepLabv3+, as a classic semantic segmentation model, has found widespread application in the field of lane line detection. However, the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions, occlusions, and wear and tear on the lane lines. Additionally, DeepLabv3+ suffers from high memory consumption and challenges in deployment on embedded platforms. To address these issues, this paper proposes a lane line detection method for complex road scenes… More >