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Yingli Liu^1,2, Yuting Cui^1,2, Haihe Zhou^1,2, Sheng Lei³, Haibin Yuan³, Tao Shen^1,2,*, Jiancheng Yin^4,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1463-1492, 2025, DOI:10.32604/cmc.2025.060109 - 17 February 2025
（This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials More >

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Robertas Damasevicius^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1493-1538, 2025, DOI:10.32604/cmc.2024.057431 - 17 February 2025
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including… More >

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291

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Laith Abualigah^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025
（This article belongs to the Special Issue: Particle Swarm Optimization: Advances and Applications)
Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

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469

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Yongjun Ren¹, Zhiming Wang¹, Pradip Kumar Sharma², Fayez Alqahtani³, Amr Tolba⁴, Jin Wang^5,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1593-1613, 2025, DOI:10.32604/cmc.2025.059170 - 17 February 2025
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defensive techniques, Zero Trust Networks (ZTN) have emerged as a widely recognized technology. Zero Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of “never trust, always verify.” Initially proposed by John Cortez in 2010 and subsequently promoted by Google, the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network environments. This paper systematically compares the current mainstream cybersecurity models, thoroughly explores More >

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416

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Priyanka¹, Manjit Kaur¹, Deepak Prashar¹, Leo Mrsic², Arfat Ahmad Khan^3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1615-1643, 2025, DOI:10.32604/cmc.2025.059043 - 17 February 2025
Abstract “Flying Ad Hoc Networks (FANETs)”, which use “Unmanned Aerial Vehicles (UAVs)”, are developing as a critical mechanism for numerous applications, such as military operations and civilian services. The dynamic nature of FANETs, with high mobility, quick node migration, and frequent topology changes, presents substantial hurdles for routing protocol development. Over the preceding few years, researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET, which is topology change and high mobility. This paper reviews current research on routing protocols and Machine Learning (ML) approaches applied to FANETs, emphasizing developments… More >

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765

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395

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Huajiang Ruan^1,2, Panjun Sun^1,2, Yuanyuan Dong^1,2, Hamid Tahaei¹, Zhaoxi Fang^1,2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1645-1680, 2025, DOI:10.32604/cmc.2024.059746 - 17 February 2025
Abstract Traditional Global Positioning System (GPS) technology, with its high power consumption and limited performance in obstructed environments, is unsuitable for many Internet of Things (IoT) applications. This paper explores LoRa as an alternative localization technology, leveraging its low power consumption, robust indoor penetration, and extensive coverage area, which render it highly suitable for diverse IoT settings. We comprehensively review several LoRa-based localization techniques, including time of arrival (ToA), time difference of arrival (TDoA), round trip time (RTT), received signal strength indicator (RSSI), and fingerprinting methods. Through this review, we evaluate the strengths and limitations of More >

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638

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290

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Milad Moradi^1,*, Ke Yan², David Colwell², Matthias Samwald³, Rhona Asgari¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1681-1698, 2025, DOI:10.32604/cmc.2025.061263 - 17 February 2025
（This article belongs to the Special Issue: Artificial Intelligence Current Perspectives and Alternative Paths: From eXplainable AI to Generative AI and Data Visualization Technologies)
Abstract This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodologies. We critically examine the evolution from Recurrent Neural Networks (RNNs) to Transformer models, highlighting the significant advancements and innovations in LLM architectures. The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches, with an emphasis on optimizing parameter efficiency. We also discuss methods for aligning LLMs with human preferences, including reinforcement learning frameworks and human feedback mechanisms. The emerging technique of retrieval-augmented generation, which integrates external knowledge into LLMs, is More >

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709

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355

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Carlos Andrés Tavera Romero^1,*, Jesús Hamilton Ortiz², Luis Carlos Rodríguez Timaná³, Fernando Vélez Varela⁴, Andrés Hernando Aristizábal Montufar¹, Javier Gamboa-Cruzado⁵
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1699-1732, 2025, DOI:10.32604/cmc.2025.056400 - 17 February 2025
Abstract This study investigates the design and implementation of Flying Ad Hoc Networks (FANETs), a network architecture inspired by the Mobile Ad Hoc Network (MANET) model, specifically tailored to support unmanned aerial vehicles (UAVs). As UAVs increasingly contribute to diverse fields, from surveillance to delivery, FANETs have emerged as essential in ensuring stable, dynamic communication channels among drones in flight. This research adopts a dual approach, combining rigorous theoretical analysis with detailed practical simulations to assess the performance, adaptability, and efficiency of FANETs in varying conditions. The findings emphasize the ability of FANETs to manage network congestion effectively… More >

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450

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233

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Huu-Tuong Ho^1,#, Luong Vuong Nguyen^1,#, Minh-Tien Pham¹, Quang-Huy Pham¹, Quang-Duong Tran¹, Duong Nguyen Minh Huy², Tri-Hai Nguyen^3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1733-1756, 2025, DOI:10.32604/cmc.2025.060363 - 17 February 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. More >

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460

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210

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Uddagiri Sirisha^1,*, Chanumolu Kiran Kumar², Sujatha Canavoy Narahari³, Parvathaneni Naga Srinivasu^4,5,6
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025
Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >

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577

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274

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Youngjin Hwang, Seungyoon Kang, Junseok Kim^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1811-1838, 2025, DOI:10.32604/cmc.2025.061071 - 17 February 2025
Abstract This review paper provides a comprehensive introduction to various numerical methods for the phase-field model used to simulate the phase separation dynamics of diblock copolymer melts. Diblock copolymer systems form complex structures at the nanometer scale and play a significant role in various applications. The phase-field model, in particular, is essential for describing the formation and evolution of these structures and is widely used as a tool to effectively predict the movement of phase boundaries and the distribution of phases over time. In this paper, we discuss the principles and implementations of various numerical methodologies More >

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292

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Meng-Hua Yen^1,*, Nilamadhab Mishra^2,*, Win-Jet Luo³, Chu-En Lin¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1839-1855, 2025, DOI:10.32604/cmc.2025.060903 - 17 February 2025
Abstract The Internet of Everything (IoE) coupled with Proactive Artificial Intelligence (AI)-Based Learning Agents (PLAs) through a cloud processing system is an idea that connects all computing resources to the Internet, making it possible for these devices to communicate with one another. Technologies featured in the IoE include embedding, networking, and sensing devices. To achieve the intended results of the IoE and ease life for everyone involved, sensing devices and monitoring systems are linked together. The IoE is used in several contexts, including intelligent cars’ protection, navigation, security, and fuel efficiency. The Smart Things Monitoring System… More >

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577

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293

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Jiewen Liu¹, Bing Chen^1,2,*, Baolu Xue¹, Mengya Guo¹, Yuntao Xu¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1857-1877, 2025, DOI:10.32604/cmc.2024.057814 - 17 February 2025
Abstract Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack… More >

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489

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Kexin Wang^1,#, Jiancheng Liu^1,#,*, Yuqing Lin^2,*, Tuo Wang¹, Zhipeng Zhang¹, Wanlong Qi¹, Xingye Han¹, Runyuan Wen³
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1879-1899, 2025, DOI:10.32604/cmc.2024.058952 - 17 February 2025
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection. Current frameworks for oriented detection modules are constrained by intrinsic limitations, including excessive computational and memory overheads, discrepancies between predefined anchors and ground truth bounding boxes, intricate training processes, and feature alignment inconsistencies. To overcome these challenges, we present ASL-OOD (Angle-based SIOU Loss for Oriented Object Detection), a novel, efficient, and robust one-stage framework tailored for oriented object detection. The ASL-OOD framework comprises three core components: the Transformer-based Backbone (TB), the Transformer-based Neck… More >

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243

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Xin Tong¹, Jingya Wang^1,*, Ying Yang², Tian Peng³, Hanming Zhai¹, Guangming Ling⁴
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1901-1924, 2025, DOI:10.32604/cmc.2024.059018 - 17 February 2025
Abstract With the widespread use of SMS (Short Message Service), the proliferation of malicious SMS has emerged as a pressing societal issue. While deep learning-based text classifiers offer promise, they often exhibit suboptimal performance in fine-grained detection tasks, primarily due to imbalanced datasets and insufficient model representation capabilities. To address this challenge, this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS detection. During the data processing stage, Large Language Models (LLMs) are employed for data augmentation, mitigating dataset imbalance. In the data input stage, both word-level and character-level features are More >

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563

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Ching-Lung Fan^1,*, Yu-Jen Chung²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1925-1945, 2025, DOI:10.32604/cmc.2025.059245 - 17 February 2025
Abstract This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle (UAV) imagery by integrating the Visible Atmospherically Resistant Index (VARI) with deep learning models. The primary challenge addressed is the detection of bananas interplanted with betel nuts, a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap. The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector (SSD), You Only Look Once version 3 (YOLOv3), and Faster Region-Based Convolutional Neural Network (Faster RCNN)—using Red, Green, Blue (RGB) and VARI images for banana detection. Results More >

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Yaoxuan Zhu, Hua Yang, Bin Zhu^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1947-1968, 2025, DOI:10.32604/cmc.2024.057866 - 17 February 2025
Abstract The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natural adversarial examples has posed significant challenges, as traditional defense methods against adversarial attacks have proven to be largely ineffective against these natural adversarial examples. This paper explores defenses against these natural adversarial examples from three perspectives: adversarial examples, model architecture, and dataset. First, it employs Class Activation Mapping (CAM) to visualize how models classify natural adversarial examples, identifying several typical attack patterns. Next, various common… More >

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Song Liu^1,2, Hongying Zhang^1,*, Xue Li¹, Xi Yang^1,3
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1969-1984, 2025, DOI:10.32604/cmc.2024.057662 - 17 February 2025
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels… More >

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648

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Liang Mao^1,2, Yue Li^1,2, Linlin Wang^1,*, Jie Li¹, Jiajun Tan¹, Yang Meng¹, Cheng Xiong¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1985-1999, 2025, DOI:10.32604/cmc.2024.058409 - 17 February 2025
Abstract This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable… More >

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Thai-Viet Dang^1,*, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

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Dawei Xu^1,2,3, Min Wang¹, Yue Lv¹, Moxuan Fu², Yi Wu^4,5,*, Jian Zhao¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2027-2041, 2025, DOI:10.32604/cmc.2024.058178 - 17 February 2025
Abstract Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF… More >

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Ömer Ekim Genel^*, Hilal Koç, Ekrem Tüfekci
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2043-2059, 2025, DOI:10.32604/cmc.2025.060111 - 17 February 2025
Abstract Due to their superior properties, the interest in nanostructures is increasing today in engineering. This study presents a new two-noded curved finite element for analyzing the in-plane static behaviors of curved nanobeams. Opposite to traditional curved finite elements developed by using approximate interpolation functions, the proposed curved finite element is developed by using exact analytical solutions. Although this approach was first introduced for analyzing the mechanical behaviors of macro-scale curved beams by adopting the local theory of elasticity, the exact analytical expressions used in this study were obtained from the solutions of governing equations that… More >

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Murun Yang^1,*, Fuxue Li²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2061-2075, 2025, DOI:10.32604/cmc.2024.058248 - 17 February 2025
Abstract Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a… More >

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Haitao He¹, Bingjian Yan¹, Ke Xu^1,*, Lu Yu^1,2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2077-2108, 2025, DOI:10.32604/cmc.2024.058779 - 17 February 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Software defect prediction aims to use measurement data of code and historical defects to predict potential problems, optimize testing resources and defect management. However, current methods face challenges: (1) Coarse-grained file level detection cannot accurately locate specific defects. (2) Fine-grained line-level defect prediction methods rely solely on local information of a single line of code, failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line, making it difficult to capture the interaction between global and local information. Therefore, this paper proposes a… More >

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Sujittra Sarakon¹, Wansuree Massagram^1,2, Kreangsak Tamee^1,3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2109-2136, 2025, DOI:10.32604/cmc.2025.058906 - 17 February 2025
Abstract This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP More >

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Shaozheng Zhang, Qiuyu Zhang^*, Jiahui Tang, Ruihua Xu
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2137-2158, 2025, DOI:10.32604/cmc.2024.057269 - 17 February 2025
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >

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Guorong Qi¹, Jian Mao^1,*, Kai Huang¹, Zhengxian You², Jinliang Lin²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2159-2176, 2025, DOI:10.32604/cmc.2024.058396 - 17 February 2025
（This article belongs to the Special Issue: Fortifying the Foundations: Novel Approaches to Cyber-Physical Systems Intrusion Detection and Industrial 4.0 Security)
Abstract Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features; Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection… More >

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Yuting Zhu^1,*, Wenyu Zhang^1,2, Hainan Wang¹, Junjie Hou¹, Haining Wang¹, Meng Wang¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2177-2198, 2025, DOI:10.32604/cmc.2024.057441 - 17 February 2025
Abstract Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The… More >

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Joon-Min Gil¹, Hyunsu Jeong¹, Jihun Kang^2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2199-2213, 2025, DOI:10.32604/cmc.2025.061182 - 17 February 2025
（This article belongs to the Special Issue: Multi-Service and Resource Management in Intelligent Edge-Cloud Platform)
Abstract In a cloud environment, graphics processing units (GPUs) are the primary devices used for high-performance computation. They exploit flexible resource utilization, a key advantage of cloud environments. Multiple users share GPUs, which serve as coprocessors of central processing units (CPUs) and are activated only if tasks demand GPU computation. In a container environment, where resources can be shared among multiple users, GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single GPU. However, unlike CPUs and memory, GPUs cannot logically multiplex their resources. Additionally, GPU memory… More >

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Mengdi Yang, Feng Yue, Weilong Wang, Xiangdong Meng, Lixin Wang, Pengyu Han, Haoran He, Benzheng Yuan, Zhiqiang Fan, Chenhui Wang, Qiming Du, Danyang Zheng, Xuefei Feng, Zheng Shan^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2215-2233, 2025, DOI:10.32604/cmc.2024.058163 - 17 February 2025
Abstract Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively… More >

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Na Deng, Jiuan Zhang^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2235-2254, 2025, DOI:10.32604/cmc.2024.058103 - 17 February 2025
Abstract Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee More >

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Usman Mahmood Malik¹, Muhammad Awais Javed², Ahmad Naseem Alvi², Mohammed Alkhathami^3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2255-2274, 2025, DOI:10.32604/cmc.2025.061254 - 17 February 2025
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing, and data storage services which are required for several 6G applications. Artificial Intelligence (AI) algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability. In this paper, the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers (POMH) in which larger tasks are divided into smaller subtasks and processed in parallel, hence expediting task completion. However, using POMH presents challenges… More >

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Xinlai Xing, Junliang Chen^*, Xiaochuan Zhang, Shuran Zhou, Runqing Zhang
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2275-2292, 2025, DOI:10.32604/cmc.2024.056877 - 17 February 2025
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets More >

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Gang Xu^1,2,6, Xinyu Fan¹, Xiu-Bo Chen², Xin Liu⁴, Zongpeng Li⁵, Yanhui Mao^6,7, Kejia Zhang^3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2293-2309, 2025, DOI:10.32604/cmc.2024.057882 - 17 February 2025
Abstract Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure,… More >

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Jawad Ahmad Ansari¹, Mohamad Khairi Ishak^2,*, Khalid Ammar²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2311-2330, 2025, DOI:10.32604/cmc.2025.057848 - 17 February 2025
Abstract Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and… More >

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Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025
Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

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Fengju Zhang¹, Kai Zhu^2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2353-2372, 2025, DOI:10.32604/cmc.2024.058944 - 17 February 2025
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >

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Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2373-2388, 2025, DOI:10.32604/cmc.2025.060745 - 17 February 2025
Abstract Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging… More >

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Wenju Wang¹, Zehua Gu^1,*, Bang Tang¹, Sen Wang², Jianfei Hao²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2389-2414, 2025, DOI:10.32604/cmc.2024.057392 - 17 February 2025
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A More >

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Shiren Ye, Qi Meng, Shuo Zhang, Hui Wang^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2415-2429, 2025, DOI:10.32604/cmc.2024.058250 - 17 February 2025
（This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
Abstract WCE (Wireless Capsule Endoscopy) is a new technology that combines computer vision and medicine, allowing doctors to visualize the conditions inside the intestines, achieving good diagnostic results. However, due to the complex intestinal environment and limited pixel resolution of WCE videos, lesions are not easily detectable, and it takes an experienced doctor 1–2 h to analyze a complete WCE video. The use of computer-aided diagnostic methods, assisting or even replacing manual WCE diagnosis, has significant application value. In response to the issue of intestinal lesion detection in WCE videos, this paper proposes a multi-scale feature… More >

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Libo Han^1,2, Sha Tao^1,2, Wen Xia³, Weixin Sun³, Li Yan³, Wanlin Gao^1,2,3,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2431-2449, 2025, DOI:10.32604/cmc.2024.057833 - 17 February 2025
Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >

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Honglin Wang¹, Zitong Shi^2,*, Cheng Zhu³
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2451-2474, 2025, DOI:10.32604/cmc.2024.058474 - 17 February 2025
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different… More >

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Soyoung Joo^#, So-Hyun Park^#, Hye-Yeon Shim, Ye-Sol Oh, Il-Gu Lee^*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2475-2494, 2025, DOI:10.32604/cmc.2025.058963 - 17 February 2025
（This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
Abstract As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by More >

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Jeng-Shyang Pan^1,2, Na Yu¹, Shu-Chuan Chu^1,*, An-Ning Zhang¹, Bin Yan³, Junzo Watada⁴
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2495-2520, 2025, DOI:10.32604/cmc.2024.058450 - 17 February 2025
Abstract The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s… More >

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Bolin Zhang, Ruichun Gu^*, Haiying Liu
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2521-2535, 2025, DOI:10.32604/cmc.2024.060331 - 17 February 2025
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >

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Wenkai Zhang^1,2, Hao Zhang^1,2, Xianming Liu¹, Xiaoyu Guo^1,2, Xinzhe Wang¹, Shuiwang Li^1,2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2537-2554, 2025, DOI:10.32604/cmc.2024.059000 - 17 February 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >

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Eunsam Kim¹, Choonhwa Lee^2,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2555-2581, 2025, DOI:10.32604/cmc.2025.059682 - 17 February 2025
（This article belongs to the Special Issue: Advanced Intelligent Technologies for Networking and Collaborative Systems)
Abstract Personal video recorders (PVRs) have altered the way users consume television (TV) content by allowing users to record programs and watch them at their convenience, overcoming the constraints of live broadcasting. However, standalone PVRs are limited by their individual storage capacities, restricting the number of programs they can store. While online catch-up TV services such as Hulu and Netflix mitigate this limitation by offering on-demand access to broadcast programs shortly after their initial broadcast, they require substantial storage and network resources, leading to significant infrastructural costs for service providers. To address these challenges, we propose… More >

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Xin Fan^1,2, Zhenlei Fu^1,2,*, Jian Shu^1,2, Zuxiong Shen^1,2, Yun Ge^1,2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2583-2607, 2025, DOI:10.32604/cmc.2024.057695 - 17 February 2025
Abstract Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of… More >

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Jiajia Liu^1,*, Peng Xie², Wei Li², Bo Tang², Jianhua Liu²
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2609-2635, 2025, DOI:10.32604/cmc.2024.058810 - 17 February 2025
（This article belongs to the Special Issue: Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control)
Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >

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Reshma Siyal¹, Muhammad Asim^2,*, Long Jun¹, Mohammed Elaffendi², Sundas Iftikhar³, Rana Alnashwan⁴, Samia Allaoua Chelloug^4,*
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2637-2664, 2025, DOI:10.32604/cmc.2025.058717 - 17 February 2025
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. More >

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