Open Access
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2023 Impact Factor 2.1; Scopus CiteScore (Impact per Publication 2023): 5.3; SNIP (Source Normalized Impact per Paper 2023): 0.73; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3643-3692, 2025, DOI:10.32604/cmc.2025.061749 - 06 March 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 The automotive sector is crucial in modern society, facilitating essential transportation needs across personal, commercial, and logistical domains while significantly contributing to national economic development and employment generation. The transformative impact of Artificial Intelligence (AI) has revolutionised multiple facets of the automotive industry, encompassing intelligent manufacturing processes, diagnostic systems, control mechanisms, supply chain operations, customer service platforms, and traffic management solutions. While extensive research exists on the above aspects of AI applications in automotive contexts, there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research. This review introduces a… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3693-3740, 2025, DOI:10.32604/cmc.2025.060793 - 06 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Pill image recognition is an important field in computer vision. It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety. This survey examines the current state of pill image recognition, focusing on advancements, methodologies, and the challenges that remain unresolved. It provides a comprehensive overview of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and aims to explore the ongoing difficulties in the field. We summarize and classify the methods used in each article, compare the strengths and More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3741-3771, 2025, DOI:10.32604/cmc.2025.061998 - 06 March 2025
（This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)
Abstract Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data. Recently, both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions. With the growth in popularity of deep learning and ensemble learning algorithms, they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data. Ensemble deep learning has exhibited significant performance in enhancing learning generalization through… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3773-3796, 2025, DOI:10.32604/cmc.2024.059455 - 06 March 2025
Abstract In recent years, with the rapid development of deep learning technology, relational triplet extraction techniques have also achieved groundbreaking progress. Traditional pipeline models have certain limitations due to error propagation. To overcome the limitations of traditional pipeline models, recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework. To support future research, this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction. The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3797-3820, 2025, DOI:10.32604/cmc.2025.062079 - 06 March 2025
Abstract Molecular dynamics (MD) is a powerful method widely used in materials science and solid-state physics. The accuracy of MD simulations depends on the quality of the interatomic potentials. In this work, a special class of exact solutions to the equations of motion of atoms in a body-centered cubic (bcc) lattice is analyzed. These solutions take the form of delocalized nonlinear vibrational modes (DNVMs) and can serve as an excellent test of the accuracy of the interatomic potentials used in MD modeling for bcc crystals. The accuracy of the potentials can be checked by comparing the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3821-3841, 2025, DOI:10.32604/cmc.2025.059937 - 06 March 2025
（This article belongs to the Special Issue: Multiscale and Multiphysics Computational Methods of Heterogeneous Materials and Structures)
Abstract Magneto-electro-elastic (MEE) materials are widely utilized across various fields due to their multi-field coupling effects. Consequently, investigating the coupling behavior of MEE composite materials is of significant importance. The traditional finite element method (FEM) remains one of the primary approaches for addressing such issues. However, the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision, which inevitably leads to a reduction in computational efficiency. To enhance the computational accuracy and efficiency of the FEM for heterogeneous… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3843-3872, 2025, DOI:10.32604/cmc.2025.062552 - 06 March 2025
（This article belongs to the Special Issue: Emerging Multimedia Tools for Software Engineering Process Optimization)
Abstract Design patterns offer reusable solutions for common software issues, enhancing quality. The advent of generative large language models (LLMs) marks progress in software development, but their efficacy in applying design patterns is not fully assessed. The recent introduction of generative large language models (LLMs) like ChatGPT and CoPilot has demonstrated significant promise in software development. They assist with a variety of tasks including code generation, modeling, bug fixing, and testing, leading to enhanced efficiency and productivity. Although initial uses of these LLMs have had a positive effect on software development, their potential influence on the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3873-3890, 2025, DOI:10.32604/cmc.2025.060918 - 06 March 2025
Abstract Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data, while the learned representation is difficult to maintain the underlying structure hidden in the origin samples, especially the high-order neighbor relationship between samples. To overcome the above challenges, this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model. We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module. By this design, the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix; then, More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3891-3905, 2025, DOI:10.32604/cmc.2025.060056 - 06 March 2025
Abstract For optimization algorithms, the most important consideration is their global optimization performance. Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed. For stochastic optimization algorithms based on population search methods, the search speed and solution quality are always contradictory. Suppose that the random range of the group search is larger; in that case, the probability of the algorithm converging to the global optimal solution is also greater, but the search speed will inevitably slow. The smaller… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025
（This article belongs to the Special Issue: Advanced Medical Imaging Techniques Using Generative Artificial Intelligence)
Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025
Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3943-3964, 2025, DOI:10.32604/cmc.2025.060788 - 06 March 2025
Abstract Image captioning, the task of generating descriptive sentences for images, has advanced significantly with the integration of semantic information. However, traditional models still rely on static visual features that do not evolve with the changing linguistic context, which can hinder the ability to form meaningful connections between the image and the generated captions. This limitation often leads to captions that are less accurate or descriptive. In this paper, we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context. Our model strengthens the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025
Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3983-4002, 2025, DOI:10.32604/cmc.2025.059103 - 06 March 2025
Abstract Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes. To address these issues, a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption (FHE) is proposed. In the proposed scheme, the functional relationship is established by Box-Muller, Discrete Gaussian Distribution Function (DGDF) and Uniform Random Distribution Function (URDF) are used to improve the security and efficiency of key generation. Subsequently, the data preprocessing function is introduced to perform cleaning, deduplication, and normalization operations on the transaction data of multi-key signature, and it is classified into… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4003-4019, 2025, DOI:10.32604/cmc.2025.057443 - 06 March 2025
（This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
Abstract In natural language processing (NLP), managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models, leading to practical inefficiencies. To address this challenge, we introduce AdaptForever, a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism. Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities, facilitating effective knowledge transfer from previous tasks to new ones. By combining Elastic Weight Consolidation (EWC) for knowledge preservation with specialized regularization terms, AdaptForever successfully maintains More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4021-4039, 2025, DOI:10.32604/cmc.2025.061267 - 06 March 2025
（This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)
Abstract The proliferation of Internet of Things (IoT) devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing. Nevertheless, the distributed nature of edge computing presents substantial security challenges, rendering it a prominent target for sophisticated malware attacks. Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources. This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation (GENOME) framework, a novel solution that is intended to improve the performance of malware detection and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4041-4067, 2025, DOI:10.32604/cmc.2025.059832 - 06 March 2025
Abstract As the trend to use the latest machine learning models to automate requirements engineering processes continues, security requirements classification is tuning into the most researched field in the software engineering community. Previous literature studies have proposed numerous models for the classification of security requirements. However, adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms. Moreover, most of the researchers focus only on the classification of requirements with security keywords. They did not consider other nonfunctional requirements (NFR) directly or… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4069-4091, 2025, DOI:10.32604/cmc.2025.059931 - 06 March 2025
Abstract Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information. However, in low-light scenarios, the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene. At this time, relying solely on the target saliency information provided by infrared images is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. The method is based on the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4093-4116, 2025, DOI:10.32604/cmc.2025.059615 - 06 March 2025
Abstract The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4117-4136, 2025, DOI:10.32604/cmc.2025.059528 - 06 March 2025
Abstract Predicting information dissemination on social media, specifically users’ reposting behavior, is crucial for applications such as advertising campaigns. Conventional methods use deep neural networks to make predictions based on features related to user topic interests and social preferences. However, these models frequently fail to account for the difficulties arising from limited training data and model size, which restrict their capacity to learn and capture the intricate patterns within microblogging data. To overcome this limitation, we introduce a novel model Adapt pre-trained Large Language model for Reposting Prediction (ALL-RP), which incorporates two key steps: (1)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4137-4159, 2025, DOI:10.32604/cmc.2025.060194 - 06 March 2025
Abstract The High-Temperature Biaxial Testing Apparatus (HTBTA) is a critical tool for studying the damage and failure mechanisms of heat-resistant composite materials under extreme conditions. However, existing methods for managing and monitoring such apparatus face challenges, including limited real-time modeling capabilities, inadequate integration of multi-source data, and inefficiencies in human-machine interaction. To address these gaps, this study proposes a novel digital twin-driven framework for HTBTA, encompassing the design, validation, operation, and maintenance phases. By integrating advanced modeling techniques, such as finite element analysis and Long Short-Term Memory (LSTM) networks, the digital twin enables high-fidelity simulation, real-time… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4161-4179, 2025, DOI:10.32604/cmc.2025.061497 - 06 March 2025
（This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)
Abstract A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025
（This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)
Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4219-4236, 2025, DOI:10.32604/cmc.2025.060567 - 06 March 2025
Abstract Traffic datasets exhibit complex spatiotemporal characteristics, including significant fluctuations in traffic volume and intricate periodical patterns, which pose substantial challenges for the accurate forecasting and effective management of traffic conditions. Traditional forecasting models often struggle to adequately capture these complexities, leading to suboptimal predictive performance. While neural networks excel at modeling intricate and nonlinear data structures, they are also highly susceptible to overfitting, resulting in inefficient use of computational resources and decreased model generalization. This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4237-4261, 2025, DOI:10.32604/cmc.2025.059262 - 06 March 2025
Abstract Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics. However, metric learning methods often suffer from high sensitivity, leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior. Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples. However, it still faces the challenge of overfitting. Although curriculum learning-based adversarial training somewhat mitigates this issue, challenges still exist, such as the lack of a quantitative… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025
（This article belongs to the Special Issue: Securing the Future: Innovations and Challenges in Next-Generation Network Security)
Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4287-4300, 2025, DOI:10.32604/cmc.2025.061661 - 06 March 2025
Abstract In the domain of knowledge graph embedding, conventional approaches typically transform entities and relations into continuous vector spaces. However, parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations. In particular, resource-intensive embeddings often lead to increased computational costs, and may limit scalability and adaptability in practical environments, such as in low-resource settings or real-world applications. This paper explores an approach to knowledge graph representation learning that leverages small, reserved entities and relation sets for parameter-efficient embedding. We introduce a hierarchical attention network designed to refine More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025
Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025
Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4339-4369, 2025, DOI:10.32604/cmc.2025.056100 - 06 March 2025
（This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)
Abstract This research presents an analysis of smart grid units to enhance connected units’ security during data transmissions. The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring, data expansion, control association, throughput, and losses. In addition, all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks. Moreover, the quantitative analysis of the optimization algorithm is discussed concerning two case studies, thereby achieving early convergence at reduced complexities. The suggested method ensures that each communication More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4389-4408, 2025, DOI:10.32604/cmc.2025.060357 - 06 March 2025
Abstract With the rapid development of Internet of Things technology, the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast, bringing unprecedented challenges to the field of network security, especially in identifying malicious attacks. However, due to the uneven distribution of network traffic data, particularly the imbalance between attack traffic and normal traffic, as well as the imbalance between minority class attacks and majority class attacks, traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data. To effectively tackle this challenge, we have designed a lightweight… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696 - 06 March 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.059880 - 06 March 2025
Abstract Opportunistic mobile crowdsensing (MCS) non-intrusively exploits human mobility trajectories, and the participants’ smart devices as sensors have become promising paradigms for various urban data acquisition tasks. However, in practice, opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform. On the one hand, participants face uncertainties in conducting MCS tasks, including their mobility and implicit interactions among participants, and participants’ economic returns given by the MCS data platform are determined by not only their own actions but also other participants’ strategic actions. On the other hand, the platform can… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4469-4484, 2025, DOI:10.32604/cmc.2025.062675 - 06 March 2025
Abstract Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4485-4501, 2025, DOI:10.32604/cmc.2024.059773 - 06 March 2025
Abstract Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images. This paper approaches the topic as an optimization problem and uses the bald eagle search (BES) algorithm to achieve optimal results. In our proposed model, gamma correction and Retinex address color cast issues and enhance image edges and details. The final enhanced image is obtained through color balancing. The BES algorithm seeks the optimal solution through the selection, search, and swooping stages. However, it is prone to getting stuck in local optima and converges slowly. To overcome these limitations, we propose… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4503-4533, 2025, DOI:10.32604/cmc.2024.059864 - 06 March 2025
Abstract The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution, particularly within multi-area interconnected power grids. This complexity necessitates the employment of distributed solution methodologies, which are not only essential but also highly desirable. In the realm of computational modelling, the multi-area economic dispatch problem (MAED) can be formulated as a linearly constrained separable convex optimization problem. The proximal point algorithm (PPA) is particularly adept at addressing such mathematical constructs effectively. This study introduces parallel (PPPA) and serial (SPPA) variants of the PPA… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4535-4554, 2025, DOI:10.32604/cmc.2025.060935 - 06 March 2025
（This article belongs to the Special Issue: Exploring Recent Trends and Advances in Sensors Cybersecurity)
Abstract The increasing adoption of Industrial Internet of Things (IIoT) systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems (IDS) to be effective. However, existing datasets for IDS training often lack relevance to modern IIoT environments, limiting their applicability for research and development. To address the latter gap, this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems. As a consequence, that can be used by an IDS to detect imminent threats. Likewise, HiTar-2024 was generated using the AREZZO simulator, which replicates realistic smart manufacturing scenarios.… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025
（This article belongs to the Special Issue: Intelligent Soft Computing Techniques for Enhancing Wireless Networks with Unmanned Aerial Vehicles)
Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4573-4591, 2025, DOI:10.32604/cmc.2025.059184 - 06 March 2025
（This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
Abstract In the context of security systems, adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel. Most studies focus on optimizing base station deployment under the assumption of static obstacles, aiming to maximize the perception coverage of wireless RF (Radio Frequency) signals and reduce positioning blind spots. However, in practical security systems, obstacles are subject to change, necessitating the consideration of base station deployment in dynamic environments. Nevertheless, research in this area still needs to be conducted. This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm (DIE-BDA)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4593-4629, 2025, DOI:10.32604/cmc.2025.061196 - 06 March 2025
Abstract Virtual Power Plants (VPPs) are integral to modern energy systems, providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data. Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations. We introduce the Memory-Enhanced Autoencoder with Adversarial Training (MemAAE) model to overcome these limitations, designed explicitly for robust anomaly detection in VPP environments. The MemAAE model integrates three principal components: an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors, an adversarial training module that… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4649-4667, 2025, DOI:10.32604/cmc.2025.059472 - 06 March 2025
Abstract Image classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated learning approach that incorporates privacy-preserving techniques. First, we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size, thereby enhancing the model’s generalization capabilities in few-shot contexts. Second, we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters, perturbing the transmitted parameters to ensure More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4669-4690, 2025, DOI:10.32604/cmc.2025.058495 - 06 March 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4691-4708, 2025, DOI:10.32604/cmc.2025.061252 - 06 March 2025
Abstract Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools. The manual forgery localization is often reliant on forensic expertise. In recent times, machine learning (ML) and deep learning (DL) have shown promising results in automating image forgery localization. However, the ML-based method relies on hand-crafted features. Conversely, the DL method automatically extracts shallow spatial features to enhance the accuracy. However, DL-based methods lack the global co-relation of the features due to this… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4709-4740, 2025, DOI:10.32604/cmc.2025.058894 - 06 March 2025
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Cyclic-system-based optimization (CSBO) is an innovative metaheuristic algorithm (MHA) that draws inspiration from the workings of the human blood circulatory system. However, CSBO still faces challenges in solving complex optimization problems, including limited convergence speed and a propensity to get trapped in local optima. To improve the performance of CSBO further, this paper proposes improved cyclic-system-based optimization (ICSBO). First, in venous blood circulation, an adaptive parameter that changes with evolution is introduced to improve the balance between convergence and diversity in this stage and enhance the exploration of search space. Second, the simplex method strategy… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4741-4757, 2025, DOI:10.32604/cmc.2025.059536 - 06 March 2025
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract The proliferation of rumors on social media has caused serious harm to society. Although previous research has attempted to use deep learning methods for rumor detection, they did not simultaneously consider the two key features of temporal and spatial domains. More importantly, these methods struggle to automatically generate convincing explanations for the detection results, which is crucial for preventing the further spread of rumors. To address these limitations, this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models (LLMs) to automatically generate explanations for the detection results.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4777-4795, 2025, DOI:10.32604/cmc.2025.060380 - 06 March 2025
Abstract Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture. Existing research has shown that attackers can compromise user privacy and security by stealing model parameters. Therefore, differential privacy is applied in federated learning to further address malicious issues. However, the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance optimization. Therefore, we propose an adaptive adjusted differential privacy federated learning method. First, a dynamic adaptive privacy budget allocation strategy… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4797-4820, 2025, DOI:10.32604/cmc.2025.060042 - 06 March 2025
Abstract With the rapid advancement of Voice over Internet Protocol (VoIP) technology, speech steganography techniques such as Quantization Index Modulation (QIM) and Pitch Modulation Steganography (PMS) have emerged as significant challenges to information security. These techniques embed hidden information into speech streams, making detection increasingly difficult, particularly under conditions of low embedding rates and short speech durations. Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals. To address these challenges, this paper proposes an Efficient Sliding Window… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4821-4839, 2025, DOI:10.32604/cmc.2025.059497 - 06 March 2025
Abstract Border Gateway Protocol (BGP), as the standard inter-domain routing protocol, is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems (AS). BGP nodes, communicating in a distributed dynamic environment, face several security challenges, with trust being one of the most important issues in inter-domain routing. Existing research, which performs trust evaluation when exchanging routing information to suppress malicious routing behavior, cannot meet the scalability requirements of BGP nodes. In this paper, we propose a blockchain-based trust model for inter-domain routing. Our model achieves scalability by allowing the master node of… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4841-4862, 2025, DOI:10.32604/cmc.2025.059733 - 06 March 2025
Abstract The self-attention mechanism of Transformers, which captures long-range contextual information, has demonstrated significant potential in image segmentation. However, their ability to learn local, contextual relationships between pixels requires further improvement. Previous methods face challenges in efficiently managing multi-scale features of different granularities from the encoder backbone, leaving room for improvement in their global representation and feature extraction capabilities. To address these challenges, we propose a novel Decoder with Multi-Head Feature Receptors (DMHFR), which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities: coarse, fine-grained, and full set.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4863-4880, 2025, DOI:10.32604/cmc.2025.059295 - 06 March 2025
Abstract To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4881-4912, 2025, DOI:10.32604/cmc.2025.061001 - 06 March 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity, capable of crippling critical infrastructures and disrupting services globally. As networks continue to expand and threats become more sophisticated, there is an urgent need for Intrusion Detection Systems (IDS) capable of handling these challenges effectively. Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics. This paper presents a novel approach for detecting unknown Distributed Denial of Service (DDoS) attacks by integrating Sliced Iterative Normalizing Flows (SINF) into IDS. SINF utilizes the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4913-4930, 2025, DOI:10.32604/cmc.2025.059770 - 06 March 2025
Abstract The surge of large-scale models in recent years has led to breakthroughs in numerous fields, but it has also introduced higher computational costs and more complex network architectures. These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization. To address this issue, various network compression techniques have been developed, such as network pruning. A typical pruning algorithm follows a three-step pipeline involving training, pruning, and retraining. Existing methods often directly set the pruned filters to zero during retraining, significantly reducing the parameter space. However, this… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4931-4948, 2025, DOI:10.32604/cmc.2025.060547 - 06 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Underwater target detection in forward-looking sonar (FLS) images is a challenging but promising endeavor. The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments. Considering the problems of low imaging resolution, complex background environment, and large changes in target imaging of underwater sonar images, this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture, named ProNet. It progressively captures the sensitive regions in the current image where potential effective targets may exist. Guided by this basic idea, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4949-4976, 2025, DOI:10.32604/cmc.2025.057693 - 06 March 2025
Abstract Towards optimal k-prototype discovery, k-means-like algorithms give us inspirations of central samples collection, yet the unstable seed samples selection, the hypothesis of a circle-like pattern, and the unknown K are still challenges, particularly for non-predetermined data patterns. We propose an adaptive k-prototype clustering method (kProtoClust) which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging. On behalf of a group of data samples, support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes, while inner samples become the first candidates for… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4977-4994, 2025, DOI:10.32604/cmc.2025.057359 - 06 March 2025
Abstract Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability, transparency, and trust in the community. Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction. Conventional design methodologies such as object-oriented design methodology (OODM) have been proposed for web-based application development, which facilitates code reuse, quantification, and security at the design level. However, OODM did not provide the feature of explainability in web-based decision-making systems. X-OODM modifies the OODM with added explainable models to introduce the explainability feature for… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4995-5017, 2025, DOI:10.32604/cmc.2025.059972 - 06 March 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5019-5033, 2025, DOI:10.32604/cmc.2025.058233 - 06 March 2025
Abstract Visual Place Recognition (VPR) technology aims to use visual information to judge the location of agents, which plays an irreplaceable role in tasks such as loop closure detection and relocation. It is well known that previous VPR algorithms emphasize the extraction and integration of general image features, while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks. To this end, this paper proposes a Domain-invariant Information Extraction and Optimization Network (DIEONet) for VPR. The core of the algorithm is a newly designed Domain-invariant Information Mining Module (DIMM)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5035-5055, 2025, DOI:10.32604/cmc.2025.058276 - 06 March 2025
Abstract Fingerprint features, as unique and stable biometric identifiers, are crucial for identity verification. However, traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks, potentially leading to user data leakage. Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data, effectively addressing privacy and security concerns. However, variations in fingerprint data due to factors such as region, ethnicity, sensor quality, and environmental conditions result in significant heterogeneity across clients. This heterogeneity adversely impacts the generalization ability of the global model, limiting its performance across… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025
Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5079-5095, 2025, DOI:10.32604/cmc.2025.059469 - 06 March 2025
Abstract In the RSSI-based positioning algorithm, regarding the problem of a great conflict between precision and cost, a low-power and low-cost synergic localization algorithm is proposed, where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness. In the ranging period, the power attenuation factor is obtained through the wireless channel modeling, and the RSSI value is transformed into distance. In the positioning period, the preferred reference nodes are used to calculate coordinates. In the position optimization period, Taylor… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5097-5113, 2025, DOI:10.32604/cmc.2025.059577 - 06 March 2025
Abstract Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum (NISQ) era, overlook the inter-layer operations can be further parallelized. Based on this, two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation process. Firstly, we introduce the Layered Topology Scheduling Approach (LTSA), which employs a greedy algorithm and leverages the principles of topological sorting in graph theory. LTSA allocates quantum gates to a layered structure, maximizing the concurrent execution of quantum gate operations. Secondly, the Layerwise Conflict Resolution Approach (LCRA) is proposed.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5135-5151, 2025, DOI:10.32604/cmc.2025.059610 - 06 March 2025
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Graph similarity learning aims to calculate the similarity between pairs of graphs. Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies, which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs. To address these issues, we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning. First, to tackle the problem of random augmentation disrupting the semantics and structure of the graph, we design a learnable augmentation method to selectively choose nodes and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5153-5167, 2025, DOI:10.32604/cmc.2024.058647 - 06 March 2025
Abstract Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5169-5184, 2025, DOI:10.32604/cmc.2025.061187 - 06 March 2025
Abstract Speech-face association aims to achieve identity matching between facial images and voice segments by aligning cross-modal features. Existing research primarily focuses on learning shared-space representations and computing one-to-one similarities between cross-modal sample pairs to establish their correlation. However, these approaches do not fully account for intra-class variations between the modalities or the many-to-many relationships among cross-modal samples, which are crucial for robust association modeling. To address these challenges, we propose a novel framework that leverages global information to align voice and face embeddings while effectively correlating identity information embedded in both modalities. First, we jointly… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5185-5204, 2025, DOI:10.32604/cmc.2025.059355 - 06 March 2025
Abstract Networking, storage, and hardware are just a few of the virtual computing resources that the infrastructure service model offers, depending on what the client needs. One essential aspect of cloud computing that improves resource allocation techniques is host load prediction. This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues, which add several minutes to response times. To solve this issue and accurately predict cloud capacity, cloud data centers use prediction algorithms. This permits dynamic cloud scalability while maintaining superior service quality. For host prediction, we therefore present a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5205-5220, 2025, DOI:10.32604/cmc.2025.059669 - 06 March 2025
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract Recently, a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement, yielding remarkable outcomes. Due to the intricate nature of imaging scenarios, including fluctuating noise levels and unpredictable environmental elements, these techniques do not fully resolve these challenges. We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture, merging the Convolutional Block Attention Module (CBAM) with the Transformer. Our model is capable of detecting more prominent features across both channel and spatial domains. We have conducted extensive More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5239-5256, 2025, DOI:10.32604/cmc.2025.059077 - 06 March 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract In the PSP (Pressure-Sensitive Paint), image deblurring is essential due to factors such as prolonged camera exposure times and high model velocities, which can lead to significant image blurring. Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources. To address these issues, this study proposes a deep learning-based approach tailored for PSP image deblurring. Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries, the images captured under such conditions exhibit distinctive non-uniform motion blur, presenting challenges for standard deep learning models utilizing convolutional… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025
Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5307-5324, 2025, DOI:10.32604/cmc.2024.060993 - 06 March 2025
Abstract With the development of the Semantic Web, the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex, understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task. At present, the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently. Therefore, we propose an Ontology Matching Method Based on the Gated Graph Attention Model (OM-GGAT). Firstly, the semantic knowledge related… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5325-5341, 2025, DOI:10.32604/cmc.2025.058802 - 06 March 2025
Abstract The integration of artificial intelligence (AI) with advanced power technologies is transforming energy system management, particularly through real-time data monitoring and intelligent decision-making driven by Artificial Intelligence Generated Content (AIGC). However, the openness of power system channels and the resource-constrained nature of power sensors have led to new challenges for the secure transmission of power data and decision instructions. Although traditional public key cryptographic primitives can offer high security, the substantial key management and computational overhead associated with these primitives make them unsuitable for power systems. To ensure the real-time and security of power data… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 06 March 2025
（This article belongs to the Special Issue: Artificial Intelligence Driven Innovations in Integrating Communications, Image and Signal Processing Applications)
Abstract Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and propose… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5363-5386, 2025, DOI:10.32604/cmc.2025.059709 - 06 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract The key to the success of few-shot semantic segmentation (FSS) depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set. Due to the few samples in the support set, FSS faces challenges such as intra-class differences, background (BG) mismatches between query and support sets, and ambiguous segmentation between the foreground (FG) and BG in the query set. To address these issues, The paper propose a multi-module network called CAMSNet, which includes four modules: the General Information Module (GIM), the Class Activation Map Aggregation (CAMA) module, the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718 - 06 March 2025
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract Due to the development of cloud computing and machine learning, users can upload their data to the cloud for machine learning model training. However, dishonest clouds may infer user data, resulting in user data leakage. Previous schemes have achieved secure outsourced computing, but they suffer from low computational accuracy, difficult-to-handle heterogeneous distribution of data from multiple sources, and high computational cost, which result in extremely poor user experience and expensive cloud computing costs. To address the above problems, we propose a multi-precision, multi-sourced, and multi-key outsourcing neural network training scheme. Firstly, we design a multi-precision More >
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