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: 2024 Impact Factor 1.7; Scopus CiteScore (Impact per Publication 2024): 6.1; SNIP (Source Normalized Impact per Paper 2024): 0.675; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1921-1950, 2025, DOI:10.32604/cmc.2025.067427 - 03 July 2025
（This article belongs to the Special Issue: Computational Analysis of Micro-Nano Material Mechanics and Manufacturing)
Abstract This review presents a comprehensive and forward-looking analysis of how Large Language Models (LLMs) are transforming knowledge discovery in the rational design of advanced micro/nano electrocatalyst materials. Electrocatalysis is central to sustainable energy and environmental technologies, but traditional catalyst discovery is often hindered by high complexity, fragmented knowledge, and inefficiencies. LLMs, particularly those based on Transformer architectures, offer unprecedented capabilities in extracting, synthesizing, and generating scientific knowledge from vast unstructured textual corpora. This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks, including automated information extraction from literature,… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1951-1979, 2025, DOI:10.32604/cmc.2025.065836 - 03 July 2025
（This article belongs to the Special Issue: Next-Generation 3D Printing: Material Innovation and Computational Methodologies)
Abstract Additive manufacturing (AM) and Three-dimensional (3D) printing build complex structures layer by layer, greatly expanding design possibilities. Traditional thermoplastics like Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Polyethylene Terephthalate Glycol (PETG) are widely used in 3D printing, but their non-renewable nature and limited biodegradability have driven research into plant fiber-based materials. These materials, mainly cellulose and lignin, come from sources like wood and agricultural waste, offering renewability, biodegradability, and biocompatibility. This paper reviews recent advances in plant fiber-based materials for 3D printing, covering their development from raw materials to applications. It highlights the sources,… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 1981-2013, 2025, DOI:10.32604/cmc.2025.064028 - 03 July 2025
Abstract The advent of 6G wireless technology, which offers previously unattainable data rates, very low latency, and compatibility with a wide range of communication devices, promises to transform the networking environment completely. The 6G wireless proposals aim to expand wireless communication’s capabilities well beyond current levels. This technology is expected to revolutionize how we communicate, connect, and use the power of the digital world. However, maintaining secure and efficient data management becomes crucial as 6G networks grow in size and complexity. This study investigates blockchain clustering and artificial intelligence (AI) approaches to ensure a reliable and… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2015-2060, 2025, DOI:10.32604/cmc.2025.063407 - 03 July 2025
（This article belongs to the Special Issue: Advances in Artificial Intelligence and Generative AI: Impacts on Multidisciplinary Applications)
Abstract This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were… More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2061-2119, 2025, DOI:10.32604/cmc.2025.066366 - 03 July 2025
Abstract The blockchain trilemma—balancing decentralization, security, and scalability—remains a critical challenge in distributed ledger technology. Despite significant advancements, achieving all three attributes simultaneously continues to elude most blockchain systems, often forcing trade-offs that limit their real-world applicability. This review paper synthesizes current research efforts aimed at resolving the trilemma, focusing on innovative consensus mechanisms, sharding techniques, layer-2 protocols, and hybrid architectural models. We critically analyze recent breakthroughs, including Directed Acyclic Graph (DAG)-based structures, cross-chain interoperability frameworks, and zero-knowledge proof (ZKP) enhancements, which aim to reconcile scalability with robust security and decentralization. Furthermore, we evaluate the trade-offs More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2121-2149, 2025, DOI:10.32604/cmc.2025.063373 - 03 July 2025
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Weather forecasting is crucial for agriculture, transportation, and industry. Deep Learning (DL) has greatly improved the prediction accuracy. Among them, Graph Neural Networks (GNNs) excel at processing weather data by establishing connections between regions. This allows them to understand complex patterns that traditional methods might miss. As a result, achieving more accurate predictions becomes possible. The paper reviews the role of GNNs in short-to medium-range weather forecasting. The methods are classified into three categories based on dataset differences. The paper also further identifies five promising research frontiers. These areas aim to boost forecasting precision and More >
Open Access
REVIEW
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2151-2188, 2025, DOI:10.32604/cmc.2025.065149 - 03 July 2025
Abstract Self-Explaining Autonomous Systems (SEAS) have emerged as a strategic frontier within Artificial Intelligence (AI), responding to growing demands for transparency and interpretability in autonomous decision-making. This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025, drawing upon 1380 documents indexed in Scopus. The analysis applies co-citation mapping, keyword co-occurrence, and author collaboration networks using VOSviewer, MASHA, and Python to examine scientific production, intellectual structure, and global collaboration patterns. The results indicate a sustained annual growth rate of 41.38%, with an h-index of 57 and an average of 21.97 citations… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2189-2216, 2025, DOI:10.32604/cmc.2025.066128 - 03 July 2025
（This article belongs to the Special Issue: Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations)
Abstract Tungsten carbide-based (WC-based) cemented carbides are widely recognized as high-performance tool materials. Traditionally, single metals such as cobalt (Co) or nickel (Ni) serve as the binder phase, providing toughness and structural integrity. Replacing this phase with high-entropy alloys (HEAs) offers a promising approach to enhancing mechanical properties and addressing sustainability challenges. However, the complex multi-element composition of HEAs complicates conventional experimental design, making it difficult to explore the vast compositional space efficiently. Traditional trial-and-error methods are time-consuming, resource-intensive, and often ineffective in identifying optimal compositions. In contrast, artificial intelligence (AI)-driven approaches enable rapid screening and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2217-2243, 2025, DOI:10.32604/cmc.2025.063289 - 03 July 2025
Abstract The global surge in electric vehicle (EV) adoption is proportionally expanding the EV charging station (EVCS) infrastructure, thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem. While ISO 15118 standardizes EV-EVCS communication, its underspecified security guidelines and the variability in manufacturers’ implementations frequently result in vulnerabilities that can disrupt charging services, compromise user data, or affect power grid stability. This research introduces a systematic black-box fuzzing methodology, accompanied by an open-source tool, to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118. The proposed… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2245-2271, 2025, DOI:10.32604/cmc.2025.064969 - 03 July 2025
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract Neural architecture search (NAS) optimizes neural network architectures to align with specific data and objectives, thereby enabling the design of high-performance models without specialized expertise. However, a significant limitation of NAS is that it requires extensive computational resources and time. Consequently, performing a comprehensive architectural search for each new dataset is inefficient. Given the continuous expansion of available datasets, there is an urgent need to predict the optimal architecture for the previously unknown datasets. This study proposes a novel framework that generates architectures tailored to unknown datasets by mapping architectures that have demonstrated effectiveness on… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2273-2303, 2025, DOI:10.32604/cmc.2025.063984 - 03 July 2025
Abstract In the era of big data, the growing number of real-time data streams often contains a lot of sensitive privacy information. Releasing or sharing this data directly without processing will lead to serious privacy information leakage. This poses a great challenge to conventional privacy protection mechanisms (CPPM). The existing data partitioning methods ignore the number of data replications and information exchanges, resulting in complex distance calculations and inefficient indexing for high-dimensional data. Therefore, CPPM often fails to meet the stringent requirements of efficiency and reliability, especially in dynamic spatiotemporal environments. Addressing this concern, we proposed… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2305-2330, 2025, DOI:10.32604/cmc.2025.065972 - 03 July 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
Abstract The shop scheduling problem with limited buffers has broad applications in real-world production scenarios, so this research direction is of great practical significance. However, there is currently little research on the hybrid flow shop scheduling problem with limited buffers (LBHFSP). This paper deeply investigates the LBHFSP to optimize the goal of the total completion time. To better solve the LBHFSP, a multi-level subpopulation-based particle swarm optimization algorithm (MLPSO) is proposed, which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO (particle swarm optimization) algorithm. In MLPSO, firstly, considering the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2331-2353, 2025, DOI:10.32604/cmc.2025.066382 - 03 July 2025
Abstract In the era of exponential growth of digital information, recommender algorithms are vital for helping users navigate vast data to find relevant items. Traditional approaches such as collaborative filtering and content-based methods have limitations in capturing complex, multi-faceted relationships in large-scale, sparse datasets. Recent advances in Graph Neural Networks (GNNs) have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks. However, existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships, leading to reduced recommendation diversity and limited generalization. To address these challenges,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2355-2373, 2025, DOI:10.32604/cmc.2025.065430 - 03 July 2025
Abstract The rapid advance of Connected-Automated Vehicles (CAVs) has led to the emergence of diverse delay-sensitive and energy-constrained vehicular applications. Given the high dynamics of vehicular networks, unmanned aerial vehicles-assisted mobile edge computing (UAV-MEC) has gained attention in providing computing resources to vehicles and optimizing system costs. We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption. We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm (DVCG-MWOA) to address this problem. A novel dynamic clustering algorithm is designed… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025
Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025
Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2411-2427, 2025, DOI:10.32604/cmc.2025.066810 - 03 July 2025
Abstract Computed Tomography (CT) reconstruction is essential in medical imaging and other engineering fields. However, blurring of the projection during CT imaging can lead to artifacts in the reconstructed images. Projection blur combines factors such as larger ray sources, scattering and imaging system vibration. To address the problem, we propose DeblurTomo, a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement. Specifically, we constructed a coordinate-based implicit neural representation reconstruction network, which can map the coordinates to the attenuation coefficient in the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2429-2454, 2025, DOI:10.32604/cmc.2025.066270 - 03 July 2025
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also raised critical privacy concerns, especially under tightening regulations such as the General Data Protection Regulation (GDPR), which explicitly restrict the processing of data that can re-identify individuals. Although existing anonymization approaches such as the Anonymizing AutoEncoder (AAE) can reduce the risk of re-identification, they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task (e.g., human activity recognition). This study proposes a novel sensor data anonymization method based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2455-2471, 2025, DOI:10.32604/cmc.2025.067413 - 03 July 2025
Abstract Food waste presents a major global environmental challenge, contributing to resource depletion, greenhouse gas emissions, and climate change. Black Soldier Fly Larvae (BSFL) offer an eco-friendly solution due to their exceptional ability to decompose organic matter. However, accurately identifying larval instars is critical for optimizing feeding efficiency and downstream applications, as different stages exhibit only subtle visual differences. This study proposes a real-time mobile application for automatic classification of BSFL larval stages. The system distinguishes between early instars (Stages 1–4), suitable for food waste processing and animal feed, and late instars (Stages 5–6), optimal for… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2473-2494, 2025, DOI:10.32604/cmc.2025.063347 - 03 July 2025
Abstract Differential evolution (DE) algorithms are simple and efficient evolutionary algorithms that perform well in various optimization problems. Unfortunately, they inevitably stagnate when differential evolutionary algorithms are used to solve complex problems (e.g., real-world artificial neural network (ANN) training problems). To resolve this issue, this paper proposes a framework based on an efficient elite centroid operator. It continuously monitors the current state of the population. Once stagnation is detected, two dedicated operators, centroid-based mutation (CM) and centroid-based crossover (CX), are executed to replace the classical mutation and binomial crossover operations in DE. CM and CX are… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2495-2512, 2025, DOI:10.32604/cmc.2025.063208 - 03 July 2025
Abstract The accurate prediction and analysis of emergencies in Urban Rail Transit Systems (URTS) are essential for the development of effective early warning and prevention mechanisms. This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data. To address issues related to data structuredness and missing values, we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network (ER-GAIN) for data structuring and imputation. Additionally, to mitigate the impact of imbalanced data on the predictive performance of emergencies, we introduced an Adaptive Boosting Ensemble… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025
（This article belongs to the Special Issue: Next-Generation Activity Recognition: Methods, Challenges, and Solutions)
Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2531-2547, 2025, DOI:10.32604/cmc.2025.064635 - 03 July 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract In recent years, fungal diseases affecting grape crops have attracted significant attention. Currently, the assessment of black rot severity mainly depends on the ratio of lesion area to leaf surface area. However, effectively and accurately segmenting leaf lesions presents considerable challenges. Existing grape leaf lesion segmentation models have several limitations, such as a large number of parameters, long training durations, and limited precision in extracting small lesions and boundary details. To address these issues, we propose an enhanced DeepLabv3+ model incorporating Strip Pooling, Content-Guided Fusion, and Convolutional Block Attention Module (SFC_DeepLabv3+), an enhanced lesion segmentation method based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2549-2572, 2025, DOI:10.32604/cmc.2025.066190 - 03 July 2025
Abstract Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems. However, it must also adhere to critical quantum constraints, notably the no-cloning theorem, which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography, secure communication, and error correction. While existing quantum circuit representations implicitly honor such constraints, they lack formal mechanisms for early-stage verification in software design. Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software. This paper presents a formal… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2573-2586, 2025, DOI:10.32604/cmc.2025.066249 - 03 July 2025
（This article belongs to the Special Issue: Computational Analysis of Micro-Nano Material Mechanics and Manufacturing)
Abstract The directional explosion behavior of finite volume water confined within nanochannels holds considerable potential for applications in precision nanofabrication and bioengineering. However, precise control of nanoscale mass transfer remains challenging in nanofluidics. This study examined the dynamic evolution of water clusters confined within a single-end-opened carbon nanotube (CNT) under pulsed electric field (EF) excitation, with a particular focus on the structural reorganization of hydrogen bond (H-bond) networks and dipole orientation realignment. Molecular dynamics simulations reveal that under the influence of pulsed EF, the confined water molecules undergo cooperative restructuring to maximize hydrogen bond formation through… More >
Graphic Abstract
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025
（This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)
Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025
（This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)
Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2677-2697, 2025, DOI:10.32604/cmc.2025.064629 - 03 July 2025
Abstract In response to the missed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects, a detection algorithm based on an improved You Only Look Once (YOLO)v8n network is proposed. First, a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual (DWR) module and a dilated reparameterization block (DRB) to replace the C2f module at the high level of the backbone network, enriching the gradient flow information and increasing the effective receptive field (ERF). Second, an efficient local attention (ELA) mechanism is fused with the high-level… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2699-2716, 2025, DOI:10.32604/cmc.2025.064541 - 03 July 2025
Abstract Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2733-2749, 2025, DOI:10.32604/cmc.2025.065205 - 03 July 2025
Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2789-2804, 2025, DOI:10.32604/cmc.2025.066304 - 03 July 2025
（This article belongs to the Special Issue: Advanced Trends in Vehicular Ad hoc Networks (VANETs))
Abstract As vehicular networks become increasingly pervasive, enhancing connectivity and reliability has emerged as a critical objective. Among the enabling technologies for advanced wireless communication, particularly those targeting low latency and high reliability, time synchronization is critical, especially in vehicular networks. However, due to the inherent mobility of vehicular environments, consistently exchanging synchronization packets with a fixed base station or access point is challenging. This issue is further exacerbated in signal shadowed areas such as urban canyons, tunnels, or large-scale indoor halls where other technologies, such as global navigation satellite system (GNSS), are unavailable. One-way synchronization… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2805-2826, 2025, DOI:10.32604/cmc.2025.066244 - 03 July 2025
Abstract As the group-buying model shows significant progress in attracting new users, enhancing user engagement, and increasing platform profitability, providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems. This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning, termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation (MAMGBR) model, specifically designed to optimize group-buying recommendations on e-commerce platforms. The core dataset of this study comes from the Chinese maternal and infant e-commerce platform “Beibei,” encompassing approximately 430,000 successful group-buying actions and… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2827-2846, 2025, DOI:10.32604/cmc.2025.065251 - 03 July 2025
Abstract The control of gas extraction in coal mines relies on the effectiveness of gas extraction. The main method of gas extraction is to drive drill pipes into the coal seam through a drilling rig and use technologies such as hydraulic fracturing to pre-extract gas in the drill holes. Therefore, the real-time detection of the drill pipe status is closely related to the effectiveness of gas extraction. To achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2847-2863, 2025, DOI:10.32604/cmc.2025.066373 - 03 July 2025
Abstract The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases. However, auscultation is highly subjective, making it challenging to analyze respiratory sounds accurately. Although deep learning has been increasingly applied to this task, most existing approaches have primarily relied on supervised learning. Since supervised learning requires large amounts of labeled data, recent studies have explored self-supervised and semi-supervised methods to overcome this limitation. However, these approaches have largely assumed a closed-set setting, where the classes present in the unlabeled data are considered identical to those in the labeled data. In contrast, this… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2865-2882, 2025, DOI:10.32604/cmc.2025.065362 - 03 July 2025
（This article belongs to the Special Issue: Privacy in the Digital Age: AI-Driven Image Encryption for Secure Data Transmission)
Abstract Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission. It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks. This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding, which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security. The traditional and state-of-the-art joint source-channel coding algorithms are selected More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2905-2933, 2025, DOI:10.32604/cmc.2025.065421 - 03 July 2025
Abstract Extracting data from visually rich documents and charts using traditional methods that rely on OCR-based parsing poses multiple challenges, including layout complexity in unstructured formats, limitations in recognizing visual elements, and the correlation between different parts of the documents, as well as domain-specific semantics. Simply extracting text is not sufficient; advanced reasoning capabilities are proving to be essential to analyze content and answer questions accurately. This paper aims to evaluate the ability of the Large Language Models (LLMs) to correctly answer questions about various types of charts, comparing their performance when using images as input… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025
（This article belongs to the Special Issue: Advanced Intelligent Technologies for Networking and Collaborative Systems)
Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2959-2974, 2025, DOI:10.32604/cmc.2025.064672 - 03 July 2025
Abstract Air quality estimation assesses the pollution level in the air, supports public health warnings, and is a valuable tool in environmental management. Although air sensors have proven helpful in this task, sensors are often expensive and difficult to install, while cameras are becoming more popular and accessible, from which images can be collected as data for deep learning models to solve the above task. This leads to another problem: several labeled images are needed to achieve high accuracy when deep-learning models predict air quality. In this research, we have three main contributions: (1) Collect and… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2975-2988, 2025, DOI:10.32604/cmc.2025.065047 - 03 July 2025
Abstract 3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems, enabling selective feature extraction from non-empty voxels while suppressing computational waste. Despite its theoretical efficiency advantages, practical implementations face under-explored limitations: the fixed geometric patterns of conventional sparse convolutional kernels inevitably process non-contributory positions during sliding-window operations, particularly in regions with uneven point cloud density. To address this, we propose Hierarchical Shape Pruning for 3D Sparse Convolution (HSP-S), which dynamically eliminates redundant kernel stripes through layer-adaptive thresholding. Unlike static soft pruning methods, HSP-S maintains trainable sparsity patterns by progressively… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2989-3011, 2025, DOI:10.32604/cmc.2025.062451 - 03 July 2025
Abstract As mobile edge computing continues to develop, the demand for resource-intensive applications is steadily increasing, placing a significant strain on edge nodes. These nodes are normally subject to various constraints, for instance, limited processing capability, a few energy sources, and erratic availability being some of the common ones. Correspondingly, these problems require an effective task allocation algorithm to optimize the resources through continued high system performance and dependability in dynamic environments. This paper proposes an improved Particle Swarm Optimization technique, known as IPSO, for multi-objective optimization in edge computing to overcome these issues. To this… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3013-3027, 2025, DOI:10.32604/cmc.2025.064147 - 03 July 2025
Abstract In the current era of intelligent technologies, comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring, emergency rescue, and agricultural plant protection. Owing to their exceptional flexibility and rapid deployment capabilities, unmanned aerial vehicles (UAVs) have emerged as the ideal platforms for accomplishing these tasks. This study proposes a swarm A*-guided Deep Q-Network (SADQN) algorithm to address the coverage path planning (CPP) problem for UAV swarms in complex environments. Firstly, to overcome the dependency of traditional modeling methods on regular terrain environments, this study proposes an improved cellular decomposition… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3029-3051, 2025, DOI:10.32604/cmc.2025.064660 - 03 July 2025
Abstract Deep learning (DL), derived from the domain of Artificial Neural Networks (ANN), forms one of the most essential components of modern deep learning algorithms. DL segmentation models rely on layer-by-layer convolution-based feature representation, guided by forward and backward propagation. A critical aspect of this process is the selection of an appropriate activation function (AF) to ensure robust model learning. However, existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning. Most current research on activation function design focuses on classification tasks using natural… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3053-3073, 2025, DOI:10.32604/cmc.2025.064930 - 03 July 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight,… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025
Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3105-3124, 2025, DOI:10.32604/cmc.2025.062628 - 03 July 2025
Abstract Kubernetes has become the dominant container orchestration platform, with widespread adoption across industries. However, its default pod-to-pod communication mechanism introduces security vulnerabilities, particularly IP spoofing attacks. Attackers can exploit this weakness to impersonate legitimate pods, enabling unauthorized access, lateral movement, and large-scale Distributed Denial of Service (DDoS) attacks. Existing security mechanisms such as network policies and intrusion detection systems introduce latency and performance overhead, making them less effective in dynamic Kubernetes environments. This research presents PodCA, an eBPF-based security framework designed to detect and prevent IP spoofing in real time while minimizing performance impact. PodCA… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3125-3148, 2025, DOI:10.32604/cmc.2025.064499 - 03 July 2025
Abstract In large-scale Wireless Rechargeable Sensor Networks (WRSN), traditional forward routing mechanisms often lead to reduced energy efficiency. To address this issue, this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing. The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area. Then, the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission. Relay nodes are selected layer by layer, starting from the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3149-3173, 2025, DOI:10.32604/cmc.2025.063255 - 03 July 2025
（This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)
Abstract Urdu, a prominent subcontinental language, serves as a versatile means of communication. However, its handwritten expressions present challenges for optical character recognition (OCR). While various OCR techniques have been proposed, most of them focus on recognizing printed Urdu characters and digits. To the best of our knowledge, very little research has focused solely on Urdu pure handwriting recognition, and the results of such proposed methods are often inadequate. In this study, we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks (CNN). Our proposed method utilizes convolutional layers… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3175-3192, 2025, DOI:10.32604/cmc.2025.065717 - 03 July 2025
Abstract As a model for the next generation of the Internet, the metaverse—a fully immersive, hyper-temporal virtual shared space—is transitioning from imagination to reality. At present, the metaverse has been widely applied in a variety of fields, including education, social entertainment, Internet of vehicles (IoV), healthcare, and virtual tours. In IoVs, researchers primarily focus on using the metaverse to improve the traffic safety of vehicles, while paying limited attention to passengers’ social needs. At the same time, Social Internet of Vehicles (SIoV) introduces the concept of social networks in IoV to provide better resources and services… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3193-3215, 2025, DOI:10.32604/cmc.2025.065230 - 03 July 2025
（This article belongs to the Special Issue: Cutting-Edge Machine Learning and AI Innovations in Medical Imaging Diagnosis)
Abstract One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients. Traditionally, urological pathologists perform the grading by scoring the morphological pattern, known as the Gleason pattern, in histopathology images. However, this manual grading is highly subjective, suffers intra- and inter-pathologist variability and lacks reproducibility. An automated grading system could be more efficient, with no subjectivity and higher accuracy and reproducibility. Automated methods presented previously… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3217-3240, 2025, DOI:10.32604/cmc.2025.065334 - 03 July 2025
Abstract Ride-hailing (e.g., DiDi and Uber) has become an important tool for modern urban mobility. To improve the utilization efficiency of ride-hailing vehicles, a novel query method, called Approachable k-nearest neighbor (A-kNN), has recently been proposed in the industry. Unlike traditional kNN queries, A-kNN considers not only the road network distance but also the availability status of vehicles. In this context, even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location. The V-Tree-based query method, due to its structural characteristics, is capable of efficiently finding k-nearest moving objects within… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3241-3257, 2025, DOI:10.32604/cmc.2025.063723 - 03 July 2025
Abstract On-device Artificial Intelligence (AI) accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field, where frequent retraining is crucial due to frequent production changes. Batch normalization (BN) is fundamental to training convolutional neural networks (CNNs), but its implementation in compact accelerator chips remains challenging due to computational complexity, particularly in calculating statistical parameters and gradients across mini-batches. Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources, limiting their practical deployment. We present a hardware-optimized BN accelerator… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3259-3280, 2025, DOI:10.32604/cmc.2025.063547 - 03 July 2025
Abstract The Pressure Sensitive Paint Technique (PSP) has gained attention in recent years because of its significant benefits in measuring surface pressure on wind tunnel models. However, in the post-processing process of PSP images, issues such as pressure taps, paint peeling, and contamination can lead to the loss of pressure data on the image, which seriously affects the subsequent calculation and analysis of pressure distribution. Therefore, image inpainting is particularly important in the post-processing process of PSP images. Deep learning offers new methods for PSP image inpainting, but some basic characteristics of convolutional neural networks (CNNs)… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3281-3304, 2025, DOI:10.32604/cmc.2025.063646 - 03 July 2025
Abstract Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps. The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models, CNN + XGBoost and KNN + XGBoost. To address the class imbalance issue, SMOTE (Synthetic Minority Over-sampling Technique) was used to preprocess the dataset, creating synthetic samples of the minority class (malware) to balance the training set. XGBoost was then used to choose the most essential features for separating malware from benign programs. The models were trained and tested using 6-fold cross-validation, measuring accuracy, precision, recall,… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3305-3319, 2025, DOI:10.32604/cmc.2025.065665 - 03 July 2025
Abstract Zero Trust Network (ZTN) enhances network security through strict authentication and access control. However, in the ZTN, optimizing flow control to improve the quality of service is still facing challenges. Software Defined Network (SDN) provides solutions through centralized control and dynamic resource allocation, but the existing scheduling methods based on Deep Reinforcement Learning (DRL) are insufficient in terms of convergence speed and dynamic optimization capability. To solve these problems, this paper proposes DRL-AMIR, which is an efficient flow scheduling method for software defined ZTN. This method constructs a flow scheduling optimization model that comprehensively considers… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3321-3343, 2025, DOI:10.32604/cmc.2025.065297 - 03 July 2025
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3345-3369, 2025, DOI:10.32604/cmc.2025.065279 - 03 July 2025
（This article belongs to the Special Issue: Beyond the Surface: Exploring the Depths of Deep Learning in Face Recognition)
Abstract Skull structures are important for biomechanical head simulations, but they are mostly reconstructed from medical images. These reconstruction methods harm the human body and have a long processing time. Currently, skull structures can be straightforwardly predicted from the head, but a full head shape must be available. Most scanning devices can only capture the face shape. Consequently, a method that can quickly predict the full skull structures from the face is necessary. In this study, a novel face-to-skull prediction procedure is introduced. Given a three-dimensional (3-D) face shape, a skull mesh could be predicted so… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3371-3391, 2025, DOI:10.32604/cmc.2025.065153 - 03 July 2025
Abstract The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and real-time decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished),… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3393-3406, 2025, DOI:10.32604/cmc.2025.062643 - 03 July 2025
（This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)
Abstract Detection of Autism Spectrum Disorder (ASD) is a crucial area of research, representing a foundational aspect of psychological studies. The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years. Interdisciplinary efforts have further propelled research into detection methods. Consequently, this study aims to contribute to both the fields of psychology and computer science. Specifically, the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder. This study is structured into two distinct phases: data preprocessing and… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3407-3429, 2025, DOI:10.32604/cmc.2025.065872 - 03 July 2025
Abstract While automatic image captioning systems have made notable progress in the past few years, generating captions that fully convey sentiment remains a considerable challenge. Although existing models achieve strong performance in visual recognition and factual description, they often fail to account for the emotional context that is naturally present in human-generated captions. To address this gap, we propose the Sentiment-Driven Caption Generator (SDCG), which combines transformer-based visual and textual processing with multi-level fusion. RoBERTa is used for extracting sentiment from textual input, while visual features are handled by the Vision Transformer (ViT). These features are More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025
Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3459-3476, 2025, DOI:10.32604/cmc.2025.064103 - 03 July 2025
Abstract Federated learning has emerged as an essential technique of protecting privacy since it allows clients to train models locally without explicitly exchanging sensitive data. Extensive research has been conducted on the issue of data heterogeneity in federated learning, but effective model training with severely imbalanced label distributions remains an unexplored area. This paper presents a novel Cluster Federated Learning Algorithm with Intra-cluster Correction (CFIC). First, CFIC selects samples from each cluster during each round of sampling, ensuring that no single category of data dominates the model training. Second, in addition to updating local models, CFIC… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025
（This article belongs to the Special Issue: Cutting-Edge Machine Learning and AI Innovations in Medical Imaging Diagnosis)
Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3503-3517, 2025, DOI:10.32604/cmc.2025.064544 - 03 July 2025
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search (NAS) algorithms designed to optimize neural network structures. However, these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance. Traditional NAS approaches, which rely on exhaustive evaluations and large training datasets, are inefficient for solving complex image classification tasks within limited time frames. To address these challenges, this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models, specifically using supernet to… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3519-3539, 2025, DOI:10.32604/cmc.2025.066212 - 03 July 2025
Abstract The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3541-3557, 2025, DOI:10.32604/cmc.2025.064394 - 03 July 2025
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities. A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images. Furthermore, the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations. Thus, it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors. The proposed method offers additional robust feature representations by imposing a limiting More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025
Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3577-3603, 2025, DOI:10.32604/cmc.2025.065326 - 03 July 2025
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract Centrifugal Pumps (CPs) are critical machine components in many industries, and their efficient operation and reliable Fault Diagnosis (FD) are essential for minimizing downtime and maintenance costs. This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps. The proposed method combines Wavelet Coherent Analysis (WCA) and Stockwell Transform (S-transform) scalograms with Sobel and non-local means filters, effectively capturing complex fault signatures from vibration signals. Using Convolutional Neural Network (CNN) for feature extraction, the method transforms these scalograms into image inputs, enabling the recognition of More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025
Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3623-3648, 2025, DOI:10.32604/cmc.2025.063985 - 03 July 2025
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multi-dimensional vision identification technology adapted to the situation in large indoor and underground spaces. With the expansion of large shopping malls and underground urban spaces (UUS), there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation, remodeling, and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps. The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3649-3672, 2025, DOI:10.32604/cmc.2025.066267 - 03 July 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
Abstract It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage. This paper studies the flexible job shop scheduling problem (FJSP) with the objective of material kitting, where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process. In this study, we consider completion time variance and maximum completion time as scheduling objectives, continue the weighted summation process for multiple objectives, and design adaptive weighted summation parameters… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3673-3705, 2025, DOI:10.32604/cmc.2025.064161 - 03 July 2025
Abstract In this study, we investigated privacy-preserving ID3 Decision Tree (PPID3) training and inference based on fully homomorphic encryption (FHE), which has not been actively explored due to the high computational cost associated with managing numerous child nodes in an ID3 tree. We propose HEaaN-ID3, a novel approach to realize PPID3 using the Cheon-Kim-Kim-Song (CKKS) scheme. HEaaN-ID3 is the first FHE-based ID3 framework that completes both training and inference without any intermediate decryption, which is especially valuable when decryption keys are inaccessible or a single-cloud security domain is assumed. To enhance computational efficiency, we adopt a… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3707-3729, 2025, DOI:10.32604/cmc.2025.062795 - 03 July 2025
Abstract Cluster-based models have numerous application scenarios in vehicular ad-hoc networks (VANETs) and can greatly help improve the communication performance of VANETs. However, the frequent movement of vehicles can often lead to changes in the network topology, thereby reducing cluster stability in urban scenarios. To address this issue, we propose a clustering model based on the density peak clustering (DPC) method and sparrow search algorithm (SSA), named SDPC. First, the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads (CHs). More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3731-3747, 2025, DOI:10.32604/cmc.2025.063693 - 03 July 2025
Abstract The newly emerging neural radiance fields (NeRF) methods can implicitly fulfill three-dimensional (3D) reconstruction via training a neural network to render novel-view images of a given scene with given posed images. The Instant Neural Graphics Primitives (Instant-NGP) method further improves the position encoding of NeRF. It obtains state-of-the-art efficiency. However, only a local pixel-wised loss is considered when training the Instant-NGP while overlooking the nonlocal structural information between pixels. Despite a good quantitative result, it leads to a poor visual effect, especially the completeness. Inspired by the stochastic structural similarity (S3IM) method that exploits nonlocal… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3749-3763, 2025, DOI:10.32604/cmc.2025.063880 - 03 July 2025
Abstract Sonic Hedgehog Medulloblastoma (SHH-MB) is one of the four primary molecular subgroups of Medulloblastoma. It is estimated to be responsible for nearly one-third of all MB cases. Using transcriptomic and DNA methylation profiling techniques, new developments in this field determined four molecular subtypes for SHH-MB. SHH-MB subtypes show distinct DNA methylation patterns that allow their discrimination from overlapping subtypes and predict clinical outcomes. Class overlapping occurs when two or more classes share common features, making it difficult to distinguish them as separate. Using the DNA methylation dataset, a novel classification technique is presented to address… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3765-3786, 2025, DOI:10.32604/cmc.2025.064849 - 03 July 2025
Abstract Moving Target Defense (MTD) necessitates scientifically effective decision-making methodologies for defensive technology implementation. While most MTD decision studies focus on accurately identifying optimal strategies, the issue of optimal defense timing remains underexplored. Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security, performance, and cost. The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead, yet existing frameworks inadequately address this temporal dimension. To bridge this gap, this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces, thereby capturing More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3787-3810, 2025, DOI:10.32604/cmc.2025.065895 - 03 July 2025
（This article belongs to the Special Issue: Applications of Artificial Intelligence in Smart Manufacturing)
Abstract Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly (HRCA), challenges remain in the robot’s ability to understand and predict human assembly intentions. This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements. We propose a video feature extraction method based on the Temporal Shift Module Network (TSM-ResNet50) to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames. Furthermore, we construct an action recognition and segmentation model based on the Refined-Multi-Scale… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3811-3834, 2025, DOI:10.32604/cmc.2025.063789 - 03 July 2025
Abstract Spiking Neural Network (SNN) inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection, offering enhanced performance and efficiency in contrast to Artificial Neural Networks (ANN). Unlike conventional ANNs, which process static images without fully capturing the inherent temporal dynamics, our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification, integrating an encoding method to convert static RGB plant images into temporally encoded spike trains. Additionally, while Bernoulli trials and standard deep learning architectures like Convolutional Neural Networks (CNNs) and Fully Connected Neural Networks… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3835-3856, 2025, DOI:10.32604/cmc.2025.065465 - 03 July 2025
Abstract Edge computing has transformed smart grids by lowering latency, reducing network congestion, and enabling real-time decision-making. Nevertheless, devising an optimal task-offloading strategy remains challenging, as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions. We cast the offloading problem as a Markov Decision Process (MDP) and solve it with Deep Reinforcement Learning (DRL). Specifically, we present a three-tier architecture—end devices, edge nodes, and a cloud server—and enhance Proximal Policy Optimization (PPO) to learn adaptive, energy-aware policies. A Convolutional Neural Network (CNN) extracts high-level features from system states, enabling More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3857-3892, 2025, DOI:10.32604/cmc.2025.065287 - 03 July 2025
Abstract Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide, exacerbated by the COVID-19 pandemic. Age, cholesterol, and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators. These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult, and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles. Modern medical datasets’ complexity and high dimensionality challenge traditional prediction models like Support Vector Machines and Decision Trees. Quantum approaches include QSVM, QkNN, QDT, and others.… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025
（This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)
Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3911-3925, 2025, DOI:10.32604/cmc.2025.063820 - 03 July 2025
Abstract Rice spike detection and counting play a crucial role in rice yield research. Automatic detection technology based on Unmanned Aerial Vehicle (UAV) imagery has the advantages of flexibility, efficiency, low cost, safety, and reliability. However, due to the complex field environment and the small target morphology of some rice spikes, the accuracy of detection and counting is relatively low, and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results. To solve the above problems, this paper improves the You Only Look Once v8 (YOLOv8) model,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3927-3941, 2025, DOI:10.32604/cmc.2025.059643 - 03 July 2025
（This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)
Abstract As quantum computing continues to advance, traditional cryptographic methods are increasingly challenged, particularly when it comes to securing critical systems like Supervisory Control and Data Acquisition (SCADA) systems. These systems are essential for monitoring and controlling industrial operations, making their security paramount. A key threat arises from Shor’s algorithm, a powerful quantum computing tool that can compromise current hash functions, leading to significant concerns about data integrity and confidentiality. To tackle these issues, this article introduces a novel Quantum-Resistant Hash Algorithm (QRHA) known as the Modular Hash Learning Algorithm (MHLA). This algorithm is meticulously crafted… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3943-3960, 2025, DOI:10.32604/cmc.2025.066498 - 03 July 2025
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract Edge computing (EC) combined with the Internet of Things (IoT) provides a scalable and efficient solution for smart homes. The rapid proliferation of IoT devices poses real-time data processing and security challenges. EC has become a transformative paradigm for addressing these challenges, particularly in intrusion detection and anomaly mitigation. The widespread connectivity of IoT edge networks has exposed them to various security threats, necessitating robust strategies to detect malicious activities. This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory (BGT) and double deep Q-learning (DDQL). The proposed framework integrates BGT… More >
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