Open Access

TECHNICAL REPORT

Lei Zhang^1,2,*, Hangyuan Deng^1,2
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1-11, 2025, DOI:10.32604/cmc.2025.062666 - 26 March 2025
Abstract NJmat is a user-friendly, data-driven machine learning interface designed for materials design and analysis. The platform integrates advanced computational techniques, including natural language processing (NLP), large language models (LLM), machine learning potentials (MLP), and graph neural networks (GNN), to facilitate materials discovery. The platform has been applied in diverse materials research areas, including perovskite surface design, catalyst discovery, battery materials screening, structural alloy design, and molecular informatics. By automating feature selection, predictive modeling, and result interpretation, NJmat accelerates the development of high-performance materials across energy storage, conversion, and structural applications. Additionally, NJmat serves as an… More >

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REVIEW

Muhammad Arif^1,*, Muhammad Rashid²
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 13-64, 2025, DOI:10.32604/cmc.2025.062819 - 26 March 2025
Abstract Edge Machine Learning (EdgeML) and Tiny Machine Learning (TinyML) are fast-growing fields that bring machine learning to resource-constrained devices, allowing real-time data processing and decision-making at the network’s edge. However, the complexity of model conversion techniques, diverse inference mechanisms, and varied learning strategies make designing and deploying these models challenging. Additionally, deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors. These factors underscore the necessity for a comprehensive literature review, as current reviews do not systematically encompass the most recent findings on these topics. Consequently, it provides… More >

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211

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ARTICLE

Jialiang Dong^1,2, Xuemao Dong^3,4, Zhongxue Feng^3,4,*, Caiju Li^3,4, Jianhong Yi^3,4, Jun Tan^5,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 65-77, 2025, DOI:10.32604/cmc.2025.059727 - 26 March 2025
（This article belongs to the Special Issue: Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations)
Abstract To explore atomic-level phenomena in the Cu-Ni-Sn alloy, a second nearest-neighbor modified embedded-atom method (2NN MEAM) potential has been developed for the Cu-Ni-Sn system, building upon the work of other researchers. This potential demonstrates remarkable accuracy in predicting the lattice constant, with a relative error of less than 0.5% when compared to density functional theory (DFT) results, and it achieves a 10% relative error in the enthalpy of formation compared to experimental data, marking substantial advancements over prior models. The bulk modulus is predicted with a relative error of 8% compared to DFT. Notably, the More >

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123

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ARTICLE

Qingxiang Pei^1,2, Fan Li^2,3, Ziheng Fei⁴, Haojie Lian^2,3, Xiaohui Yuan^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 79-96, 2025, DOI:10.32604/cmc.2025.061741 - 26 March 2025
Abstract This paper employs the Direct Finite Element Squared (DFE²) method to develop Sparse Polynomial Chaos Expansions (SPCE) models for analyzing the electromechanical properties of multiscale piezoelectric structures. By incorporating variations in piezoelectric and elastic constants, the DFE² method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties, including Mises stress, maximum in-plane principal strain, electric potential gradient, and electric potential, under varying parameters. This approach achieves a balance between computational efficiency and accuracy. Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric More >

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97

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ARTICLE

Yi-Ze Wu, Yi-Cheng Huang^*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 97-114, 2025, DOI:10.32604/cmc.2025.063441 - 26 March 2025
（This article belongs to the Special Issue: Selected Papers from the International Multi-Conference on Engineering and Technology Innovation 2024 (IMETI2024))
Abstract Precision steel balls are critical components in precision bearings. Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors. Human visual inspection of precision steel balls demands significant labor work. Besides, human inspection cannot maintain consistent quality assurance. To address these limitations and reduce inspection time, a convolutional neural network (CNN) based optical inspection system has been developed that automatically detects steel ball defects using a novel designated vertical mechanism. During image detection processing, two key challenges were addressed and resolved. They are the reflection caused… More >

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55

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ARTICLE

Zaid Derea^1,2,*, Beiji Zou¹, Xiaoyan Kui^1,*, Monir Abdullah³, Alaa Thobhani¹, Amr Abdussalam⁴
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 115-136, 2025, DOI:10.32604/cmc.2025.062729 - 26 March 2025
Abstract Improving website security to prevent malicious online activities is crucial, and CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has emerged as a key strategy for distinguishing human users from automated bots. Text-based CAPTCHAs, designed to be easily decipherable by humans yet challenging for machines, are a common form of this verification. However, advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency. In our comprehensive investigation into CAPTCHA recognition, we have tailored the renowned UpDown image captioning model specifically for this… More >

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159

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ARTICLE

Haitao Wang^1,2, Yuanzhao Guo^1,2, Xiaotong Han^1,2, Yuan Tian^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 137-155, 2025, DOI:10.32604/cmc.2025.060534 - 26 March 2025
Abstract Relation extraction plays a crucial role in numerous downstream tasks. Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue. To tackle the problem of low information density in dialogues, methods based on trigger enhancement have been proposed, yielding positive results. However, trigger enhancement faces challenges, which cause suboptimal model performance. First, the proportion of annotated triggers is low in DialogRE. Second, feature representations of triggers and arguments often contain conflicting information. In this paper, we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations. We first… More >

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80

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ARTICLE

Xiaoyin Yi^1,2, Long Chen^1,3,4,*, Jiacheng Huang¹, Ning Yu¹, Qian Huang⁵
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 157-175, 2025, DOI:10.32604/cmc.2025.059863 - 26 March 2025
Abstract Transfer-based Adversarial Attacks (TAAs) can deceive a victim model even without prior knowledge. This is achieved by leveraging the property of adversarial examples. That is, when generated from a surrogate model, they retain their features if applied to other models due to their good transferability. However, adversarial examples often exhibit overfitting, as they are tailored to exploit the particular architecture and feature representation of source models. Consequently, when attempting black-box transfer attacks on different target models, their effectiveness is decreased. To solve this problem, this study proposes an approach based on a Regularized Constrained Feature More >

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85

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ARTICLE

Vitor F. C. Sousa¹, Jorge Gil¹, Tiago E. F. Silva¹, Abílio M. P. de Jesus^1,2, Francisco J. G. Silva^1,3, João Manuel R. S. Tavares^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 177-199, 2025, DOI:10.32604/cmc.2025.062133 - 26 March 2025
Abstract The machining process remains relevant for manufacturing high-quality and high-precision parts, which can be found in industries such as aerospace and aeronautical, with many produced by turning, drilling, and milling processes. Monitoring and analyzing tool wear during these processes is crucial to assess the tool’s life and optimize the tool’s performance under study; as such, standards detail procedures to measure and assess tool wear for various tools. Measuring wear in machining tools can be time-consuming, as the process is usually manual, requiring human interaction and judgment. In the present work, an automated offline flank wear… More >

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77

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ARTICLE

Josua Käser¹, Thomas Nagy¹, Patrick Stirnemann¹, Thomas Hanne^2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 201-217, 2025, DOI:10.32604/cmc.2025.061527 - 26 March 2025
Abstract We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference… More >

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81

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ARTICLE

Jiakang Sun^1,2, Ke Chen^1,2, Xinyang He^1,2, Xu Liu^1,2, Ke Li^1,2, Cheng Peng^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 219-238, 2025, DOI:10.32604/cmc.2025.059745 - 26 March 2025
Abstract With the advancements in parameter-efficient transfer learning techniques, it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions. However, applying this technique to multimodal knowledge transfer introduces a significant challenge: ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation. This paper introduces UniTrans, a framework aimed at facilitating efficient knowledge transfer across multiple modalities. UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead. To further enhance modality alignment, we introduce two key components: the Multimodal More >

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108

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ARTICLE

Long Cai, Ke Gu^*, Jiaqi Lei
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 239-258, 2025, DOI:10.32604/cmc.2025.061377 - 26 March 2025
Abstract Large-scale neural networks-based federated learning (FL) has gained public recognition for its effective capabilities in distributed training. Nonetheless, the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential attacks. Poisoning attacks turn into a major menace to federated learning on account of their concealed property and potent destructive force. By altering the local model during routine machine learning training, attackers can easily contaminate the global model. Traditional detection and aggregation solutions mitigate certain threats, but they are still insufficient to completely eliminate the influence generated by attackers. Therefore, federated… More >

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104

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ARTICLE

Aadil Mushtaq Pandith¹, Manni Kumar², Naveen Kumar³, Nitin Goyal^4,*, Sachin Ahuja², Yonis Gulzar⁵, Rashi Rastogi⁶, Rupesh Gupta⁷
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025
Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >

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72

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ARTICLE

Mathew Nicho^1,2,*, Oluwasegun Adelaiye³, Christopher D. McDermott⁴, Shini Girija⁵
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 281-308, 2025, DOI:10.32604/cmc.2025.059597 - 26 March 2025
Abstract The successful penetration of government, corporate, and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace. Advanced Persistent Threat (APT) attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence (AI)-based defense mechanisms. While AI has enhanced organizational capabilities for deterrence, detection, and mitigation of APTs, the global escalation in reported incidents, particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology (IT) security administrators and decision-makers. Literature review has identified the stealthy lateral movement (LM) of malware within the initially… More >

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107

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ARTICLE

Yuanqing Li¹, Mengyao Dai¹, Sanfeng Zhang^1,2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 309-333, 2025, DOI:10.32604/cmc.2025.060455 - 26 March 2025
Abstract Social media has significantly accelerated the rapid dissemination of information, but it also boosts propagation of fake news, posing serious challenges to public awareness and social stability. In real-world contexts, the volume of trustable information far exceeds that of rumors, resulting in a class imbalance that leads models to prioritize the majority class during training. This focus diminishes the model’s ability to recognize minority class samples. Furthermore, models may experience overfitting when encountering these minority samples, further compromising their generalization capabilities. Unlike node-level classification tasks, fake news detection in social networks operates on graph-level samples,… More >

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109

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ARTICLE

Stellar Choi¹, HeeAe Ko², KyungRok Bae³, HyunSook Lee², HaeJong Joo⁴, Woong Choi^5,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 335-355, 2025, DOI:10.32604/cmc.2025.061243 - 26 March 2025
（This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)
Abstract Previous research utilizing Cartoon Generative Adversarial Network (CartoonGAN) has encountered limitations in managing intricate outlines and accurately representing lighting effects, particularly in complex scenes requiring detailed shading and contrast. This paper presents a novel Enhanced Pixel Integration (EPI) technique designed to improve the visual quality of images generated by CartoonGAN. Rather than modifying the core model, the EPI approach employs post-processing adjustments that enhance images without significant computational overhead. In this method, images produced by CartoonGAN are converted from Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) format, allowing for precise adjustments in hue, saturation, and brightness, thereby… More >

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227

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ARTICLE

Zhen Yang¹, Yifan Liu^1,2,*, Fan Feng³, Yi Liu³, Zhenpeng Liu^1,3
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 357-380, 2025, DOI:10.32604/cmc.2025.060709 - 26 March 2025
Abstract Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’ devices without sharing private data. It trains a global model through collaboration between clients and the server. However, the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability. Meanwhile, data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks, and standalone personalization tasks may cause severe overfitting issues. To address these limitations, we introduce a federated learning dual optimization model based on… More >

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ARTICLE

Fahad Ahmad^1,2,*, Saad Awadh Alanazi³, Kashaf Junaid⁴, Maryam Shabbir⁵, Asim Ali¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 381-405, 2025, DOI:10.32604/cmc.2025.062452 - 26 March 2025
Abstract Image processing plays a vital role in various fields such as autonomous systems, healthcare, and cataloging, especially when integrated with deep learning (DL). It is crucial in medical diagnostics, including the early detection of diseases like chronic obstructive pulmonary disease (COPD), which claimed 3.2 million lives in 2015. COPD, a life-threatening condition often caused by prolonged exposure to lung irritants and smoking, progresses through stages. Early diagnosis through image processing can significantly improve survival rates. COPD encompasses chronic bronchitis (CB) and emphysema; CB particularly increases in smokers and generally affects individuals between 50 and 70… More >

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ARTICLE

Tien-Wen Sung¹, Wei Li¹, Chao-Yang Lee^2,*, Yuzhen Chen¹, Qingjun Fang¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 407-434, 2025, DOI:10.32604/cmc.2025.061694 - 26 March 2025
（This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)
Abstract To transmit customer power data collected by smart meters (SMs) to utility companies, data must first be transmitted to the corresponding data aggregation point (DAP) of the SM. The number of DAPs installed and the installation location greatly impact the whole network. For the traditional DAP placement algorithm, the number of DAPs must be set in advance, but determining the best number of DAPs is difficult, which undoubtedly reduces the overall performance of the network. Moreover, the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the… More >

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ARTICLE

Sai Venkata Akhil Ammu¹, Sukhjit Singh Sehra^1,*, Sumeet Kaur Sehra², Jaiteg Singh³
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 435-453, 2025, DOI:10.32604/cmc.2025.057792 - 26 March 2025
Abstract Duplicate bug reporting is a critical problem in the software repositories’ mining area. Duplicate bug reports can lead to redundant efforts, wasted resources, and delayed software releases. Thus, their accurate identification is essential for streamlining the bug triage process mining area. Several researchers have explored classical information retrieval, natural language processing, text and data mining, and machine learning approaches. The emergence of large language models (LLMs) (ChatGPT and Huggingface) has presented a new line of models for semantic textual similarity (STS). Although LLMs have shown remarkable advancements, there remains a need for longitudinal studies to… More >

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ARTICLE

Borui Wang, Dongyue Gao^*, Haiyang Gu, Mengke Ding, Zhanjun Wu
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 455-473, 2025, DOI:10.32604/cmc.2025.060617 - 26 March 2025
（This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)
Abstract Fatigue damage is a primary contributor to the failure of composite structures, underscoring the critical importance of monitoring its progression to ensure structural safety. This paper introduces an innovative approach to fatigue damage monitoring in composite structures, leveraging a hybrid methodology that integrates the Whale Optimization Algorithm (WOA)-Backpropagation (BP) neural network with an ultrasonic guided wave feature selection algorithm. Initially, a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves, thereby establishing a signal space that correlates with the structural condition. Subsequently, the Relief-F algorithm is applied for signal feature extraction,… More >

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107

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ARTICLE

Jeng-Shyang Pan^1,2,3, Mengfei Zhang¹, Shu-Chuan Chu^2,*, Xingsi Xue⁴, Václav Snášel⁵
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 475-496, 2025, DOI:10.32604/cmc.2025.060170 - 26 March 2025
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis. Traditional clustering algorithms, such as K-means, are widely used due to their simplicity and efficiency. This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm (SPPE) to improve clustering performance. The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution (PPE) algorithm. Firstly, a Variable Neighborhood Search (VNS) factor is incorporated to strengthen the local search capability and foster population diversity. Secondly, a position update model, incorporating a spiral mechanism, is… More >

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ARTICLE

Shijie Xiang, Dong Zhou, Dan Tian^*, Zihao Wang
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244 - 26 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… More >

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ARTICLE

Chayut Bunterngchit¹, Laith H. Baniata², Mohammad H. Baniata³, Ashraf ALDabbas⁴, Mohannad A. Khair⁵, Thanaphon Chearanai⁶, Sangwoo Kang^2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 517-536, 2025, DOI:10.32604/cmc.2025.060368 - 26 March 2025
Abstract Stroke is a leading cause of death and disability worldwide, significantly impairing motor and cognitive functions. Effective rehabilitation is often hindered by the heterogeneity of stroke lesions, variability in recovery patterns, and the complexity of electroencephalography (EEG) signals, which are often contaminated by artifacts. Accurate classification of motor imagery (MI) tasks, involving the mental simulation of movements, is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability. To address these challenges, this study introduces a graph-attentive convolutional long short-term memory (LSTM) network (GACL-Net), a novel hybrid deep learning model… More >

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ARTICLE

Shih-Lin Lin^*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 537-557, 2025, DOI:10.32604/cmc.2025.061379 - 26 March 2025
Abstract Tires are integral to vehicular systems, directly influencing both safety and overall performance. Traditional tire pressure inspection methods—such as manual or gauge-based approaches—are often time-consuming, prone to inconsistency, and lack the flexibility needed to meet diverse operational demands. In this research, we introduce an AI-driven tire pressure detection system that leverages an enhanced GoogLeNet architecture incorporating a novel Softplus-LReLU activation function. By combining the smooth, non-saturating characteristics of Softplus with a linear adjustment term, this activation function improves computational efficiency and helps stabilize network gradients, thereby mitigating issues such as gradient vanishing and neuron death.… More >

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ARTICLE

Chuanyun Xu^1,#, Die Hu^1,#, Yang Zhang^1,*, Shuaiye Huang¹, Yisha Sun¹, Gang Li²
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 559-574, 2025, DOI:10.32604/cmc.2025.061579 - 26 March 2025
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer. However, this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size. Pathologists often refer to surrounding cells to identify abnormalities. To emulate this slide examination behavior, this study proposes a Multi-Scale Feature Fusion Network (MSFF-Net) for detecting cervical abnormal cells. MSFF-Net employs a Cross-Scale Pooling Model (CSPM) to effectively capture diverse features and contextual information, ranging from local details to the overall structure. Additionally, a Multi-Scale Fusion Attention (MSFA)… More >

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ARTICLE

Xu Liu¹, Qiujie Li^1,*, Youlin Xu¹, Musaed Alhussein², Khursheed Aurangzeb^2,*, Fa Zhu¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 575-593, 2025, DOI:10.32604/cmc.2025.062445 - 26 March 2025
Abstract The correction of Light Detection and Ranging (LiDAR) intensity data is of great significance for enhancing its application value. However, traditional intensity correction methods based on Terrestrial Laser Scanning (TLS) technology rely on manual site setup to collect intensity training data at different distances and incidence angles, which is noisy and limited in sample quantity, restricting the improvement of model accuracy. To overcome this limitation, this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning (MLS) technology. The method utilizes the continuous scanning characteristics of MLS technology to obtain dense point… More >

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ARTICLE

Xiaoyan Shao¹, Jiaqi Han^1,*, Lingling Li^1,*, Xuezhuan Zhao^1,2,3,4, Jingjing Yan¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 595-617, 2025, DOI:10.32604/cmc.2025.060295 - 26 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods. End-to-end model designs have gained significant attention for improving training efficiency. Most current algorithms rely on Convolutional Neural Networks (CNNs) for feature extraction. Although CNNs are proficient at capturing local features, they often struggle with global context, leading to incomplete and false Class Activation Mapping (CAM). To address these limitations, this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation (CPEWS) model, which improves feature extraction by utilizing the Vision Transformer… More >

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ARTICLE

Jianfeng Lu¹, Tao Huang¹, Yuanai Xie^2,*, Shuqin Cao¹, Bing Li³
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 619-634, 2025, DOI:10.32604/cmc.2025.060094 - 26 March 2025
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract The proliferation of deep learning (DL) has amplified the demand for processing large and complex datasets for tasks such as modeling, classification, and identification. However, traditional DL methods compromise client privacy by collecting sensitive data, underscoring the necessity for privacy-preserving solutions like Federated Learning (FL). FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data. Given that FL clients autonomously manage training data, encouraging client engagement is pivotal for successful model training. To overcome challenges like unreliable communication and budget constraints, we present ENTIRE, a contract-based dynamic… More >

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ARTICLE

Ishaani Priyadarshini^*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025
（This article belongs to the Special Issue: Next-Generation AI for Ethical and Explainable Decision-Making in Critical Systems)
Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >

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ARTICLE

Sungbum Kim¹, Gwangsik Lee², Jian Song², Insoo Lee², Taeshik Shon^3,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 661-683, 2025, DOI:10.32604/cmc.2025.063687 - 26 March 2025
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract The smart home platform integrates with Internet of Things (IoT) devices, smartphones, and cloud servers, enabling seamless and convenient services. It gathers and manages extensive user data, including personal information, device operations, and patterns of user behavior. Such data plays an essential role in criminal investigations, highlighting the growing importance of specialized smart home forensics. Given the rapid advancement in smart home software and hardware technologies, many companies are introducing new devices and services that expand the market. Consequently, scalable and platform-specific forensic research is necessary to support efficient digital investigations across diverse smart home… More >

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ARTICLE

Keyuan Chen¹, Xingkao Zhang¹, Li Ma¹, Jueyi Ye¹, Qi Qiu¹, Haoxiang Zhang¹, Ju Rong^1,*, Yudong Sui^1,*, Xiaohua Yu^1,2, Jing Feng¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 685-700, 2025, DOI:10.32604/cmc.2025.060713 - 26 March 2025
（This article belongs to the Special Issue: Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations)
Abstract The deep potential (DP) is an innovative approach based on deep learning that uses ab initio calculation data derived from density functional theory (DFT), to create high-accuracy potential functions for various materials. Platinum (Pt) is a rare metal with significant potential in energy and catalytic applications, However, there are challenges in accurately capturing its physical properties due to high experimental costs and the limitations of traditional empirical methods. This study employs deep learning methods to construct high-precision potential models for single-element systems of Pt and validates their predictive performance in complex environments. The newly developed DP… More >

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Yuxin Gao¹, Xiaodong Duan^2,3, Qiguo Dai^2,3,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 701-718, 2025, DOI:10.32604/cmc.2025.060137 - 26 March 2025
（This article belongs to the Special Issue: Advances in Action Recognition: Algorithms, Applications, and Emerging Trends)
Abstract Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action… More >

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Tajinder Kumar¹, Purushottam Sharma^2,*, Xiaochun Cheng^3,*, Sachin Lalar⁴, Shubham Kumar⁵, Sandhya Bansal⁶
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 719-738, 2025, DOI:10.32604/cmc.2025.060836 - 26 March 2025
Abstract With cloud computing, large chunks of data can be handled at a small cost. However, there are some reservations regarding the security and privacy of cloud data stored. For solving these issues and enhancing cloud computing security, this research provides a Three-Layered Security Access model (TLSA) aligned to an intrusion detection mechanism, access control mechanism, and data encryption system. The TLSA underlines the need for the protection of sensitive data. This proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard (AES). For data transfer and storage, this encryption guarantees the data’s… More >

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Zhiyong Li, Xinlian Zhou^*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 739-760, 2025, DOI:10.32604/cmc.2025.059807 - 26 March 2025
（This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
Abstract Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through More >

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Huansha Wang^*, Ruiyang Huang^*, Qinrang Liu, Shaomei Li, Jianpeng Zhang
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 761-783, 2025, DOI:10.32604/cmc.2025.060395 - 26 March 2025
Abstract Multi-modal knowledge graph completion (MMKGC) aims to complete missing entities or relations in multi-modal knowledge graphs, thereby discovering more previously unknown triples. Due to the continuous growth of data and knowledge and the limitations of data sources, the visual knowledge within the knowledge graphs is generally of low quality, and some entities suffer from the issue of missing visual modality. Nevertheless, previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing. In this case, mainstream MMKGC models only use… More >

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Ra-Yeon Choi¹, Yeji Song², Minsoo Jang¹, Taekyung Kim³, Jinhyun Ahn^4,*, Dong-Hyuk Im^5,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 785-801, 2025, DOI:10.32604/cmc.2025.061185 - 26 March 2025
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity. However, their immutability after deployment makes programming errors particularly critical, as such errors can be exploited to compromise blockchain security. Existing vulnerability detection methods often rely on fixed rules or target specific vulnerabilities, limiting their scalability and adaptability to diverse smart contract scenarios. Furthermore, natural language processing approaches for source code analysis frequently fail to capture program flow, which is essential for identifying structural vulnerabilities. To address these limitations, we propose a novel model that integrates textual and structural… More >

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Jia Liu¹, Hang Gu¹, Fangmei Liu¹, Hao Chen¹, Zuhe Li¹, Gang Xu², Qidong Liu², Wei Wang^2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025
Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection More >

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Yi-Feng Luo¹, Guan-Jhu Chen^2,*, Chun-Liang Liu³, Yen-Tse Chung⁴
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 823-844, 2025, DOI:10.32604/cmc.2025.061498 - 26 March 2025
Abstract As lithium-ion batteries become increasingly prevalent in electric scooters, vehicles, mobile devices, and energy storage systems, accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability. Unlike traditional methods that rely on static alternating internal resistance (SAIR) measurements in an open-circuit state, this study presents a real-time state of charge (SOC) estimation method combining dynamic alternating internal resistance (DAIR) with artificial neural networks (ANN). The system simultaneously measures electrochemical impedance |Z| at various frequencies, discharge C-rate, and battery surface temperature during the discharge process, using these parameters for ANN training. The… More >

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Ravendra Kumar Ray^*, A.C. Tiwari
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 845-860, 2025, DOI:10.32604/cmc.2025.059435 - 26 March 2025
（This article belongs to the Special Issue: Innovative Approaches to the Materials Genome: Machine Learning, Big Data, and Computational Methods for Modern Material Design and Manufacturing)
Abstract The design and development of solar dryers are crucial in regions with abundant solar energy, such as Bhopal, India, where seasonal variations significantly impact the efficiency of drying processes. The paper is focused on employing a comprehensive mathematical model to predict the dryer’s performance in drying the materials such as banana slices. To enhance this model, Hyper Tuned Swarm Optimization with Gradient Tree (HT_SOGT) was utilized to accurately predict and determine the optimal size of the dryer dimensions considering various mathematical calculations for material drying. The predictive model considered the influence of seasonal fluctuations, ensuring More >

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Mingwen Bi¹, Hefei Chen^2,*, Zhenghong Yang^3,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 861-876, 2025, DOI:10.32604/cmc.2025.060517 - 26 March 2025
Abstract Entity relation extraction, a fundamental and essential task in natural language processing (NLP), has garnered significant attention over an extended period., aiming to extract the core of semantic knowledge from unstructured text, i.e., entities and the relations between them. At present, the main dilemma of Chinese entity relation extraction research lies in nested entities, relation overlap, and lack of entity relation interaction. This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge, imprecise syntactic structure, and lack of semantic roles. To address these challenges, this paper presents an innovative “character-level” Chinese part-of-speech… More >

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Jian Luo¹, Yaling Li¹, Xueyu Li¹, Xuliang Hu^2,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 877-896, 2025, DOI:10.32604/cmc.2025.061401 - 26 March 2025
Abstract Sarcasm detection in Natural Language Processing (NLP) has become increasingly important, particularly with the rise of social media and non-textual emotional expressions, such as images. Existing methods often rely on separate image and text modalities, which may not fully utilize the information available from both sources. To address this limitation, we propose a novel multimodal large model, i.e., the PKME-MLM (Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection). The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images, which is then combined… More >

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Junpeng Wu^1,2,*, Xingfan Jiang²
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 897-915, 2025, DOI:10.32604/cmc.2025.058714 - 26 March 2025
Abstract As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance, the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment. In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis, we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once (GD-YOLO). Firstly, a partial convolution group is designed based on different convolution kernels. We combine the partial convolution group with… More >

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Zeric Tabekoueng Njitacke¹, Louai A. Maghrabi², Musheer Ahmad^3,*, Turki Althaqafi⁴
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 917-933, 2025, DOI:10.32604/cmc.2025.059640 - 26 March 2025
Abstract This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map. The map demonstrates remarkable chaotic dynamics over a wide range of parameters. We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map, which allows us to select optimal parameter configurations for the encryption process. Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors, an essential characteristic for effective encryption. The encryption technique is based on bit-plane decomposition, wherein a plain image is divided into distinct… More >

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Smita Kapse¹, Ganesh Yenurkar^1,*, Vincent Omollo Nyangaresi^2,3,*, Gunjan Balpande¹, Shravani Kale¹, Manthan Jadhav¹, Sahil Lawankar¹, Vikrant Jaunjale¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 935-976, 2025, DOI:10.32604/cmc.2025.061981 - 26 March 2025
Abstract In today’s fast-paced world, many elderly individuals struggle to adhere to their medication schedules, especially those with memory-related conditions like Alzheimer’s disease, leading to serious health risks, hospitalizations, and increased healthcare costs. Traditional reminder systems often fail due to a lack of personalization and real-time intervention. To address this critical challenge, we introduce MediServe, an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized, secure, and adaptive solution. MediServe features a smart medication box equipped with biometric authentication, such as fingerprint recognition, ensuring authorized access to prescribed medication while… More >

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Di Wu^*, Liming Feng, Xiaoyu Wang
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 977-999, 2025, DOI:10.32604/cmc.2025.060319 - 26 March 2025
Abstract Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model (SNID-ENSEF) is proposed. Syntactic elimination contrast learning leverages verb-dominant syntactic features, systematically replacing specific words to enhance data diversity. The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency. A neighborhood sample fusion strategy, based on sample distribution patterns, dynamically adjusts neighborhood size and fuses sample More >

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Zhicheng Yin^1,2, Lang Li^1,2,*, Yu Ou^1,2
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1001-1022, 2025, DOI:10.32604/cmc.2025.060925 - 26 March 2025
Abstract The adoption of deep learning-based side-channel analysis (DL-SCA) is crucial for leak detection in secure products. Many previous studies have applied this method to break targets protected with countermeasures. Despite the increasing number of studies, the problem of model overfitting. Recent research mainly focuses on exploring hyperparameters and network architectures, while offering limited insights into the effects of external factors on side-channel attacks, such as the number and type of models. This paper proposes a Side-channel Analysis method based on a Stacking ensemble, called Stacking-SCA. In our method, multiple models are deeply integrated. Through the… More >

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Faten S. Alamri¹, Tariq Sadad^2,*, Ahmed S. Almasoud³, Raja Atif Aurangzeb⁴, Amjad Khan³
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1023-1039, 2025, DOI:10.32604/cmc.2025.061890 - 26 March 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Mango farming significantly contributes to the economy, particularly in developing countries. However, mango trees are susceptible to various diseases caused by fungi, viruses, and bacteria, and diagnosing these diseases at an early stage is crucial to prevent their spread, which can lead to substantial losses. The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture. This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer (ViT) architectures. Two datasets were used. The first, MangoLeafBD, contains data for mango leaf diseases such as… More >

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Hui Jin^1,2,*, Shuaiqi Xu¹, Chengyi Duan¹, Ruixue He¹, Ji Zhang¹
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1041-1055, 2025, DOI:10.32604/cmc.2025.060304 - 26 March 2025
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Instance segmentation is crucial in various domains, such as autonomous driving and robotics. However, there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices. Therefore, it is essential to enhance detection speed while maintaining high accuracy. In this study, we propose you only look once-layer fusion (YOLO-LF), a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications. Based on the You Only Look Once version 8 nano (YOLOv8n) framework, we introduce a lightweight convolutional module and design a lightweight layer aggregation module… More >

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Bonan Yu^1,2, Taiping Mo^1,3, Qi Ma¹, Qiumei Li¹, Peng Sun^1,3,*
CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1057-1078, 2025, DOI:10.32604/cmc.2025.059946 - 26 March 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Super-resolution (SR) reconstruction addresses the challenge of enhancing image resolution, which is critical in domains such as medical imaging, remote sensing, and computational photography. High-quality image reconstruction is essential for enhancing visual details and improving the accuracy of subsequent tasks. Traditional methods, including interpolation techniques and basic CNNs, often fail to recover fine textures and detailed structures, particularly in complex or high-frequency regions. In this paper, we present Deep Supervised Swin Transformer U-Net (DSSTU-Net), a novel architecture designed to improve image SR by integrating Residual Swin Transformer Blocks (RSTB) and Deep Supervision (DS) mechanisms into… More >

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