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  • Open Access

    ARTICLE

    Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

    Safwan Al-sayed, Xi Wang, Yijiang Peng*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048916
    (This article belongs to the Special Issue: Multiscale Computational Methods for Advanced Materials and Structures)
    Abstract The mechanical properties and failure mechanism of lightweight aggregate concrete (LWAC) is a hot topic in the engineering field, and the relationship between its microstructure and macroscopic mechanical properties is also a frontier research topic in the academic field. In this study, the image processing technology is used to establish a micro-structure model of lightweight aggregate concrete. Through the information extraction and processing of the section image of actual light aggregate concrete specimens, the mesostructural model of light aggregate concrete with real aggregate characteristics is established. The numerical simulation of uniaxial tensile test, uniaxial compression test and three-point bending test… More >

  • Open Access

    ARTICLE

    SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

    Jiahui Liu1,2, Lang Li1,2,*, Di Li1,2, Yu Ou1,2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051613
    Abstract Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual. Secondly, the… More >

  • Open Access

    ARTICLE

    Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

    Lili Bai1,*, Wenhui Li1, He Ren1,2, Feng Li1, Tao Yan1, Lirong Chen3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051348
    (This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
    Abstract Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery, where weak fault characteristic signals hinder accurate fault state representation, we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform (FAWT) with Nonlinear Quantum Permutation Entropy. FAWT, leveraging fractional orders and arbitrary scaling and translation factors, exhibits superior translational invariance and adjustable fundamental oscillatory characteristics. This flexibility enables FAWT to provide well-suited wavelet shapes, effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults. In our approach, gearbox vibration signals undergo FAWT… More >

  • Open Access

    ARTICLE

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

    Djeldjli Halima1,*, Benatiallah Djelloul1, Ghasri Mehdi2, Tanougast Camel3, Benatiallah Ali4, Benabdelkrim Bouchra1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051002
    (This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
    Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of… More >
    Graphic Abstract

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

  • Open Access

    ARTICLE

    Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

    Zhenyu Qian1, Yizhang Jiang1, Zhou Hong1, Lijun Huang2, Fengda Li3, KhinWee Lai6, Kaijian Xia4,5,6,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050920
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction mechanism is designed to adapt… More >
    Graphic Abstract

    Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

  • Open Access

    ARTICLE

    DeBERTa-GRU: Sentiment Analysis for Large Language Model

    Adel Assiri1, Abdu Gumaei2,*, Faisal Mehmood3,*, Touqeer Abbas4, Sami Ullah5
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050781
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However, the calculation times for the… More >

  • Open Access

    ARTICLE

    MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

    Tao Zhou1,3, Yujie Guo1,3,*, Caiyue Peng1,3, Yuxia Niu1,3, Yunfeng Pan1,3, Huiling Lu2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050767
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the Multi-scale Directional Feature Perception Module… More >

  • Open Access

    ARTICLE

    CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

    Peng Xiao1, Qi Zhong2, Jingxue Chen1, Dongyuan Wu1, Zhen Qin1, Erqiang Zhou1,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049791
    (This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
    Abstract In the intelligent medical diagnosis area, Artificial Intelligence (AI)’s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing high-resolution digital pathological images. This limitation is particularly evident in pathological diagnosis, which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease. Therefore, the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but also a key to improving… More >

  • Open Access

    ARTICLE

    Recommendation System Based on Perceptron and Graph Convolution Network

    Zuozheng Lian1,2, Yongchao Yin1, Haizhen Wang1,2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049780
    Abstract The relationship between users and items, which cannot be recovered by traditional techniques, can be extracted by the recommendation algorithm based on the graph convolution network. The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data. This paper presents a new approach to address such issues, utilizing the graph convolution network to extract association relations. The proposed approach mainly includes three modules: Embedding layer, forward propagation layer, and score prediction layer. The embedding layer models users and items according to their interaction information and generates initial feature vectors as… More >

  • Open Access

    ARTICLE

    Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

    Reem Ibrahim Alkanhel1, Doaa Sami Khafaga2, Ahmed Mohamed Zaki3, Marwa M. Eid4,5, Abdyalaziz A. Al-Mooneam6, Abdelhameed Ibrahim7, S. K. Towfek3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049582
    Abstract The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni… More >

  • Open Access

    ARTICLE

    Research on Enhanced Contraband Dataset ACXray Based on ETL

    Xueping Song1,*, Jianming Yang1, Shuyu Zhang1, Jicun Zhang1,2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049446
    (This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
    Abstract To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method… More >

  • Open Access

    ARTICLE

    LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

    Hao Chen#, Runfeng Xie#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan*, Kai Zhang*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049129
    (This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we use LLMs’ powerful text understanding… More >

  • Open Access

    ARTICLE

    A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

    Mahatir Ahmed Tusher1, Saket Choudary Kongara1, Sagar Dhanraj Pande2, SeongKi Kim3,*, Salil Bharany4,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049007
    Abstract The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional text by extracting style vectors… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

    Chen Chen1,2, Zhongwei Xu1, Meng Mei1,*, Kai Huang3, Siu Ming Lo2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048995
    (This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
    Abstract Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by utilizing the images of the… More >

  • Open Access

    ARTICLE

    Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

    Abdulelah Alwabel1,*, Chinmaya Kumar Swain2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048833
    Abstract Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services… More >

  • Open Access

    ARTICLE

    Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

    Amerah Alabrah*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048528
    Abstract The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’ private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However, the development of NIDS still… More >

  • Open Access

    ARTICLE

    Deep Learning Based Efficient Crowd Counting System

    Waleed Khalid Al-Ghanem1, Emad Ul Haq Qazi2,*, Muhammad Hamza Faheem2, Syed Shah Amanullah Quadri3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048208
    (This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd’s density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier to evaluate density maps to… More >

  • Open Access

    ARTICLE

    An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

    Bo Zhu*, Xiaona Jing, Lan Qiu, Runbo Li
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048062
    Abstract When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid resampling approach (MSHR) and a… More >

  • Open Access

    ARTICLE

    Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

    Maie Aboghazalah1,*, Passent El-kafrawy2, Abdelmoty M. Ahmed3, Rasha Elnemr5, Belgacem Bouallegue3, Ayman El-sayed4
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039936
    Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several researchers found that Machine Learning… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM

    Chunming Wu1, Shupeng Zheng2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049665
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process. The learning capacity of LSTM… More >

  • Open Access

    ARTICLE

    EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

    Zhenjiang Dong1, Xin Ge1, Yuehua Huang1, Jiankuo Dong1, Jiang Xu2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049233
    (This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
    Abstract This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts to enhance efficiency have been… More >

  • Open Access

    ARTICLE

    An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

    Cheng Yang1,2, Miaojia Lou2,*, Xiaoyu Chen1,2, Zixuan Ren1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049183
    (This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and color adjustment, all aimed at… More >

  • Open Access

    ARTICLE

    A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

    Jiajie Shen1, Yan Wang1,*, Dongxu Zhang2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047903
    Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions. To address the existing deficiencies,… More >

  • Open Access

    ARTICLE

    Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

    Tingting Su1, Jia Wang1,*, Wei Hu2,*, Gaoqiang Dong1, Jeon Gwanggil3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051535
    (This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more difficult. To address these issues,… More >

  • Open Access

    ARTICLE

    Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

    Mohammad Aldossary1,*, Hatem A. Alharbi2, Nasir Ayub3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050862
    Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers. These sensors continuously… More >

  • Open Access

    ARTICLE

    An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

    Yu Zuo1, Wenwen Li2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049805
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the… More >

  • Open Access

    ARTICLE

    BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

    Xuejie Wang1, Jianxun Zhang1,*, Ye Tao2, Xiaoli Yuan1, Yifan Guo1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.051556
    (This article belongs to the Special Issue: Multimodal Learning in Image Processing)
    Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity and texture information for enhanced… More >

  • Open Access

    ARTICLE

    A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

    Xinkun Tang1,2, Feng Ouyang1,2, Ying Xu2,*, Ligu Zhu1, Bo Peng1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049057
    (This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract High-resolution video transmission requires a substantial amount of bandwidth. In this paper, we present a novel video processing methodology that innovatively integrates region of interest (ROI) identification and super-resolution enhancement. Our method commences with the accurate detection of ROIs within video sequences, followed by the application of advanced super-resolution techniques to these areas, thereby preserving visual quality while economizing on data transmission. To validate and benchmark our approach, we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications. The proposed model architecture leverages the transformer network framework, guided by a carefully designed… More >

  • Open Access

    ARTICLE

    Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

    Emran Al-Buraihy, Dan Wang*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048104
    Abstract Cross-lingual image description, the task of generating image captions in a target language from images and descriptions in a source language, is addressed in this study through a novel approach that combines neural network models and semantic matching techniques. Experiments conducted on the Flickr8k and AraImg2k benchmark datasets, featuring images and descriptions in English and Arabic, showcase remarkable performance improvements over state-of-the-art methods. Our model, equipped with the Image & Cross-Language Semantic Matching module and the Target Language Domain Evaluation module, significantly enhances the semantic relevance of generated image descriptions. For English-to-Arabic and Arabic-to-English cross-language image descriptions, our approach achieves… More >

  • Open Access

    ARTICLE

    MCIF-Transformer Mask RCNN: Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

    Huiling Lu1,*, Tao Zhou2,3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047827
    (This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
    Abstract The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship is established in local and… More >

  • Open Access

    ARTICLE

    AnonymousTollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

    Jane Kim1, Soojin Lee1, Chan Yeob Yeun2, Seung-Hyun Seo1,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050461
    (This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
    Abstract As big data, Artificial Intelligence, and Vehicle-to-Everything (V2X) communication have advanced, Intelligent Transportation Systems (ITS) are being developed to enable efficient and safe transportation systems. Electronic Toll Collection (ETC), which is one of the services included in ITS systems, is an automated system that allows vehicles to pass through toll plazas without stopping for manual payment. The ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system flow. To ensure secure and efficient toll payments, designing a distributed model for sharing toll payment information among untrusted toll service providers is necessary. However, the… More >

  • Open Access

    ARTICLE

    An Immune-Inspired Approach with Interval Allocation in Solving Multimodal Multi-Objective Optimization Problems with Local Pareto Sets

    Weiwei Zhang1, Jiaqiang Li1, Chao Wang2, Meng Li3, Zhi Rao4,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050430
    Abstract In practical engineering, multi-objective optimization often encounters situations where multiple Pareto sets (PS) in the decision space correspond to the same Pareto front (PF) in the objective space, known as Multi-Modal Multi-Objective Optimization Problems (MMOP). Locating multiple equivalent global PSs poses a significant challenge in real-world applications, especially considering the existence of local PSs. Effectively identifying and locating both global and local PSs is a major challenge. To tackle this issue, we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded, promising regions and regulate the number of offspring in areas that have been thoroughly explored.… More >

  • Open Access

    ARTICLE

    MD Simulation of Diffusion Behaviors in Collision Welding Processes of Al-Cu, Al-Al, Cu-Cu

    Dingyi Jin1, Guo Wei2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048644
    Abstract To investigate the effects of material combinations and velocity conditions on atomic diffusion behavior near collision interfaces, this study simulates the atomic diffusion behavior near collision interfaces in Cu-Al, Al-Al and Cu-Cu combinations fabricated through collision welding using molecular dynamic (MD) simulation. The atomic diffusion behaviors are compared between similar metal combinations (Al-Al, Cu-Cu) and dissimilar metal combinations (Al-Cu). By combining the simulation results and classical diffusion theory, the diffusion coefficients for similar and dissimilar metal material combinations under different velocity conditions are obtained. The effects of material combinations and collision velocity on diffusion behaviors are also discussed. The diffusion… More >

  • Open Access

    ARTICLE

    A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

    Chunhui Li1,*, Hua Jiang2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048431
    Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in training local data and subsequently… More >

  • Open Access

    ARTICLE

    Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

    Mehrdad Shoeibi1, Mohammad Mehdi Sharifi Nevisi2, Reza Salehi3, Diego Martín3,*, Zahra Halimi4, Sahba Baniasadi5
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049847
    (This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
    Abstract Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using deep Q learning (DQL) and… More >

  • Open Access

    ARTICLE

    A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics—A Supply Chain Backlog Elimination Framework

    Yasser Hachaichi1, Ayman E. Khedr1, Amira M. Idrees2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048929
    Abstract The diversity of data sources resulted in seeking effective manipulation and dissemination. The challenge that arises from the increasing dimensionality has a negative effect on the computation performance, efficiency, and stability of computing. One of the most successful optimization algorithms is Particle Swarm Optimization (PSO) which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task. This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance. On… More >

  • Open Access

    ARTICLE

    A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

    Chao-Lung Yang1,*, Melkamu Mengistnew Teshome1, Yu-Zhen Yeh1, Tamrat Yifter Meles2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048462
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract In this study, we introduce a novel multi-objective optimization model tailored for modern manufacturing, aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance. Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel, addressing a crucial gap in the integration of maintenance personnel dispatching and station selection. Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness. The core of our methodology is the NSGA III+ Dispatch, an advanced adaptation of the Non-Dominated Sorting Genetic… More >

  • Open Access

    ARTICLE

    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050586
    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct a thorough assessment of the… More >

  • Open Access

    ARTICLE

    Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

    Xiuyang Meng1,2, Chunling Wang1,2,*, Jingran Yang1,2, Mairui Li1,2, Yue Zhang1,2, Luo Wang1,2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050325
    Abstract Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features and applying correction factors within… More >

  • Open Access

    ARTICLE

    A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

    Yan Xiang1,2, Xuedong Zhao1,2, Junjun Guo1,2,*, Zhiliang Shi3, Enbang Chen3, Xiaobo Zhang3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050229
    (This article belongs to the Special Issue: Recognition Tasks with Transformers)
    Abstract Chinese named entity recognition (CNER) has received widespread attention as an important task of Chinese information extraction. Most previous research has focused on individually studying flat CNER, overlapped CNER, or discontinuous CNER. However, a unified CNER is often needed in real-world scenarios. Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER. Nevertheless, how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge. In this study, we enhance the character-pair grid representation by incorporating both local and… More >

  • Open Access

    ARTICLE

    Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Applications

    Ibraheem Al-Hejri1, Farag Azzedin1,*, Sultan Almuhammadi1, Naeem Firdous Syed2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047117
    (This article belongs to the Special Issue: Artificial Intelligence for Addressing Security and Communications Challenges of Internet-connected Critical Infrastructures)
    Abstract The use of the Internet of Things (IoT) is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices. In critical infrastructure domains like oil and gas supply, intelligent transportation, power grids, and autonomous agriculture, it is essential to guarantee the confidentiality, integrity, and authenticity of data collected and exchanged. However, the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques. Consequently, designing a lightweight secure data transmission scheme is becoming… More >

  • Open Access

    ARTICLE

    A New Framework for Software Vulnerability Detection Based on an Advanced Computing

    Bui Van Cong1, Cho Do Xuan2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050019
    (This article belongs to the Special Issue: Securing Machine Learning Algorithms)
    Abstract The detection of software vulnerabilities written in C and C++ languages takes a lot of attention and interest today. This paper proposes a new framework called DrCSE to improve software vulnerability detection. It uses an intelligent computation technique based on the combination of two methods: Rebalancing data and representation learning to analyze and evaluate the code property graph (CPG) of the source code for detecting abnormal behavior of software vulnerabilities. To do that, DrCSE performs a combination of 3 main processing techniques: (i) building the source code feature profiles, (ii) rebalancing data, and (iii) contrastive learning. In which, the method… More >

  • Open Access

    ARTICLE

    A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

    Jun Wang, Changfu Si, Zhen Wang, Qiang Fu*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050223
    Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyberattacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper proposes a hybrid deep learning… More >