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Yehao Long, Jing Zhong^*, Tongdi Zhang, Li Chen, Lijun Zhang^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3435-3453, 2024, DOI:10.32604/cmc.2024.051629
（This article belongs to the Special Issue: Multiscale Computational Methods for Advanced Materials and Structures)
Abstract Physical Vapor Deposited (PVD) TiAlN coatings are extensively utilized as protective layers for cutting tools, renowned for their excellent comprehensive performance. To optimize quality control of TiAlN coatings for cutting tools, a multi-scale simulation approach is proposed that encompasses the microstructure evolution of coatings considering the entire preparation and service lifecycle of PVD TiAlN coatings. This scheme employs phase-field simulation to capture the essential microstructure of the PVD-prepared TiAlN coatings. Moreover, cutting simulation is used to determine the service temperature experienced during cutting processes at varying rates. Cahn-Hilliard modeling is finally utilized to consume the More >

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Dingyi Jin¹, Guo Wei^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3455-3468, 2024, 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 More >

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Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Reza Salehi³, Diego Martín^3,*, Zahra Halimi⁴, Sahba Baniasadi⁵
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3469-3493, 2024, 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… More >

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Jane Kim¹, Soojin Lee¹, Chan Yeob Yeun², Seung-Hyun Seo^1,3,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3495-3518, 2024, 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… More >

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78

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Chao-Lung Yang^1,*, Melkamu Mengistnew Teshome¹, Yu-Zhen Yeh¹, Tamrat Yifter Meles²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3519-3547, 2024, 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… More >

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Reem Ibrahim Alkanhel¹, Doaa Sami Khafaga², Ahmed Mohamed Zaki³, Marwa M. Eid^4,5, Abdyalaziz A. Al-Mooneam⁶, Abdelhameed Ibrahim⁷, S. K. Towfek^3,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3549-3568, 2024, 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… More >

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Karshiev Sanjar¹, Soyoun Bang¹, Sookhee Ryue², Heechul Jung^1,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3569-3583, 2024, DOI:10.32604/cmc.2024.048312
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types… More >

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Imtiaz Ahmad¹, Tariq Hussain², Babar Shah³, Altaf Hussain⁴, Iqtidar Ali¹, Farman Ali^5,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3585-3629, 2024, DOI:10.32604/cmc.2024.050596
Abstract Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these… More >

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71

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Ibraheem Abu Falahah¹, Osama Al-Baik², Saleh Alomari³, Gulnara Bektemyssova⁴, Saikat Gochhait^5,6, Irina Leonova⁷, Om Parkash Malik⁸, Frank Werner^9,*, Mohammad Dehghani¹⁰
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3631-3678, 2024, DOI:10.32604/cmc.2024.053189
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two… More >

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Song Deng^1,*, Yiming Yuan²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3679-3697, 2024, DOI:10.32604/cmc.2024.052008
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract Malicious attacks against data are unavoidable in the interconnected, open and shared Energy Internet (EI), Intrusion tolerant techniques are critical to the data security of EI. Existing intrusion tolerant techniques suffered from problems such as low adaptability, policy lag, and difficulty in determining the degree of tolerance. To address these issues, we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas: 1) it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights; and 2) it combines a tournament competition More >

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Bui Van Cong¹, Cho Do Xuan^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3699-3723, 2024, 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… More >

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Widad Elbakri¹, Maheyzah Md. Siraj^1,*, Bander Ali Saleh Al-rimy¹, Sultan Noman Qasem², Tawfik Al-Hadhrami³
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

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Nidhika Chauhan¹, Navneet Kaur², Kamaljit Singh Saini³, Sahil Verma³, Kavita³, Ruba Abu Khurma^4,5, Pedro A. Castillo^6,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3757-3782, 2024, DOI:10.32604/cmc.2024.046516
Abstract Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of… More >

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Yi-Chun Lai¹, Shu-Yin Chiang², Yao-Chiang Kan³, Hsueh-Chun Lin^4,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More >

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Siwan Noh¹, Kyung-Hyune Rhee^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

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Simona-Vasilica Oprea^*, Adela Bâra
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3827-3853, 2024, DOI:10.32604/cmc.2024.051598
Abstract The potential of text analytics is revealed by Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators (URLs). Three categories of features, both ML and Deep Learning (DL) algorithms and a ranking schema are included in the proposed framework. We apply frequency and prediction-based embeddings, such as hash vectorizer, Term Frequency-Inverse Dense Frequency (TF-IDF) and predictors, word to vector-word2vec (continuous bag of words, skip-gram) from Google, to extract features from text. Further, we apply more… More >

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Maie Aboghazalah^1,*, Passent El-kafrawy², Abdelmoty M. Ahmed³, Rasha Elnemr⁵, Belgacem Bouallegue³, Ayman El-sayed⁴
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, 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 More >

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Mingyuan Li^1,*, Yuanli Gu¹, Qingqiao Geng², Hongru Yu¹
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3877-3896, 2024, DOI:10.32604/cmc.2024.047765
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors. The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Convolutional Long Short Term Memory Neural Network (ConvLSTM) to predict short-term taxi travel demand. The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components, capturing sequence characteristics at different time scales and frequencies. Based on the sample entropy value of components, secondary processing of more… More >

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Amerah Alabrah^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3897-3912, 2024, 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,… More >

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Emran Al-Buraihy, Dan Wang^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3913-3938, 2024, 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… More >

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Zuozheng Lian^1,2, Yongchao Yin¹, Haizhen Wang^1,2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3939-3954, 2024, 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… More >

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Xinkun Tang^1,2, Feng Ouyang^1,2, Ying Xu^2,*, Ligu Zhu¹, Bo Peng¹
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3955-3975, 2024, 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,… More >

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Bo Zhu^*, Xiaona Jing, Lan Qiu, Runbo Li
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3977-3999, 2024, 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… More >

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Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Muhammad Hamza Faheem², Syed Shah Amanullah Quadri³
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4001-4020, 2024, 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 More >

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Zhenjiang Dong¹, Xin Ge¹, Yuehua Huang¹, Jiankuo Dong¹, Jiang Xu^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4021-4044, 2024, 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… More >

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Xiu Liu, Liang Gu^*, Xin Gong, Long An, Xurui Gao, Juying Wu
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4045-4061, 2024, DOI:10.32604/cmc.2024.048442
Abstract With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficient extraction of data features, which seriously affects the accuracy and performance of anomaly detection. Therefore, this paper proposes a deep learning-based anomaly detection model for power data, which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction. Aiming at the distribution variability of power data, this paper developed a sliding window-based data adjustment method for… More >

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Mahatir Ahmed Tusher¹, Saket Choudary Kongara¹, Sagar Dhanraj Pande², SeongKi Kim^3,*, Salil Bharany^4,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4063-4080, 2024, 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… More >

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Yasser Hachaichi¹, Ayman E. Khedr¹, Amira M. Idrees^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4081-4105, 2024, 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… More >

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Cheng Yang^1,2, Miaojia Lou^2,*, Xiaoyu Chen^1,2, Zixuan Ren¹
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4107-4126, 2024, 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… More >

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Abdulelah Alwabel^1,*, Chinmaya Kumar Swain²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, 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… More >

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Yan Xiang^1,2, Xuedong Zhao^1,2, Junjun Guo^1,2,*, Zhiliang Shi³, Enbang Chen³, Xiaobo Zhang³
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4149-4167, 2024, 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… More >

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Safwan Al-sayed, Xi Wang, Yijiang Peng^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4169-4195, 2024, 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… More >

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Ibraheem Al-Hejri¹, Farag Azzedin^1,*, Sultan Almuhammadi¹, Naeem Firdous Syed²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4197-4218, 2024, 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 More >

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Adel Assiri¹, Abdu Gumaei^2,*, Faisal Mehmood^3,*, Touqeer Abbas⁴, Sami Ullah⁵
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4219-4236, 2024, 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,… More >

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Weiwei Zhang¹, Jiaqiang Li¹, Chao Wang², Meng Li³, Zhi Rao^4,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4237-4257, 2024, DOI:10.32604/cmc.2024.050430
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
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… More >

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Xiuyang Meng^1,2, Chunling Wang^1,2,*, Jingran Yang^1,2, Mairui Li^1,2, Yue Zhang^1,2, Luo Wang^1,2
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4259-4281, 2024, 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 More >

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ARTICLE

Hao Chen^#, Runfeng Xie^#, Xiangyang Cui, Zhou Yan, Xin Wang, Zhanwei Xuan^*, Kai Zhang^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4283-4296, 2024, 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… More >

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Jun Wang, Changfu Si, Zhen Wang, Qiang Fu^*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, 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 cyber-attacks 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… More >

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Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, 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… More >

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Jiajie Shen¹, Yan Wang^1,*, Dongxu Zhang²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, 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.… More >

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Huiling Lu^1,*, Tao Zhou^2,3
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4371-4393, 2024, 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… More >

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Chunming Wu¹, Shupeng Zheng^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, 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.… More >

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71

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ARTICLE

Yu Zuo¹, Wenwen Li^2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4413-4431, 2024, 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… More >

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Tingting Su¹, Jia Wang^1,*, Wei Hu^2,*, Gaoqiang Dong¹, Jeon Gwanggil³
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, 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… More >

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ARTICLE

Weiming Huang^1,2, Baisong Liu^1,*, Zhaoliang Wang¹
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4449-4469, 2024, DOI:10.32604/cmc.2024.050389
Abstract In the tag recommendation task on academic platforms, existing methods disregard users’ customized preferences in favor of extracting tags based just on the content of the articles. Besides, it uses co-occurrence techniques and tries to combine nodes’ textual content for modelling. They still do not, however, directly simulate many interactions in network learning. In order to address these issues, we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations. Specifically, we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles… More >

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ARTICLE

Suyi Liu^1,*, Jianning Chi¹, Chengdong Wu¹, Fang Xu^2,3,4, Xiaosheng Yu¹
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

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ARTICLE

Chunhui Li^1,*, Hua Jiang²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
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… More >

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Lili Bai^1,*, Wenhui Li¹, He Ren^1,2, Feng Li¹, Tao Yan¹, Lirong Chen³
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4513-4531, 2024, 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,… More >

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Chen Chen^1,2, Zhongwei Xu¹, Meng Mei^1,*, Kai Huang³, Siu Ming Lo²
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4533-4549, 2024, 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 More >

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Xueping Song^1,*, Jianming Yang¹, Shuyu Zhang¹, Jicun Zhang^1,2,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4551-4572, 2024, 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… More >

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