Open Access
ISSN:1546-2218(print)
ISSN:1546-2226(online)
Publication Frequency:Monthly
Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials. This journal is published monthly by Tech Science Press.
SCI: 2023 Impact Factor 2.0; Scopus CiteScore (Impact per Publication 2023): 5.3; SNIP (Source Normalized Impact per Paper 2023): 0.73; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.
Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
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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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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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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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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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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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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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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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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 >
Open Access
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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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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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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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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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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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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 >
Graphic Abstract
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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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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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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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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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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 >
Open Access
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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 >
Open Access
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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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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 >
Open Access
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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 >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4573-4601, 2024, DOI:10.32604/cmc.2024.049310
Abstract Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST)… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, 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… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, 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… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4641-4657, 2024, 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… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844
Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4703-4724, 2024, 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… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, 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… More >
Graphic Abstract
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, 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… More >
Graphic Abstract
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4763-4779, 2024, DOI:10.32604/cmc.2024.051181
Abstract The Internet of Medical Things (IoMT) is an application of the Internet of Things (IoT) in the medical field. It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems, which is essential in smart healthcare. However, Personal Health Records (PHRs) are normally kept in public cloud servers controlled by IoMT service providers, so privacy and security incidents may be frequent. Fortunately, Searchable Encryption (SE), which can be used to execute queries on encrypted data, can address the issue above. Nevertheless, most existing SE schemes cannot solve the vector dominance threshold… More >
Open Access
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4781-4803, 2024, DOI:10.32604/cmc.2024.050821
Abstract Smart agriculture modifies traditional farming practices, and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies. In today’s world where technology is everything, these technologies are utilized to streamline regular tasks and procedures in agriculture, one of the largest and most significant industries in every nation. This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems. Divided into three main sections—security analysis, system architecture and design and risk assessment of Cyber-Physical Systems (CPS) applications—the study delves… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4805-4824, 2024, DOI:10.32604/cmc.2024.050931
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract The application of deep learning techniques in the medical field, specifically for Atrial Fibrillation (AFib) detection through Electrocardiogram (ECG) signals, has witnessed significant interest. Accurate and timely diagnosis increases the patient’s chances of recovery. However, issues like overfitting and inconsistent accuracy across datasets remain challenges. In a quest to address these challenges, a study presents two prominent deep learning architectures, ResNet-50 and DenseNet-121, to evaluate their effectiveness in AFib detection. The aim was to create a robust detection mechanism that consistently performs well. Metrics such as loss, accuracy, precision, sensitivity, and Area Under the Curve… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4825-4844, 2024, DOI:10.32604/cmc.2024.047852
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract The act of transmitting photos via the Internet has become a routine and significant activity. Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced. This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images. The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression. This paper introduces… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4845-4861, 2024, DOI:10.32604/cmc.2024.051539
Abstract Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, 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… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860
Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4905-4924, 2024, DOI:10.32604/cmc.2024.052016
Abstract Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710
Abstract Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967
（This article belongs to the Special Issue: Intelligent Detection Methods for AI-Powered Healthcare and Enhanced Medical Insights)
Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4963-4977, 2024, DOI:10.32604/cmc.2024.050204
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435
Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158
Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371
（This article belongs to the Special Issue: Unveiling the Role of AIGC, Large Models, and Human - Centric Insights in Digital Defense)
Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5051-5066, 2024, DOI:10.32604/cmc.2024.050865
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract With the rapid advancement of social economies, intelligent transportation systems are gaining increasing attention. Central to these systems is the detection of abnormal vehicle behavior, which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions. Current research on detecting abnormal traffic behaviors is still nascent, with significant room for improvement in recognition accuracy. To address this, this research has developed a new model for recognizing abnormal traffic behaviors. This model employs the R3D network as its core architecture, incorporating a dense block to facilitate feature reuse. This… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5067-5082, 2024, DOI:10.32604/cmc.2024.051494
Abstract Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5083-5103, 2024, DOI:10.32604/cmc.2024.050921
Abstract To support the explosive growth of Information and Communications Technology (ICT), Mobile Edge Computing (MEC) provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge. However, resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications. To address the difficulty of running computationally intensive applications on resource-constrained clients, a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper. Then a user benefit function EoU (Experience of Users) is… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5105-5124, 2024, DOI:10.32604/cmc.2024.049584
Abstract This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies. Unlike independent batch tasks, workflows typically consist of multiple subtasks with intrinsic correlations and dependencies. It necessitates the distribution of various computational tasks to appropriate computing node resources in accordance with task dependencies to ensure the smooth completion of the entire workflow. Workflow scheduling must consider an array of factors, including task dependencies, availability of computational resources, and the schedulability of tasks. Therefore, this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5143-5174, 2024, DOI:10.32604/cmc.2024.051364
Abstract The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos. Grounded in the H.264 video coding standard, the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel. Subsequently, it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information, enabling successful watermark extraction and lossless recovery of the original video content. Experimental results demonstrate the algorithm’s strong imperceptibility, with… More >
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CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally,… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384
Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5273-5294, 2024, DOI:10.32604/cmc.2024.050869
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios, this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access (OFDMA) and non-orthogonal multiple access (NOMA). The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units (RUs), and NOMA is used on each RU to enable more users to access the channel and improve spectrum efficiency. Based on the protocol designed in this paper, in the case of imperfect successive interference… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >
Open Access
ARTICLE
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >
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