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Yan Dong^1,2, Kang Zhao¹, Liang Gao¹, Hao Li^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1-18, 2024, DOI:10.32604/cmc.2024.048870
（This article belongs to the Special Issue: Multiscale Computational Methods for Advanced Materials and Structures)
Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >

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Tsu-Yang Wu^1,2, Haonan Li², Saru Kumari³, Chien-Ming Chen^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 19-46, 2024, DOI:10.32604/cmc.2024.048347
Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian mutation helps the algorithm avoid… More >

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Deming Lei, Surui Duan, Mingbo Li^*, Jing Wang
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the elite class. Adaptive adjustment on… More >

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Souhayla Dargaoui¹, Mourade Azrour^1,*, Ahmad El Allaoui¹, Azidine Guezzaz², Abdulatif Alabdulatif³, Abdullah Alnajim⁴
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 65-91, 2024, DOI:10.32604/cmc.2024.047625
Abstract Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 and smart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still the biggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services provided by an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures, data, and devices. Authentication, as the first line of defense against security threats, becomes the priority of everyone. It can either grant or deny users access to resources according… More >

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Shafi Ullah¹, Sibghat Ullah Bazai^1,*, Mohammad Imran², Qazi Mudassar Ilyas^3,*, Abid Mehmood⁴, Muhammad Asim Saleem⁵, Muhmmad Aasim Rafique³, Arsalan Haider⁶, Ilyas Khan⁷, Sajid Iqbal³, Yonis Gulzar⁴, Kauser Hameed³
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 93-115, 2024, DOI:10.32604/cmc.2024.048796
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the data and devices becomes challenging… More >

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Ankita Gangwar¹, Vijaypal Singh Dhaka¹, Geeta Rani^2,*, Shrey Khandelwal¹, Ester Zumpano^3,4, Eugenio Vocaturo^3,4
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119
Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes… More >

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Jiao Wang, Bin Wu^*, Hongying Zhang
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >

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ARTICLE

Yu Zhou¹, Bosong Lin¹, Siqi Hu², Dandan Yu^3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 161-182, 2024, DOI:10.32604/cmc.2024.047836
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >

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Shabana Ramzan¹, Yazeed Yasin Ghadi², Hanan Aljuaid³, Aqsa Mahmood^1,*, Basharat Ali⁴
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 183-199, 2024, DOI:10.32604/cmc.2024.047603
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield. These parameters play a vital… More >

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Naglaa F. Soliman¹, Fatma E. Fadl-Allah², Walid El-Shafai^3,4,*, Mahmoud I. Aly², Maali Alabdulhafith¹, Fathi E. Abd El-Samie¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 201-241, 2024, DOI:10.32604/cmc.2024.046757
Abstract The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away from unauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper is mainly… More >

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Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine… More >

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ARTICLE

Yashan Feng¹, Yafang Tian¹, Yongxin Yang¹, Yufang Zhang¹, Haiwei Guo¹, Jing’an Li^2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 263-278, 2024, DOI:10.32604/cmc.2024.047004
（This article belongs to the Special Issue: Application of Soft Computing in Techniques in Materials Development)
Abstract Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment.… More >

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Zenglun Guan^1,2, Murad S. Alfarzaeai^1,3,*, Eryi Hu^1,3,*, Taqiaden Alshmeri⁴, Wang Peng³
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 279-306, 2024, DOI:10.32604/cmc.2024.047159
（This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >

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Xingfan Zhao¹, Changgen Peng^1,2,*, Weijie Tan², Kun Niu¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 307-328, 2024, DOI:10.32604/cmc.2024.047975
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract Traditional blockchain key management schemes store private keys in the same location, which can easily lead to security issues such as a single point of failure. Therefore, decentralized threshold key management schemes have become a research focus for blockchain private key protection. The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security. The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress, but these schemes do not consider participants’ self-interested behavior, and require trusted nodes to keep private key fragments, resulting in a narrow… More >

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ARTICLE

Qiang Fu¹, Jun Wang^1,*, Changfu Si¹, Jiawei Liu²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 329-349, 2024, DOI:10.32604/cmc.2024.048117
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract As industrialization and informatization become more deeply intertwined, industrial control networks have entered an era of intelligence. The connection between industrial control networks and the external internet is becoming increasingly close, which leads to frequent security accidents. This paper proposes a model for the industrial control network. It includes a malware containment strategy that integrates intrusion detection, quarantine, and monitoring. Based on this model, the role of key nodes in the spread of malware is studied, a comparison experiment is conducted to validate the impact of the containment strategy. In addition, the dynamic behavior of the model is analyzed, the… More >

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Ameni Ellouze¹, Nesrine Kadri², Alaa Alaerjan^3,*, Mohamed Ksantini¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >

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Xiao Lu^1,*, Chengling Jiang¹, Zhoujun Ma¹, Haitao Li², Yuexin Liu²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect tiny objects. Secondly, a simplified… More >

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Cheng Wan¹, Jiani Zhao¹, Xiangqian Hong², Weihua Yang^2,*, Shaochong Zhang^2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 391-407, 2024, DOI:10.32604/cmc.2024.048307
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >

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Luda Chen¹, Kuangzhu Bao², Ying Chen², Jingang Hao^2,*, Jianfeng He^1,3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

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Sardar Hasen Ali^*, Maiwan Bahjat Abdulrazzaq
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

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Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771
（This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel approach: Safety-Constrained Multi-Agent Reinforcement Learning… More >

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ARTICLE

Hongle Li¹, SeongKi Kim^2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2024.048402
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications.… More >

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Yifan Gao^*, Jieming Zhang, Zhanchen Chen, Xianchao Chen
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 493-507, 2024, DOI:10.32604/cmc.2024.048615
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and… More >

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Yutao Hu¹, Yuntao Zhao^1,*, Yongxin Feng², Xiangyu Ma¹
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 509-530, 2024, DOI:10.32604/cmc.2024.042216
Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on time based on new data… More >

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Meng Huang, Honglei Wei^*, Xianyi Zhai
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >

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Supeng Yu¹, Fen Huang^1,*, Chengcheng Fan^2,3,4,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 549-562, 2024, DOI:10.32604/cmc.2024.048608
Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One… More >

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Jiajun Yan, Ying Zhou^*, Anchen Dai, Tao Wang
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 563-580, 2024, DOI:10.32604/cmc.2024.048922
Abstract As an emerging network paradigm, the software-defined network (SDN) finds extensive application in areas such as smart grids, the Internet of Things (IoT), and edge computing. The forwarding layer in software-defined networks is susceptible to eavesdropping attacks. Route hopping is a moving target defense (MTD) technology that is frequently employed to resist eavesdropping attacks. In the traditional route hopping technology, both request and reply packets use the same hopping path. If an eavesdropping attacker monitors the nodes along this path, the risk of 100% data leakage becomes substantial. In this paper, we present an effective route hopping approach, called two-day… More >

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Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202
Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural… More >

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Kai Wei¹, Song Yu², Qingxian Pan^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240
（This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value on the public dataset. In… More >

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Yushan Yin¹, Witold Pedrycz^1,2, Zhiwu Li^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 623-650, 2024, DOI:10.32604/cmc.2024.048787
Abstract Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with… More >

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Ahsan Wajahat¹, Jingsha He¹, Nafei Zhu¹, Tariq Mahmood^2,3, Tanzila Saba², Amjad Rehman Khan², Faten S. Alamri^4,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530
Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This means that while attempting to… More >

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Hala AlShamlan^*, Halah AlMazrua^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146
Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

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Fangjun Luan^1,2,3, Xuewen Mu^1,2,3, Shuai Yuan^1,2,3,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2024.048502
Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries. However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To address these issues, we propose a novel approach for online signature verification, using a one-dimensional Ghost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature information and extracting local information, effectively complementing whole and local signature features and mitigating… More >

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Aizaz Ali¹, Maqbool Khan^1,2, Khalil Khan³, Rehan Ullah Khan⁴, Abdulrahman Aloraini^4,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features, presents an additional hurdle due… More >

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Wael M. S. Yafooz^*, Abdullah Alsaeedi
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims… More >

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Siyuan Liu^1,*, Jinying Huang², Jiancheng Ma¹, Licheng Jing², Yuxuan Wang²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 761-777, 2024, DOI:10.32604/cmc.2024.049484
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation via cost-friendly complementary label learning.… More >

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Ananthakrishnan Balasundaram^1,2, Ayesha Shaik^1,2,*, Japmann Kaur Banga², Aman Kumar Singh²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

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Shahid Naseem¹, Tariq Mahmood^2,3, Amjad Rehman Khan², Umer Farooq¹, Samra Nawazish⁴, Faten S. Alamri^5,*, Tanzila Saba²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 801-817, 2024, DOI:10.32604/cmc.2024.047623
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device… More >

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Yuejiao Wang, Zhong Ma^*, Chaojie Yang, Yu Yang, Lu Wei
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

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Jingyi Mao, Yuchen Zhou, Yifan Wang, Junyu Li, Ziqing Liu, Fanliang Bu^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 837-855, 2024, DOI:10.32604/cmc.2024.048703
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract Voice portrait technology has explored and established the relationship between speakers’ voices and their facial features, aiming to generate corresponding facial characteristics by providing the voice of an unknown speaker. Due to its powerful advantages in image generation, Generative Adversarial Networks (GANs) have now been widely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based on GANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs face limitations in image generation quality and struggle to maintain facial similarity. Additionally, the training process is relatively unstable, thereby affecting the overall generative performance… More >

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Amardeep Singh¹, Hamad Ali Abosaq², Saad Arif³, Zohaib Mushtaq^4,*, Muhammad Irfan⁵, Ghulam Abbas⁶, Arshad Ali⁷, Alanoud Al Mazroa⁸
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 857-873, 2024, DOI:10.32604/cmc.2024.048036
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the initial phase, the rectified linear… More >

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Mohammed Alatiyyah^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 875-907, 2024, DOI:10.32604/cmc.2024.047790
（This article belongs to the Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts… More >

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Yiwei Liu¹, Yingnan Zhao^1,*, Yi Chen¹, Zheng Hu¹, Min Xia²
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901
（This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place of C3, employing Spatial Pyramid… More >

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Le Zou¹, Linsong Hu¹, Yifan Wang¹, Zhize Wu², Xiaofeng Wang^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 927-941, 2024, DOI:10.32604/cmc.2024.048889
Abstract Depth estimation is an important task in computer vision. Collecting data at scale for monocular depth estimation is challenging, as this task requires simultaneously capturing RGB images and depth information. Therefore, data augmentation is crucial for this task. Existing data augmentation methods often employ pixel-wise transformations, which may inadvertently disrupt edge features. In this paper, we propose a data augmentation method for monocular depth estimation, which we refer to as the Perpendicular-Cutdepth method. This method involves cutting real-world depth maps along perpendicular directions and pasting them onto input images, thereby diversifying the data without compromising edge features. To validate the… More >

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Kranthi Alluri, S. Gopikrishnan^*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each… More >

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Muhammad Umar Nasir¹, Omar Kassem Khalil², Karamath Ateeq³, Bassam SaleemAllah Almogadwy⁴, M. A. Khan⁵, Khan Muhammad Adnan^6,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 963-981, 2024, DOI:10.32604/cmc.2024.047874
Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons… More >

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Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541
Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects.… More >

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Yan Xiang^1,2, Daofeng Li^1,2,*, Xinyi Meng^1,2, Chengfeng Dong^1,2, Guanglin Qin^1,2
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1005-1023, 2024, DOI:10.32604/cmc.2024.047143
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection… More >

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Radwan Abu-Gdairi¹, R. Mareay^2,*, M. Badr³
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647
（This article belongs to the Special Issue: Emerging Trends in Fuzzy Logic)
Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life application from medical records is… More >

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Meiyan Zhang¹, Boqi Zhao¹, Jipu Li², Qisong Wang^1,*, Dan Liu¹, Jinwei Sun¹, Jingxiao Liao^1,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1039-1061, 2024, DOI:10.32604/cmc.2024.048643
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological signals and then reflected in… More >

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