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REVIEW

Zhenhua Yu¹, Zhengqi Liu¹, Xuya Cong^1,*, Xiaobo Li², Li Yin³
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1-29, 2024, DOI:10.32604/cmc.2023.042361
Abstract As one of the most effective techniques for finding software vulnerabilities, fuzzing has become a hot topic in software security. It feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the system. In recent years, considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing, so there are more and more methods and forms, which make it difficult to have a comprehensive understanding of the technique. This paper conducts a thorough survey of fuzzing, focusing on its general process, classification, common application scenarios, and some state-of-the-art techniques that have been… More >

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385

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Sunil Kumar¹, Dilip Kumar¹, Ramraj Dangi², Gaurav Choudhary³, Nicola Dragoni⁴, Ilsun You^5,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 31-63, 2024, DOI:10.32604/cmc.2023.047084
Abstract The widespread and growing interest in the Internet of Things (IoT) may be attributed to its usefulness in many different fields. Physical settings are probed for data, which is then transferred via linked networks. There are several hurdles to overcome when putting IoT into practice, from managing server infrastructure to coordinating the use of tiny sensors. When it comes to deploying IoT, everyone agrees that security is the biggest issue. This is due to the fact that a large number of IoT devices exist in the physical world and that many of them have constrained resources such as electricity, memory,… More >

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356

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Xin Fan^1,2,3, Jingen Mao^2,3,*, Liangjue Lian^2,3, Li Yu¹, Wei Zheng^2,3, Yun Ge^2,3
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522
Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

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286

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Dan Xu^*, Jiale Ru, Jinlong Shi
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 85-104, 2024, DOI:10.32604/cmc.2023.045258
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract Video salient object detection (VSOD) aims at locating the most attractive objects in a video by exploring the spatial and temporal features. VSOD poses a challenging task in computer vision, as it involves processing complex spatial data that is also influenced by temporal dynamics. Despite the progress made in existing VSOD models, they still struggle in scenes of great background diversity within and between frames. Additionally, they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration. We propose a multi-stream temporal enhanced network (MSTENet) to address these problems. It… More >

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575

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216

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Tayyaba Farhat^1,2, Sheeraz Akram^3,*, Hatoon S. AlSagri³, Zulfiqar Ali⁴, Awais Ahmad³, Arfan Jaffar^1,2
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated… More >

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210

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Khursheed Aurangzeb¹, Khalid Javeed², Musaed Alhussein¹, Imad Rida³, Syed Irtaza Haider¹, Anubha Parashar^4,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 127-144, 2024, DOI:10.32604/cmc.2023.042886
Abstract Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant advancements in computer vision-based gesture recognition for language understanding, two considerable challenges persist in this field: (a) limited and common gestures are considered, (b) processing multiple channels of information across a network takes huge computational time during discriminative feature extraction. Therefore, a novel hand vision-based convolutional neural network (CNN) model named (HVCNNM)… More >

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201

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Arslan Akram^1,3, Javed Rashid^2,3,4, Arfan Jaffar¹, Fahima Hajjej⁵, Waseem Iqbal⁶, Nadeem Sarwar^7,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 145-166, 2024, DOI:10.32604/cmc.2023.041074
（This article belongs to the Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)
Abstract Today’s forensic science introduces a new research area for digital image analysis for multimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or create misleading publicity by using tempered images. Exiting forgery detection methods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybrid method for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal… More >

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639

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184

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Jiansheng Peng^1,2,*, Qing Yang¹, Dunhua Chen¹, Chengjun Yang², Yong Xu², Yong Qin²
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 167-196, 2024, DOI:10.32604/cmc.2023.041900
Abstract Dynamic Simultaneous Localization and Mapping (SLAM) in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving. However, in the face of complex real-world environments, current dynamic SLAM systems struggle to achieve precise localization and map construction. With the advancement of deep learning, there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years, and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM. Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic… More >

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461

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194

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REVIEW

Xiaozhong Yu^1,2,*, Jinhua Zheng^1,2
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943
Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to the network structure differences; and… More >

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487

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192

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Amal Hkiri^1,*, Mouna Karmani¹, Omar Ben Bahri², Ahmed Mohammed Murayr², Fawaz Hassan Alasmari², Mohsen Machhout¹
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 227-247, 2024, DOI:10.32604/cmc.2023.047087
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem. Despite its significance, RPL’s susceptibility to attacks remains a concern. This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static and mobile network environments. We employ the Random Direction Mobility Model (RDM) for mobile scenarios within the Cooja simulator. Our systematic evaluation focuses on critical performance metrics, including Packet Delivery Ratio (PDR), Average End to End Delay (AE2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption… More >

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214

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Hyun Kwon^1,*, Sanghyun Lee²
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 249-263, 2024, DOI:10.32604/cmc.2023.042466
Abstract Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific… More >

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361

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153

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Ming Luo¹, Huili Dou², Ning Zheng^3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067
（This article belongs to the Special Issue: Big Data Analysis for Healthcare Applications)
Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables. In terms… More >

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423

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137

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ARTICLE

Sijia Zhu^1,*, Pinxiu Wang², Ke Shen¹
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 283-302, 2024, DOI:10.32604/cmc.2023.045506
（This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
Abstract This paper proposes a new network structure, namely the ProNet network. Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment. The baseline model of the ProNet network is UperNet (Unified perceptual parsing Network), and the backbone network is ConvNext (Convolutional Network). A network structure based on depth-separable convolution and 1 × 1 convolution is used, which has good performance and robustness. We further optimise ProNet mainly in two aspects. One is data enhancement using increased noise and slight angle rotation, which can significantly increase the diversity of data and help… More >

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Yaoyao Du, Xiangkui Jiang^*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 303-327, 2024, DOI:10.32604/cmc.2023.046068
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the… More >

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Hosam Alhakami^*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 329-356, 2024, DOI:10.32604/cmc.2023.043439
Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks, with a focus on developing… More >

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151

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Ch Avais Hanif¹, Muhammad Ali Mughal¹, Muhammad Attique Khan^2,3,*, Nouf Abdullah Almujally⁴, Taerang Kim⁵, Jae-Hyuk Cha⁵
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 357-374, 2024, DOI:10.32604/cmc.2023.043061
Abstract The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of… More >

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220

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158

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Liang Zeng^1,2, Junyang Shi¹, Yanyan Li¹, Shanshan Wang^1,2,*, Weigang Li³
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution… More >

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Kun Yang^1,2,3, Shilong Chang¹, Yucheng Wang¹, Minghui Wang¹, Jiahui Yang¹, Shuang Liu^1,2,3, Kun Liu^1,2,3, Linyan Xue^1,2,3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract Clear cell renal cell carcinoma (ccRCC) represents the most frequent form of renal cell carcinoma (RCC), and accurate International Society of Urological Pathology (ISUP) grading is crucial for prognosis and treatment selection. This study presents a new deep network called Multi-scale Fusion Network (MsfNet), which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images. The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity. The model was trained and tested using 90 Hematoxylin and Eosin (H&E) stained whole slide images (WSIs), which were all cropped into 320… More >

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427

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145

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Zhihua Liu, Shengquan Liu^*, Jian Zhang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

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Zifeng Yu¹, Xianfeng Li^1,*, Lianpeng Sun², Jinjun Zhu², Jianxin Lin³
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 435-451, 2024, DOI:10.32604/cmc.2023.046685
Abstract Urban sewer pipes are a vital infrastructure in modern cities, and their defects must be detected in time to prevent potential malfunctioning. In recent years, to relieve the manual efforts by human experts, models based on deep learning have been introduced to automatically identify potential defects. However, these models are insufficient in terms of dataset complexity, model versatility and performance. Our work addresses these issues with a multi-stage defect detection architecture using a composite backbone Swin Transformer. The model based on this architecture is trained using a more comprehensive dataset containing more classes of defects. By ablation studies on the… More >

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294

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154

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Hairong Wang, Beijing Zhou^*, Lisi Zhang, He Ma
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 453-472, 2024, DOI:10.32604/cmc.2023.046560
Abstract Knowledge graph can assist in improving recommendation performance and is widely applied in various personalized recommendation domains. However, existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph. To tackle these issues, this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise. Specifically, first, this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items, mining the high-order neighbor information of users and items. Next,… More >

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2

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Xuecheng Yu¹, Yan Huang², Yu Zhang¹, Mingyang Song¹, Zhenhong Jia^1,3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2023.044999
Abstract With the increasing dimensionality of network traffic, extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems (IDS). However, both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features, resulting in an analysis that is not an optimal set. Therefore, in order to extract more representative traffic features as well as to improve the accuracy of traffic identification, this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T² and a multilayer convolutional bidirectional long short-term memory (MSC_BiLSTM)… More >

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176

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Kun Fang, Julong Pan^*, Lingyi Li, Ruihan Xiang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach… More >

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Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 515-532, 2024, DOI:10.32604/cmc.2023.044504
Abstract This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been compared with two other services… More >

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Tianmin Deng^*, Xiyue Zhang, Xinxin Cheng
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network, which amplifies the disparities between… More >

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112

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Li Li^*, Youran Kong, Qing Zhang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 551-567, 2024, DOI:10.32604/cmc.2023.045512
Abstract With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people’s lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore,… More >

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Haizhen Wang^1,2,*, Jinying Yan^1,*, Na Jia¹
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055
Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the variational autoencoder (VAE) is combined… More >

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Syeda Wajiha Zahra¹, Muhammad Nadeem², Ali Arshad^3,*, Saman Riaz³, Muhammad Abu Bakr⁴, Ashit Kumar Dutta⁵, Zaid Alzaid⁶, Badr Almutairi⁷, Sultan Almotairi⁸
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 589-616, 2024, DOI:10.32604/cmc.2023.042386
Abstract Various organizations store data online rather than on physical servers. As the number of user’s data stored in cloud servers increases, the attack rate to access data from cloud servers also increases. Different researchers worked on different algorithms to protect cloud data from replay attacks. None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack. This study presents the development of a TKN (Text, Key and Name) cryptographic algorithm aimed at protecting data from replay attacks. The program employs distinct ways to encrypt plain text [P], a user-defined Key [K], and a Secret Code… More >

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Jun Lee¹, Hanna Lee², Seong Chan Lee², Hyun Kwon^3,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 617-632, 2024, DOI:10.32604/cmc.2023.045768
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regarding client identity. However, these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions, such as facial cues, to the counselor. This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling. The proposed system incorporates a voice modulation function that instantly modifies/masks the client’s voice to safeguard their identity. Additionally, it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s… More >

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Bin Zhou, Bo Li^*, Wenfei Lan, Congwen Tian, Wei Yao
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 633-652, 2024, DOI:10.32604/cmc.2023.046667
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract Urban underground pipelines are an important infrastructure in cities, and timely investigation of problems in underground pipelines can help ensure the normal operation of cities. Owing to the growing demand for defect detection in urban underground pipelines, this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector (FCOS), called spatial pyramid pooling-fast (SPPF) feature fusion and dual detection heads based on FCOS (SDH-FCOS) model. This study improved the feature fusion component of the model network based on FCOS, introduced an SPPF network structure behind the last output feature layer of the… More >

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Haibo Li^*, Yongbo Yu, Zhenbo Zhao, Xiaokang Tang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 653-676, 2024, DOI:10.32604/cmc.2023.046424
（This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
Abstract Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead… More >

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Junpeng Wu^1,2,*, Zhenpeng Liu², Xingfan Jiang², Xinguang Tao², Ye Zhang³
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 677-694, 2024, DOI:10.32604/cmc.2023.045759
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy… More >

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Yajing Ma^1,2,3, Gulila Altenbek^1,2,3,*, Yingxia Yu¹
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486
Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations,… More >

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Yanmei Li^1,*, Jinghong Tang¹, Weiwu Ding¹, Jian Luo², Naveed Ahmad³, Rajesh Kumar⁴
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2023.046872
Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study, we introduce an advanced diagnostic methodology rooted in the Med-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to selectively focus on the salient… More >

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Yang Yang¹, Zhenying Qu¹, Zefan Yan¹, Zhipeng Gao^1,*, Ti Wang²
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 735-757, 2024, DOI:10.32604/cmc.2023.045807
（This article belongs to the Special Issue: Recognition Tasks with Transformers)
Abstract Nowadays, ensuring the quality of network services has become increasingly vital. Experts are turning to knowledge graph technology, with a significant emphasis on entity extraction in the identification of device configurations. This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms. Initially, an improved active learning approach is employed to select the most valuable unlabeled samples, which are subsequently submitted for expert labeling. This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set. Then the labeled samples are utilized to train the model… More >

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Jinhai Wang¹, Wei Wang¹, Zongyin Zhang¹, Xuemin Lin¹, Jingxian Zhao¹, Mingyou Chen¹, Lufeng Luo^2,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 759-780, 2024, DOI:10.32604/cmc.2023.041600
Abstract As computer technology continues to advance, factories have increasingly higher demands for detecting defects. However, detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes. To address this issue, this paper proposes YOLO-DD, a defect detection model based on YOLOv5 that is effective and robust. To improve the feature extraction process and better capture global information, the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer (RDAT). Additionally, an Information Gap Filling Strategy (IGFS) is proposed to improve the fusion of features at different… More >

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S. Sofana Reka¹, Ankita Bagelikar², Prakash Venugopal^2,*, V. Ravi², Harimurugan Devarajan³
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369
Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries… More >

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Ala’a R. Al-Shamasneh¹, Rabha W. Ibrahim^2,3,4,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 795-810, 2024, DOI:10.32604/cmc.2023.042755
Abstract The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detection model, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the “support vector… More >

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Qinhui Liu, Laizheng Zhu, Zhijie Gao, Jilong Wang, Jiang Li^*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 811-843, 2024, DOI:10.32604/cmc.2023.046040
Abstract To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method is designed to solve the… More >

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Zhenxiang He^*, Xunxi Wang, Chunwei Li
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607
Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long Short-term Memory Network (Bi-LSTM). By… More >

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Shereen K. Refaay¹, Samia A. Ali², Moumen T. El-Melegy², Louai A. Maghrabi³, Hamdy H. El-Sayed^1,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 873-897, 2024, DOI:10.32604/cmc.2023.044982
（This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
Abstract The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime. Many studies have been conducted for homogeneous networks, but few have been performed for heterogeneous wireless sensor networks. This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies. The proposed algorithms lack algorithm-specific parameters and metaphorical connotations. The proposed algorithms examine the search space based on the relations of the population with the best, worst, and randomly assigned solutions. The proposed algorithms can be evaluated using any… More >

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Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854
Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

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Xinyu Hu, Defeng Kong^*, Xiyang Liu, Junwei Zhang, Daode Zhang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet… More >

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Li Zheng, Gang Xu^*, Wenbin Chen
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 935-957, 2024, DOI:10.32604/cmc.2023.046006
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Drone logistics is a novel method of distribution that will become prevalent. The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption, resulting in cost savings for the company’s transportation operations. Logistics firms must discern the ideal location for establishing a logistics hub, which is challenging due to the simplicity of existing models and the intricate delivery factors. To simulate the drone logistics environment, this study presents a new mathematical model. The model not only retains the aspects of the current models, but also considers the degree of transportation difficulty from the logistics hub to… More >

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Ali Sheraz Akram¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,3,5, Atifa Athar⁴, Taher M. Ghazal^5,6, Hussam Al Hamadi^7,*
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 959-973, 2024, DOI:10.32604/cmc.2023.032216
Abstract Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization and increased user comfort. To… More >

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Wei Liu, Meijuan Yin^*, Jialong Zhang, Lunchong Cui
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475
Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as… More >

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Jinxian Bai, Yao Fan^*, Zhiwei Zhao, Lizhi Zheng
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 999-1025, 2024, DOI:10.32604/cmc.2023.044612
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Recently, deep learning-based image inpainting methods have made great strides in reconstructing damaged regions. However, these methods often struggle to produce satisfactory results when dealing with missing images with large holes, leading to distortions in the structure and blurring of textures. To address these problems, we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms. The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details. This method divides the inpainting task… More >

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Senjie Zhang^1,2, Jinbo Wang^2,*, Shan Zhou², Jingpei Wang^2,3, Zhenyong Zhang^4,*, Ruixue Wang²
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1027-1047, 2024, DOI:10.32604/cmc.2023.046355
Abstract The globalization of hardware designs and supply chains, as well as the integration of third-party intellectual property (IP) cores, has led to an increased focus from malicious attackers on computing hardware. However, existing defense or detection approaches often require additional circuitry to perform security verification, and are thus constrained by time and resource limitations. Considering the scale of actual engineering tasks and tight project schedules, it is usually difficult to implement designs for all modules in field programmable gate array (FPGA) circuits. Some studies have pointed out that the failure of key modules tends to cause greater damage to the… More >

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Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051
Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >

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Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418
Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

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