Home / Journals / CMC / Vol.78, No.1, 2024
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  • Open AccessOpen Access

    REVIEW

    Fuzzing: Progress, Challenges, and Perspectives

    Zhenhua Yu1, Zhengqi Liu1, Xuya Cong1,*, 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 >

  • Open AccessOpen Access

    REVIEW

    A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

    Sunil Kumar1, Dilip Kumar1, Ramraj Dangi2, Gaurav Choudhary3, Nicola Dragoni4, Ilsun You5,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Software Defect Prediction Method Based on Stable Learning

    Xin Fan1,2,3, Jingen Mao2,3,*, Liangjue Lian2,3, Li Yu1, Wei Zheng2,3, Yun Ge2,3
    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 >

  • Open AccessOpen Access

    ARTICLE

    Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    Facial Image-Based Autism Detection: A Comparative Study of Deep Neural Network Classifiers

    Tayyaba Farhat1,2, Sheeraz Akram3,*, Hatoon S. AlSagri3, Zulfiqar Ali4, Awais Ahmad3, Arfan Jaffar1,2
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022
    (This article belongs to this 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 >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Approach for Hand Gesture Recognition: Applications in Deaf Communication and Healthcare

    Khursheed Aurangzeb1, Khalid Javeed2, Musaed Alhussein1, Imad Rida3, Syed Irtaza Haider1, Anubha Parashar4,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Weber Law Based Approach for Multi-Class Image Forgery Detection

    Arslan Akram1,3, Javed Rashid2,3,4, Arfan Jaffar1, Fahima Hajjej5, Waseem Iqbal6, Nadeem Sarwar7,*
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 145-166, 2024, DOI:10.32604/cmc.2023.041074
    (This article belongs to this 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 >

  • Open AccessOpen Access

    REVIEW

    Dynamic SLAM Visual Odometry Based on Instance Segmentation: A Comprehensive Review

    Jiansheng Peng1,2,*, 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 >

  • Open AccessOpen Access

    REVIEW

    A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

    Xiaozhong Yu1,2,*, Jinhua Zheng1,2
    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 >

  • Open AccessOpen Access

    ARTICLE

    RPL-Based IoT Networks under Decreased Rank Attack: Performance Analysis in Static and Mobile Environments

    Amal Hkiri1,*, 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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

    Hyun Kwon1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

    Ming Luo1, Huili Dou2, Ning Zheng3,*
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067
    (This article belongs to this 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 >

  • Open AccessOpen Access

    ARTICLE

    ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

    Sijia Zhu1,*, 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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    Enhancing IoT Security: Quantum-Level Resilience against Threats

    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 >

  • Open AccessOpen Access

    ARTICLE

    Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

    Ch Avais Hanif1, Muhammad Ali Mughal1, Muhammad Attique Khan2,3,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

    Liang Zeng1,2, Junyang Shi1, Yanyan Li1, Shanshan Wang1,2,*, Weigang Li3
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803
    (This article belongs to this 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 >

  • Open AccessOpen Access

    ARTICLE

    Using MsfNet to Predict the ISUP Grade of Renal Clear Cell Carcinoma in Digital Pathology Images

    Kun Yang1,2,3, Shilong Chang1, Yucheng Wang1, Minghui Wang1, Jiahui Yang1, Shuang Liu1,2,3, Kun Liu1,2,3, Linyan Xue1,2,3,*
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994
    (This article belongs to this 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 >

  • Open AccessOpen Access

    ARTICLE

    An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    A Composite Transformer-Based Multi-Stage Defect Detection Architecture for Sewer Pipes

    Zifeng Yu1, Xianfeng Li1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Recommendation Method for Contrastive Enhancement of Neighborhood Information

    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 >

  • Open AccessOpen Access

    ARTICLE

    Network Intrusion Traffic Detection Based on Feature Extraction

    Xuecheng Yu1, Yan Huang2, Yu Zhang1, Mingyang Song1, Zhenhong Jia1,3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    A Measurement Study of the Ethereum Underlying P2P Network

    Mohammad Z. Masoud1, Yousef Jaradat1, Ahmad Manasrah2, Mohammad Alia3, Khaled Suwais4,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    Lightweight Malicious Code Classification Method Based on Improved SqueezeNet

    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 >

  • Open AccessOpen Access

    ARTICLE

    A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

    Haizhen Wang1,2,*, Jinying Yan1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

    Syeda Wajiha Zahra1, Muhammad Nadeem2, Ali Arshad3,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Identity Protection in Metaverse-Based Psychological Counseling System

    Jun Lee1, Hanna Lee2, Seong Chan Lee2, Hyun Kwon3,*
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 617-632, 2024, DOI:10.32604/cmc.2023.045768
    (This article belongs to this 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 >

  • Open AccessOpen Access

    ARTICLE

    SDH-FCOS: An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

    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 this 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 >

  • Open AccessOpen Access

    ARTICLE

    An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

    Junpeng Wu1,2,*, 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 this 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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    ARTICLE

    IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

    Yajing Ma1,2,3, Gulila Altenbek1,2,3,*, 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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    ARTICLE

    An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

    Yanmei Li1,*, 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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    ARTICLE

    Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

    Yang Yang1, Zhenying Qu1, Zefan Yan1, Zhipeng Gao1,*, Ti Wang2
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 735-757, 2024, DOI:10.32604/cmc.2023.045807
    (This article belongs to this 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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    ARTICLE

    YOLO-DD: Improved YOLOv5 for Defect Detection

    Jinhai Wang1, Wei Wang1, Zongyin Zhang1, Xuemin Lin1, Jingxian Zhao1, Mingyou Chen1, Lufeng Luo2,*
    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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    ARTICLE

    Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

    S. Sofana Reka1, Ankita Bagelikar2, Prakash Venugopal2,*, 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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    ARTICLE

    Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

    Ala’a R. Al-Shamasneh1, Rabha W. Ibrahim2,3,4,*
    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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    ARTICLE

    Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

    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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    ARTICLE

    A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM

    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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    ARTICLE

    Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

    Shereen K. Refaay1, Samia A. Ali2, Moumen T. El-Melegy2, Louai A. Maghrabi3, Hamdy H. El-Sayed1,*
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 873-897, 2024, DOI:10.32604/cmc.2023.044982
    (This article belongs to this 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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    ARTICLE

    Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

    Chengsheng Yuan1,2, Baojie Cui1,2, Zhili Zhou3, Xinting Li4,*, 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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    ARTICLE

    Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

    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 this 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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    ARTICLE

    Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

    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 this 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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    ARTICLE

    Smart Energy Management System Using Machine Learning

    Ali Sheraz Akram1, Sagheer Abbas1, Muhammad Adnan Khan2,3,5, Atifa Athar4, Taher M. Ghazal5,6, Hussam Al Hamadi7,*
    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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    ARTICLE

    A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

    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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    ARTICLE

    Image Inpainting Technique Incorporating Edge Prior and Attention Mechanism

    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 this 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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    ARTICLE

    Identification of Important FPGA Modules Based on Complex Network

    Senjie Zhang1,2, Jinbo Wang2,*, Shan Zhou2, Jingpei Wang2,3, Zhenyong Zhang4,*, 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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    ARTICLE

    Credit Card Fraud Detection Using Improved Deep Learning Models

    Sumaya S. Sulaiman1,2,*, 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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    ARTICLE

    A Video Captioning Method by Semantic Topic-Guided Generation

    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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