Home / Journals / CMC / Vol.77, No.3, 2023
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  • Open AccessOpen Access

    ARTICLE

    CFSA-Net: Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

    Jun Shu1,2, Shuai Wang1,2, Shiqi Yu1,2, Jie Zhang3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818
    Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Breast Cancer Diagnosis with Channel-Wise Attention Mechanisms in Deep Learning

    Muhammad Mumtaz Ali, Faiqa Maqsood, Shiqi Liu, Weiyan Hou, Liying Zhang, Zhenfei Wang*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2699-2714, 2023, DOI:10.32604/cmc.2023.045310
    (This article belongs to this Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Breast cancer, particularly Invasive Ductal Carcinoma (IDC), is a primary global health concern predominantly affecting women. Early and precise diagnosis is crucial for effective treatment planning. Several AI-based techniques for IDC-level classification have been proposed in recent years. Processing speed, memory size, and accuracy can still be improved for better performance. Our study presents ECAM, an Enhanced Channel-Wise Attention Mechanism, using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma (BIDC). The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture, improve data representation through hierarchical feature aggregation, and increase accuracy and… More >

  • Open AccessOpen Access

    ARTICLE

    Flag-Based Vehicular Clustering Scheme for Vehicular Ad-Hoc Networks

    Fady Samann1,*, Shavan Askar2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2715-2734, 2023, DOI:10.32604/cmc.2023.043580
    Abstract Clustering schemes in vehicular networks organize vehicles into logical groups. They are vital for improving network performance, accessing the medium, and enabling efficient data dissemination. Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles. However, the periodic exchange of messages overwhelms the system and reduces efficiency. This paper proposes the Flag-based Vehicular Clustering (FVC) scheme. The scheme leverages a combination of Fitness Score (FS), Link Expiration Time (LET), and clustering status flags to enable efficient cluster formation in a hybrid manner. The FVC relies on the periodic broadcast of the basic safety message in… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

    Lei Hu1,*, Yuanwen Lu1, Si Wang2, Wenbin Wang3, Yongmei Zhang4
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2735-2749, 2023, DOI:10.32604/cmc.2023.042974
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle (UAV) due to the complex background of distribution lines, variable morphology of equipment, and large differences in equipment sizes. Therefore, aiming at the difficult detection of power equipment in UAV inspection images, we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s. Based on the YOLOx-s network, we make the following improvements: 1) The Receptive Field Block (RFB) module is added after the shallow feature layer of the backbone network to… More >

  • Open AccessOpen Access

    ARTICLE

    Zero-DCE++ Inspired Object Detection in Less Illuminated Environment Using Improved YOLOv5

    Ananthakrishnan Balasundaram1,*, Anshuman Mohanty2, Ayesha Shaik1, Krishnadoss Pradeep2, Kedalu Poornachary Vijayakumar2, Muthu Subash Kavitha3
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2751-2769, 2023, DOI:10.32604/cmc.2023.044374
    (This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the Zero-DCE++ approach to enhance lowlight images. It is followed by optimizing the existing YOLOv5… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Method for Identifying Lower Limb Behavior Intentions Based on Surface Electromyography

    Liuyi Ling1,2,3, Yiwen Wang1,*, Fan Ding4, Li Jin1, Bin Feng3, Weixiao Li3, Chengjun Wang1, Xianhua Li1
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2771-2790, 2023, DOI:10.32604/cmc.2023.043383
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Surface electromyography (sEMG) is widely used for analyzing and controlling lower limb assisted exoskeleton robots. Behavior intention recognition based on sEMG is of great significance for achieving intelligent prosthetic and exoskeleton control. Achieving highly efficient recognition while improving performance has always been a significant challenge. To address this, we propose an sEMG-based method called Enhanced Residual Gate Network (ERGN) for lower-limb behavioral intention recognition. The proposed network combines an attention mechanism and a hard threshold function, while combining the advantages of residual structure, which maps sEMG of multiple acquisition channels to the lower limb motion states. Firstly, continuous wavelet transform… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

    Laila Almutairi1, Shabnam Mohamed Aslam2,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2791-2811, 2023, DOI:10.32604/cmc.2023.040268
    Abstract The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide. Modern data centres’ operating costs mostly come from back-end cloud infrastructure and energy consumption. In cloud computing, extensive communication resources are required. Moreover, cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements. It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers. This paper proposes a novel Energy and Communication (EC) aware scheduling (EC-scheduler) algorithm for green cloud computing, which optimizes data centre energy consumption and traffic load. The primary goal… More >

  • Open AccessOpen Access

    ARTICLE

    Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

    Sahar Arooj1, Muhammad Farhan Khan2, Tariq Shahzad3, Muhammad Adnan Khan4,5,6, Muhammad Umar Nasir7, Muhammad Zubair1, Atta-ur-Rahman8, Khmaies Ouahada3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2813-2831, 2023, DOI:10.32604/cmc.2023.043013
    Abstract Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body. Tumors are classified as benign or malignant, and the absence of cancer in the breast is considered normal. Deep learning, machine learning, and transfer learning… More >

  • Open AccessOpen Access

    ARTICLE

    Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization

    Soonshin Seo1,2, Ji-Hwan Kim2,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816
    Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly, it proposes the incorporation of… More >

  • Open AccessOpen Access

    ARTICLE

    DL-Powered Anomaly Identification System for Enhanced IoT Data Security

    Manjur Kolhar*, Sultan Mesfer Aldossary
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2857-2879, 2023, DOI:10.32604/cmc.2023.042726
    Abstract In many commercial and public sectors, the Internet of Things (IoT) is deeply embedded. Cyber security threats aimed at compromising the security, reliability, or accessibility of data are a serious concern for the IoT. Due to the collection of data from several IoT devices, the IoT presents unique challenges for detecting anomalous behavior. It is the responsibility of an Intrusion Detection System (IDS) to ensure the security of a network by reporting any suspicious activity. By identifying failed and successful attacks, IDS provides a more comprehensive security capability. A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.… More >

  • Open AccessOpen Access

    ARTICLE

    Using Metaheuristic OFA Algorithm for Service Placement in Fog Computing

    Riza Altunay1,2,*, Omer Faruk Bay3
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2881-2897, 2023, DOI:10.32604/cmc.2023.042340
    Abstract The use of fog computing in the Internet of Things (IoT) has emerged as a crucial solution, bringing cloud services closer to end users to process large amounts of data generated within the system. Despite its advantages, the increasing task demands from IoT objects often overload fog devices with limited resources, resulting in system delays, high network usage, and increased energy consumption. One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds. To address this challenge, we propose a novel Optimal Foraging Algorithm (OFA) for task placement on appropriate fog… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning for Data Fusion: A Fuzzy AHP Approach for Open Issues

    Vinay Kukreja1, Asha Abraham2, K. Kalaiselvi3, K. Deepa Thilak3, Shanmugasundaram Hariharan4, Shih-Yu Chen5,6,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2899-2914, 2023, DOI:10.32604/cmc.2023.045136
    Abstract Data fusion generates fused data by combining multiple sources, resulting in information that is more consistent, accurate, and useful than any individual source and more reliable and consistent than the raw original data, which are often imperfect, inconsistent, complex, and uncertain. Traditional data fusion methods like probabilistic fusion, set-based fusion, and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data. Data fusion is the process of integrating multiple data sources. Data filtering means examining a dataset to exclude, rearrange, or apportion data according to the criteria. Different sensors generate a large… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Speech Emotion Recognition Focusing on High-Level Data Representations and Swift Feature Extraction Calculation

    Akmalbek Abdusalomov1, Alpamis Kutlimuratov2, Rashid Nasimov3, Taeg Keun Whangbo1,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2915-2933, 2023, DOI:10.32604/cmc.2023.044466
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The performance of a speech emotion recognition (SER) system is heavily influenced by the efficacy of its feature extraction techniques. The study was designed to advance the field of SER by optimizing feature extraction techniques, specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients (MFCC). This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches. Ultimately, the primary objective was to elevate both the intricacy and effectiveness of our SER model, with a focus on augmenting its proficiency in the accurate identification of emotions… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

    Xiang Wang1, Liangsa Wang2,*, Han Li1, Yibin Guo1
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948
    (This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects. Firstly, an improved whale algorithm… More >

  • Open AccessOpen Access

    REVIEW

    Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

    Jiansheng Peng1,2,*, Jinsong Guo1, Fengbo Bao1, Chengjun Yang2, Yong Xu2, Yong Qin2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897
    Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of federated learning and multi-robot systems… More >

  • Open AccessOpen Access

    ARTICLE

    Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

    Abdulwahab Alazeb*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2995-3014, 2023, DOI:10.32604/cmc.2023.043173
    (This article belongs to this Special Issue: Multimedia Encryption and Information Security)
    Abstract Data protection in databases is critical for any organization, as unauthorized access or manipulation can have severe negative consequences. Intrusion detection systems are essential for keeping databases secure. Advancements in technology will lead to significant changes in the medical field, improving healthcare services through real-time information sharing. However, reliability and consistency still need to be solved. Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption. Disruptions to data items can propagate throughout the database, making it crucial to reverse fraudulent transactions without delay, especially in the healthcare industry, where real-time… More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

    Chao Dong, Xiangkui Jiang*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the Neck part of the original… More >

  • Open AccessOpen Access

    ARTICLE

    From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

    Asif Khan1, Nada Boudjellal2, Huaping Zhang1,*, Arshad Ahmad3, Maqbool Khan3
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3037-3055, 2023, DOI:10.32604/cmc.2023.044403
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data,… More >

  • Open AccessOpen Access

    ARTICLE

    SCChOA: Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

    Shanshan Wang1,2,3, Quan Yuan1, Weiwei Tan1, Tengfei Yang1, Liang Zeng1,2,3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3057-3075, 2023, DOI:10.32604/cmc.2023.044807
    Abstract Feature Selection (FS) is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy. However, due to the high dimensionality and complexity of the dataset, most optimization algorithms for feature selection suffer from a balance issue during the search process. Therefore, the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm (SCChOA) to address the feature selection problem. In this approach, firstly, a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm (SCA) and the Chimp Optimization Algorithm (ChOA), enabling a more effective search in the objective space. Secondly,… More >

  • Open AccessOpen Access

    ARTICLE

    Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

    Manas Ranjan Prusty1, Rishi Dinesh2, Hariket Sukesh Kumar Sheth2, Alapati Lakshmi Viswanath2, Sandeep Kumar Satapathy2,3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718
    Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time augmentation, to improve segmentation accuracy.… More >

  • Open AccessOpen Access

    ARTICLE

    Maximizing Influence in Temporal Social Networks: A Node Feature-Aware Voting Algorithm

    Wenlong Zhu1,2,*, Yu Miao1, Shuangshuang Yang3, Zuozheng Lian1,2, Lianhe Cui1
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646
    Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting… More >

  • Open AccessOpen Access

    ARTICLE

    PP-GAN: Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

    Jongwook Si1, Sungyoung Kim2,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3119-3138, 2023, DOI:10.32604/cmc.2023.043797
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The objective of style transfer is to maintain the content of an image while transferring the style of another image. However, conventional methods face challenges in preserving facial features, especially in Korean portraits where elements like the “Gat” (a traditional Korean hat) are prevalent. This paper proposes a deep learning network designed to perform style transfer that includes the “Gat” while preserving the identity of the face. Unlike traditional style transfer techniques, the proposed method aims to preserve the texture, attire, and the “Gat” in the style image by employing image sharpening and face landmark, with the GAN. The color,… More >

  • Open AccessOpen Access

    ARTICLE

    Software Coupling and Cohesion Model for Measuring the Quality of Software Components

    Zakarya Abdullah Alzamil*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3139-3161, 2023, DOI:10.32604/cmc.2023.042711
    Abstract Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements. The quality attribute is a qualitative property; however, the quantitative feature is needed for software measurement, which is not considered during the development of most software systems. Many research studies have investigated different approaches for measuring software quality, but with no practical approaches to quantify and measure quality attributes. This paper proposes a software quality measurement model, based on a software interconnection model, to measure the quality of software components and the overall quality of the software system. Unlike most of the existing approaches, the… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

    Yuzheng Yang*, Zhe Tu, Ying Liu, Huachun Zhou
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3163-3184, 2023, DOI:10.32604/cmc.2023.043974
    (This article belongs to this Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
    Abstract Currently, data security and privacy protection are becoming more and more important. Access control is a method of authorization for users through predefined policies. Token-based access control (TBAC) enhances the manageability of authorization through the token. However, traditional access control policies lack the ability to dynamically adjust based on user access behavior. Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility. As a result, this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control. The TBAC system divides the access control process… More >

  • Open AccessOpen Access

    ARTICLE

    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan1,*, Jianguo Yu1, Zafi Sherhan Syed2, Arif Hussain Magsi3, Nadeem Ahmed4
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650
    (This article belongs to this Special Issue: Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments )
    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task… More >

  • Open AccessOpen Access

    REVIEW

    Visual SLAM Based on Object Detection Network: A Review

    Jiansheng Peng1,2,*, Dunhua Chen1, Qing Yang1, Chengjun Yang2, Yong Xu2, Yong Qin2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3209-3236, 2023, DOI:10.32604/cmc.2023.041898
    Abstract Visual simultaneous localization and mapping (SLAM) is crucial in robotics and autonomous driving. However, traditional visual SLAM faces challenges in dynamic environments. To address this issue, researchers have proposed semantic SLAM, which combines object detection, semantic segmentation, instance segmentation, and visual SLAM. Despite the growing body of literature on semantic SLAM, there is currently a lack of comprehensive research on the integration of object detection and visual SLAM. Therefore, this study aims to gather information from multiple databases and review relevant literature using specific keywords. It focuses on visual SLAM based on object detection, covering different aspects. Firstly, it discusses… More >

  • Open AccessOpen Access

    ARTICLE

    FPGA Optimized Accelerator of DCNN with Fast Data Readout and Multiplier Sharing Strategy

    Tuo Ma, Zhiwei Li, Qingjiang Li*, Haijun Liu, Zhongjin Zhao, Yinan Wang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3237-3263, 2023, DOI:10.32604/cmc.2023.045948
    Abstract With the continuous development of deep learning, Deep Convolutional Neural Network (DCNN) has attracted wide attention in the industry due to its high accuracy in image classification. Compared with other DCNN hardware deployment platforms, Field Programmable Gate Array (FPGA) has the advantages of being programmable, low power consumption, parallelism, and low cost. However, the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator. The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing, but this method’s data multiplexing rate is low… More >

  • Open AccessOpen Access

    ARTICLE

    An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

    Xiulai Li1,2,3,4, Jieren Cheng1,3,*, Chengchun Ruan1,3, Bin Zhang1,3, Xiangyan Tang1,3, Mengzhe Sun5
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3265-3288, 2023, DOI:10.32604/cmc.2023.045588
    Abstract With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our methodology was applied to detect… More >

  • Open AccessOpen Access

    ARTICLE

    Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

    Tamal Kumar Kundu1, Dinesh Kumar Anguraj1,*, S. V. Sudha2,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3289-3304, 2023, DOI:10.32604/cmc.2023.039515
    Abstract A theoretical methodology is suggested for finding the malaria parasites’ presence with the help of an intelligent hyper-parameter tuned Deep Learning (DL) based malaria parasite detection and classification (HPTDL-MPDC) in the smear images of human peripheral blood. Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy. The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset. The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network (DNN) with the help of the contrastive divergence method to pre-train.… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

    Xiangmin Guo1,2, Guangjun Liang1,2,*, Jiayin Liu1,2, Xianyi Chen3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529
    (This article belongs to this Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
    Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud.… More >

  • Open AccessOpen Access

    ARTICLE

    Research on Human Activity Recognition Algorithm Based on LSTM-1DCNN

    Yuesheng Zhao1, Xiaoling Wang1,*, Yutong Luo2,*, Muhammad Shamrooz Aslam3
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3325-3347, 2023, DOI:10.32604/cmc.2023.040528
    Abstract With the rapid advancement of wearable devices, Human Activities Recognition (HAR) based on these devices has emerged as a prominent research field. The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer. This algorithm comprises two branches: one branch consists of a Long and Short-Term Memory Network (LSTM), while the other parallel branch incorporates a one-dimensional Convolutional Neural Network (1DCNN). The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately, which are then concatenated and fed into a fully… More >

  • Open AccessOpen Access

    ARTICLE

    Detection of Safety Helmet-Wearing Based on the YOLO_CA Model

    Xiaoqin Wu, Songrong Qian*, Ming Yang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3349-3366, 2023, DOI:10.32604/cmc.2023.043671
    (This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been realized for management, but the… More >

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    ARTICLE

    Asymmetric Loss Based on Image Properties for Deep Learning-Based Image Restoration

    Linlin Zhu, Yu Han, Xiaoqi Xi, Zhicun Zhang, Mengnan Liu, Lei Li, Siyu Tan, Bin Yan*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3367-3386, 2023, DOI:10.32604/cmc.2023.045878
    (This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Deep learning techniques have significantly improved image restoration tasks in recent years. As a crucial component of deep learning, the loss function plays a key role in network optimization and performance enhancement. However, the currently prevalent loss functions assign equal weight to each pixel point during loss calculation, which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully. To address this issue, this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task. This novel loss function can adjust the weight of… More >

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    ARTICLE

    EduASAC: A Blockchain-Based Education Archive Sharing and Access Control System

    Ronglei Hu1, Chuce He1, Yaping Chi2, Xiaoyi Duan1, Xiaohong Fan1, Ping Xu1, Wenbin Gao1,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3387-3422, 2023, DOI:10.32604/cmc.2023.042000
    Abstract In the education archive sharing system, when performing homomorphic ciphertext retrieval on the storage server, there are problems such as low security of shared data, confusing parameter management, and weak access control. This paper proposes an Education Archives Sharing and Access Control (EduASAC) system to solve these problems. The system research goal is to realize the sharing of security parameters, the execution of access control, and the recording of system behaviors based on the blockchain network, ensuring the legitimacy of shared membership and the security of education archives. At the same time, the system can be combined with most homomorphic… More >

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    ARTICLE

    Bearing Fault Diagnosis with DDCNN Based on Intelligent Feature Fusion Strategy in Strong Noise

    Chaoqian He1,2, Runfang Hao1,2,*, Kun Yang1,2, Zhongyun Yuan1,2, Shengbo Sang1,2, Xiaorui Wang1,2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3423-3442, 2023, DOI:10.32604/cmc.2023.045718
    (This article belongs to this Special Issue: Trends in Machine Learning and Internet of Things for Industrial Applications)
    Abstract Intelligent fault diagnosis in modern mechanical equipment maintenance is increasingly adopting deep learning technology. However, conventional bearing fault diagnosis models often suffer from low accuracy and unstable performance in noisy environments due to their reliance on a single input data. Therefore, this paper proposes a dual-channel convolutional neural network (DDCNN) model that leverages dual data inputs. The DDCNN model introduces two key improvements. Firstly, one of the channels substitutes its convolution with a larger kernel, simplifying the structure while addressing the lack of global information and shallow features. Secondly, the feature layer combines data from different sensors based on their… More >

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    ARTICLE

    An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

    Jiamin Xiang1, Ying Zhang1, Xiaohua Cao1,*, Zhigang Zhou2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3443-3466, 2023, DOI:10.32604/cmc.2023.045120
    Abstract This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles (AGVs) under the composite operation mode. The multi-objective model aims to minimize the maximum completion time, the total distance covered by AGVs, and the distance traveled while empty-loaded. The improved hybrid algorithm combines the improved genetic algorithm (GA) and the simulated annealing algorithm (SA) to strengthen the local search ability of the algorithm and improve the stability of the calculation results. Based on the characteristics of the composite operation mode, the authors introduce the combined coding and parallel decoding… More >

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    ARTICLE

    Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

    Zheng Shi, Wanru Song*, Junhao Shan, Feng Liu
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849
    Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature… More >

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    ARTICLE

    Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

    Ameerah Abdullah Alshahrani*, Emad Sami Jaha, Nahed Alowidi
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3489-3509, 2023, DOI:10.32604/cmc.2023.044490
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various real-life applications, such as helping crime investigators… More >

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    ARTICLE

    Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

    Ning Li*, Shucai Huang, Daozhi Wei
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987
    (This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image. Besides, an improved… More >

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    ARTICLE

    Smart MobiNet: A Deep Learning Approach for Accurate Skin Cancer Diagnosis

    Muhammad Suleman1, Faizan Ullah1, Ghadah Aldehim2,*, Dilawar Shah1, Mohammad Abrar1,3, Asma Irshad4, Sarra Ayouni2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365
    (This article belongs to this Special Issue: Advances, Challenges, and Opportunities of IoT-Based Big Data in Healthcare Industry 4.0)
    Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from the International Skin Imaging Collaboration,… More >

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    ARTICLE

    FIR-YOLACT: Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

    Wen Dong1, Ziyan Liu1,2,*, Mo Yang1, Ying Wu1
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3551-3572, 2023, DOI:10.32604/cmc.2023.044967
    Abstract Autonomous driving technology has made a lot of outstanding achievements with deep learning, and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems. The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information, which is more accurate and reliable than object detection. However, the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed. Therefore, this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT, which fuses the ICIoU (Improved Complete Intersection over Union) and Res2Net for the YOLACT algorithm. Specifically, the… More >

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    ARTICLE

    Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

    Ming Wan1, Minglei Hao1, Jiawei Li1, Jiangyuan Yao2,*, Yan Song3
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857
    (This article belongs to this Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
    Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due… More >

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    ARTICLE

    Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

    Shaik Thaseentaj, S. Sudhakar Ilango*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3593-3618, 2023, DOI:10.32604/cmc.2023.042496
    Abstract The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. The management and prevention of these diseases depend mainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery Mildew, and Leaf Blight. To… More >

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    ARTICLE

    Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

    Ye-Yeon Kang1, Geon Park1, Hyun Yoo2, Kyungyong Chung1,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566
    Abstract Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts to determine the same object… More >

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    ARTICLE

    Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

    Derwin Suhartono1,*, Alif Tri Handoyo1, Franz Adeta Junior2
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3637-3657, 2023, DOI:10.32604/cmc.2023.045301
    Abstract Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication. This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network (GAN) based augmentation on diverse datasets, including iSarcasm, SemEval-18, and Ghosh. This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network (RGAN). The proposed RGAN method works by inverting labels between original and synthetic data during the training process. This inversion of labels provides feedback to the generator for generating… More >

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    ARTICLE

    Nonlinear Components of a Block Cipher over Eisenstein Integers

    Mohammad Mazyad Hazzazi1, Muhammad Sajjad2, Zaid Bassfar3, Tariq Shah2,*, Ashwag Albakri4
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3659-3675, 2023, DOI:10.32604/cmc.2023.039013
    Abstract In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose to induce confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves, chaotic maps, and Gaussian integers has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers (EI). The fascinating features of this structure provide S-boxes pair at a time by fixing… More >

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    ARTICLE

    Analysis on Operational Safety and Efficiency of FAO System in Urban Rail Transit

    Kaige Guo, Jin Zhou*, Xiaoming Zhang, Di Sun*, Zishuo Wang, Lixian Zhao
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3677-3696, 2023, DOI:10.32604/cmc.2023.038660
    Abstract This paper discusses two urgent problems that need to be solved in fully automatic operation (FAO) for urban rail transit. The first is the analysis of safety in FAO, while another is the analysis of efficiency in FAO. Firstly, this paper establishes an operational safety evaluation index system from the perspective of operation for the unique or typical risk sources of the FAO system, and uses the analytic hierarchy process (AHP) to evaluate the indicators, analyzes various factors that affect the safe operation of FAO, and provides safety management recommendations for FAO lines operation to maintain the FAO system specifically.… More >

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    ARTICLE

    YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

    Fares Alharbi1, Reem Alshahrani2, Mohammed Zakariah3,*, Amjad Aldweesh1, Abdulrahman Abdullah Alghamdi1
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3697-3722, 2023, DOI:10.32604/cmc.2023.040086
    (This article belongs to this Special Issue: Innovations in Pervasive Computing and Communication Technologies)
    Abstract Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing the code, there are still… More >

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    ARTICLE

    Optical Neural Networks: Analysis and Prospects for 5G Applications

    Doaa Sami Khafaga1, Zongming Lv2, Imran Khan3,4, Shebnam M. Sefat5, Amel Ali Alhussan1,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3723-3740, 2023, DOI:10.32604/cmc.2023.039956
    (This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)
    Abstract With the capacities of self-learning, acquainted capacities, high-speed looking for ideal arrangements, solid nonlinear fitting, and mapping self-assertively complex nonlinear relations, neural systems have made incredible advances and accomplished broad application over the final half-century. As one of the foremost conspicuous methods for fake insights, neural systems are growing toward high computational speed and moo control utilization. Due to the inborn impediments of electronic gadgets, it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage. Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the bottleneck. This… More >

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    ARTICLE

    Learning Dual-Domain Calibration and Distance-Driven Correlation Filter: A Probabilistic Perspective for UAV Tracking

    Taiyu Yan1, Yuxin Cao1, Guoxia Xu1, Xiaoran Zhao2, Hu Zhu1, Lizhen Deng3,*
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3741-3764, 2023, DOI:10.32604/cmc.2023.039828
    (This article belongs to this Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
    Abstract Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence correlation filter (DJSCF) model address… More >

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