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Vijey Thayananthan¹, Aiiad Albeshri², Hassan A. Alamri³, Muhammad Bilal Qureshi⁴, Muhammad Shuaib Qureshi^5,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2573-2595, 2023, DOI:10.32604/cmc.2023.040184 - 08 October 2023
（This article belongs to the Special Issue: Applications of Digital Twins in Intelligent Healthcare Systems)
Abstract Complex networks on the Internet of Things (IoT) and brain communication are the main focus of this paper. The benefits of complex networks may be applicable in the future research directions of 6G, photonic, IoT, brain, etc., communication technologies. Heavy data traffic, huge capacity, minimal level of dynamic latency, etc. are some of the future requirements in 5G+ and 6G communication systems. In emerging communication, technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain communication. In this paper, the state of the… More >

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617

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Xiulai Li^1,2,3,4, Jieren Cheng^1,3,*, Zhaoxin Shi^2,3, Jingxin Liu^2,3, Bin Zhang^1,3, Xinbing Xu^2,3, Xiangyan Tang^1,3, Victor S. Sheng⁵
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2597-2629, 2023, DOI:10.32604/cmc.2023.040596 - 08 October 2023
Abstract As a distributed database, the system security of the blockchain is of great significance to prevent tampering, protect privacy, prevent double spending, and improve credibility. Due to the decentralized and trustless nature of blockchain, the security defense of the blockchain system has become one of the most important measures. This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense, and we first introduce the overview, classification, and threat assessment process of blockchain security threats. Then, we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize More >

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603

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Kaleem Arshid^1,*, Jianbiao Zhang¹, Muhammad Yaqub¹, Mohammad Daud Awan², Habiba Ijaz³, Imran Shabir Chuhan⁴
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2631-2647, 2023, DOI:10.32604/cmc.2023.035735 - 08 October 2023
Abstract Channel assignment has emerged as an essential study subject in Cognitive Radio-based Wireless Mesh Networks (CR-WMN). In an era of alarming increase in Multi-Radio Multi-Channel (MRMC) network expansion interference is decreased and network throughput is significantly increased when non-overlapping or partially overlapping channels are correctly integrated. Because of its ad hoc behavior, dynamic channel assignment outperforms static channel assignment. Interference reduces network throughput in the CR-WMN. As a result, there is an extensive research gap for an algorithm that dynamically distributes channels while accounting for all types of interference. This work presents a method for More >

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Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891 - 08 October 2023
Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method… More >

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473

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Inzamam Mashood Nasir^1,*, Mudassar Raza¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Yun-Cheol Nam³, Yunyoung Nam^4,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2667-2684, 2023, DOI:10.32604/cmc.2023.035214 - 08 October 2023
（This article belongs to the Special Issue: Recent Advances in Hyper Parameters Optimization, Features Optimization, and Deep Learning for Video Surveillance and Biometric Applications)
Abstract Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted… More >

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525

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ARTICLE

Huaixi Xing^*, Qinghua Xing
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2685-2705, 2023, DOI:10.32604/cmc.2023.036223 - 08 October 2023
（This article belongs to the Special Issue: Optimization Algorithm in Real-World Applications)
Abstract With the advancement of combat equipment technology and combat concepts, new requirements have been put forward for air defense operations during a group target attack. To achieve high-efficiency and low-loss defensive operations, a reasonable air defense weapon assignment strategy is a key step. In this paper, a multi-objective and multi-constraints weapon target assignment (WTA) model is established that aims to minimize the defensive resource loss, minimize total weapon consumption, and minimize the target residual effectiveness. An optimization framework of air defense weapon mission scheduling based on the multi-objective artificial bee colony (MOABC) algorithm is proposed.… More >

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477

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Yalong Xie¹, Aiping Li^1,*, Biyin Hu², Liqun Gao¹, Hongkui Tu¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039 - 08 October 2023
Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature… More >

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663

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Ramiz Gorkem Birdal^*, Ahmet Sertbas
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069 - 08 October 2023
Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal… More >

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523

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367

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ARTICLE

Shangting Miao¹, Yang Li^2,*, Quan Pan²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2745-2762, 2023, DOI:10.32604/cmc.2023.037257 - 08 October 2023
Abstract A space called Unmanned Aerial Vehicle (UAV) cyber is a new environment where UAV, Ground Control Station (GCS) and business processes are integrated. Denial of service (DoS) attack is a standard network attack method, especially suitable for attacking the UAV cyber. It is a robust security risk for UAV cyber and has recently become an active research area. Game theory is typically used to simulate the existing offensive and defensive mechanisms for DoS attacks in a traditional network. In addition, the honeypot, an effective security vulnerability defense mechanism, has not been widely adopted or modeled… More >

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710

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411

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Shuo Cai¹, Tingyu Luo¹, Fei Yu^1,*, Pradip Kumar Sharma², Weizheng Wang¹, Lairong Yin³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2763-2777, 2023, DOI:10.32604/cmc.2023.037825 - 08 October 2023
Abstract In the Internet of Things (IoT) system, relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency. In Body Sensor Network (BSN) systems, biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency. When the relay node fails, the biosensor can communicate directly with the receiving device by releasing more transmitting power. However, if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device, the biosensor will be isolated by the system. Therefore, More >

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349

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ARTICLE

Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860 - 08 October 2023
（This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized… More >

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401

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ARTICLE

Gaoyuan Yu¹, Yunfeng Lou², Hang Dong³, Junjie Li¹, Xianlong Jin^1,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2795-2816, 2023, DOI:10.32604/cmc.2023.037375 - 08 October 2023
Abstract The strict and high-standard requirements for the safety and stability of major engineering systems make it a tough challenge for large-scale finite element modal analysis. At the same time, realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice. This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis. Based on two-level partitioning and four-transformation strategies, the proposed algorithm not only improves… More >

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352

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ARTICLE

Zeliang An¹, Tianqi Zhang^1,*, Debang Liu¹, Yuqing Xu², Gert Frølund Pedersen², Ming Shen²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2817-2834, 2023, DOI:10.32604/cmc.2023.037832 - 08 October 2023
Abstract With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle… More >

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566

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387

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ARTICLE

Hao Li, Jun Tang^*, Qingtao Pan, Jianjun Zhan, Songyang Lao
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2835-2859, 2023, DOI:10.32604/cmc.2023.038670 - 08 October 2023
Abstract No optimization algorithm can obtain satisfactory results in all optimization tasks. Thus, it is an effective way to deal with the problem by an ensemble of multiple algorithms. This paper proposes an ensemble of population-based metaheuristics (EPM) to solve single-objective optimization problems. The design of the EPM framework includes three stages: the initial stage, the update stage, and the final stage. The framework applies the transformation of the real and virtual population to balance the problem of exploration and exploitation at the population level and uses an elite strategy to communicate among virtual populations. The… More >

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408

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ARTICLE

Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2861-2877, 2023, DOI:10.32604/cmc.2023.039181 - 08 October 2023
（This article belongs to the Special Issue: Cancer Diagnosis using Deep Learning, Federated Learning, and Features Optimization Techniques)
Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A… More >

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384

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ARTICLE

Shengyao Sun^1,2, Ying Du³, Jiajun Chen⁴, Xuan Zhang⁵, Jiwei Zhang^6,*, Yiyi Xu⁷
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2879-2900, 2023, DOI:10.32604/cmc.2023.037483 - 08 October 2023
（This article belongs to the Special Issue: Innovations in Pervasive Computing and Communication Technologies)
Abstract In Multi-access Edge Computing (MEC), to deal with multiple user equipment (UE)’s task offloading problem of parallel relationships under the multi-constraints, this paper proposes a cooperation partial task offloading method (named CPMM), aiming to reduce UE's energy and computation consumption, while meeting the task completion delay as much as possible. CPMM first studies the task offloading of single-UE and then considers the task offloading of multi-UE based on single-UE task offloading. CPMM uses the critical path algorithm to divide the modules into key and non-key modules. According to some constraints of UE-self when offloading tasks,… More >

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350

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ARTICLE

Dehai Zhang^*, Yu Ma, Qing Liu, Haoxing Wang, Anquan Ren, Jiashu Liang
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2901-2920, 2023, DOI:10.32604/cmc.2023.038264 - 08 October 2023
（This article belongs to the Special Issue: Transport Resilience and Emergency Management in the Era of Artificial Intelligence)
Abstract Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn… More >

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341

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Chengyu Mo^1,2, Fenlin Liu^1,2, Ma Zhu^1,2,*, Gengcong Yan³, Baojun Qi^1,2, Chunfang Yang^1,2
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540 - 08 October 2023
Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the… More >

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Kang-Moon Seo¹, Jane Kim¹, Soojin Lee¹, Jun-Woo Kwon¹, Seung-Hyun Seo^1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2937-2958, 2023, DOI:10.32604/cmc.2023.039459 - 08 October 2023
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract With the advancement of unmanned aerial vehicle (UAV) technology, the market for drones and the cooperation of many drones are expanding. Drone swarms move together in multiple regions to perform their tasks. A Ground Control Server (GCS) located in each region identifies drone swarm members to prevent unauthorized drones from trespassing. Studies on drone identification have been actively conducted, but existing studies did not consider multiple drone identification environments. Thus, developing a secure and effective identification mechanism for drone swarms is necessary. We suggested a novel approach for the remote identification of drone swarms. For… More >

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518

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Muhammad Firdaus¹, Harashta Tatimma Larasati², Kyung-Hyune Rhee^3,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2959-2978, 2023, DOI:10.32604/cmc.2023.039487 - 08 October 2023
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks… More >

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Tahir Sher¹, Abdul Rehman², Dongsun Kim^2,*, Imran Ihsan¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2979-2995, 2023, DOI:10.32604/cmc.2023.036553 - 08 October 2023
（This article belongs to the Special Issue: Data Science for the Internet of Things)
Abstract The rise or fall of the stock markets directly affects investors’ interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and… More >

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Xinliang Tang¹, Xiaotong Ru¹, Jingfang Su^1,*, Gabriel Adonis²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923 - 08 October 2023
Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for… More >

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476

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Yang Liu^1,3, Jiabo Wang^1,2,*, Qinbo Liu¹, Mehdi Gheisari¹, Wanyin Xu¹, Zoe L. Jiang¹, Jiajia Zhang^1,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3013-3027, 2023, DOI:10.32604/cmc.2023.039452 - 08 October 2023
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaboratively without sharing raw data. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy… More >

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Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838 - 08 October 2023
（This article belongs to the Special Issue: Cancer Diagnosis using Deep Learning, Federated Learning, and Features Optimization Techniques)
Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the… More >

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368

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Yinxue Ao¹, Jian Lv^1,*, Qingsheng Xie¹, Zhengming Zhang²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3049-3074, 2023, DOI:10.32604/cmc.2023.040088 - 08 October 2023
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation (DNSGA-II) strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product. First, the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users; then, the product multidimensional scale analysis is applied to classify the research objects, and again the reference samples are screened by the semantic differential method, and the samples are parametrized in two dimensions by using elliptic Fourier analysis; finally, the… More >

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Raheem Ogla^1,*, Eman Shakar Mahmood¹, Rasha I. Ahmed¹, Abdul Monem S. Rahma²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3075-3096, 2023, DOI:10.32604/cmc.2023.039818 - 08 October 2023
Abstract The transmission of video content over a network raises various issues relating to copyright authenticity, ethics, legality, and privacy. The protection of copyrighted video content is a significant issue in the video industry, and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media. However, there are still many unresolved challenges. This paper aims to address those challenges by proposing a new technique for detecting moving objects in digital videos, which can help prove the credibility of video content by detecting any fake… More >

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Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914 - 08 October 2023
Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model… More >

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Zhiruo Zhang, Feng Wang^*, Yang Liu, Yang Lu, Xinlei Liu
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3113-3129, 2023, DOI:10.32604/cmc.2023.040600 - 08 October 2023
Abstract The consensus protocol is one of the core technologies in blockchain, which plays a crucial role in ensuring the block generation rate, consistency, and safety of the blockchain system. Blockchain systems mainly adopt the Byzantine Fault Tolerance (BFT) protocol, which often suffers from slow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus. In this paper, this paper proposes a new dual-mode consensus protocol based on node identity authentication. It divides the consensus process into two subprotocols: Check_BFT and Fast_BFT. In Check_BFT, the replicas authenticate the primary’s identity by monitoring… More >

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Arif Hussain Magsi¹, Ghulam Muhammad^2,*, Sajida Karim³, Saifullah Memon¹, Zulfiqar Ali⁴
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3131-3150, 2023, DOI:10.32604/cmc.2023.040784 - 08 October 2023
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Recent advancements in the Vehicular Ad-hoc Network (VANET) have tremendously addressed road-related challenges. Specifically, Named Data Networking (NDN) in VANET has emerged as a vital technology due to its outstanding features. However, the NDN communication framework fails to address two important issues. The current NDN employs a pull-based content retrieval network, which is inefficient in disseminating crucial content in Vehicular Named Data Networking (VNDN). Additionally, VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles. Although various solutions have been proposed for detecting vehicles’ behavior, they inadequately addressed the challenges specific… More >

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Awny Sayed¹, Sohair Kinlany², Alaa Zaki², Ahmed Mahfouz^2,3,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.040256 - 08 October 2023
Abstract Face verification systems are critical in a wide range of applications, such as security systems and biometric authentication. However, these systems are vulnerable to adversarial attacks, which can significantly compromise their accuracy and reliability. Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images. These perturbations can be imperceptible to the human eye but can cause the system to misclassify or fail to recognize the person in the image. To address this issue, we propose a novel system called VeriFace that comprises two defense mechanisms, adversarial detection,… More >

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Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721 - 08 October 2023
（This article belongs to the Special Issue: AI-driven Cybersecurity in Cyber Physical Systems enabled Healthcare, Current Challenges, Requirements and Future research Foresights)
Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to… More >

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Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3189-3218, 2023, DOI:10.32604/cmc.2023.039466 - 08 October 2023
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms… More >

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Zhenyu Huang^1,2, Gun Li², Xudong Sun¹, Yong Chen¹, Jie Sun¹, Zhangsong Ni^1,*, Yang Yang^1,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3219-3238, 2023, DOI:10.32604/cmc.2023.039489 - 08 October 2023
Abstract Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation… More >

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Hui Xu, Yalin Hu^*, Weidong Cao, Longjie Han
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3239-3255, 2023, DOI:10.32604/cmc.2023.039227 - 08 October 2023
（This article belongs to the Special Issue: Evolving Network Traffic Identification Technology)
Abstract The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex. Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks. Recently, machine learning has been widely applied to network traffic recognition. Still, high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms. Taking advantage of the faster optimization-seeking capability of the jumping spider optimization algorithm (JSOA), this paper proposes a jumping spider optimization algorithm that incorporates the… More >

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Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Xianyu Wang², Uzair Aslam Bhatti¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3257-3274, 2023, DOI:10.32604/cmc.2023.039430 - 08 October 2023
（This article belongs to the Special Issue: Evolving Network Traffic Identification Technology)
Abstract To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space… More >

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Muhammad Naeem Akbar^1,*, Seemab Khan², Muhammad Umar Farooq¹, Majed Alhaisoni³, Usman Tariq⁴, Muhammad Usman Akram¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289 - 08 October 2023
（This article belongs to the Special Issue: Recent Advances in Hyper Parameters Optimization, Features Optimization, and Deep Learning for Video Surveillance and Biometric Applications)
Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of… More >

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Weizhi Liao¹, Xiaoyun Xia^1,3, Xiaojun Jia¹, Shigen Shen^2,*, Helin Zhuang^4,*, Xianchao Zhang¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3297-3323, 2023, DOI:10.32604/cmc.2023.040967 - 08 October 2023
Abstract As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO³) is developed. A position updating method based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) and Global Leader Phase (GLP) is… More >

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Peng Liu¹, Qian He^1,*, Baokang Zhao², Biao Guo¹, Zhongyi Zhai¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3325-3343, 2023, DOI:10.32604/cmc.2023.041167 - 08 October 2023
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT. However, data privacy is potentially risky when data is outsourced to cloud servers or edge services. While data encryption ensures data confidentiality, it can impede data sharing and retrieval. Attribute-based searchable encryption (ABSE) is proposed as an effective technique for enhancing data security and privacy. Nevertheless, ABSE has its limitations, such as single attribute authorization failure, privacy leakage during the search process, and high decryption overhead. This paper presents a novel approach called… More >

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Asif Khan¹, Huaping Zhang^1,*, Nada Boudjellal², Arshad Ahmad³, Maqbool Khan³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520 - 08 October 2023
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets… More >

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Hong Zhu^*, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3363-3377, 2023, DOI:10.32604/cmc.2023.040731 - 08 October 2023
Abstract Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates… More >

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Shaohua Li, Haixiang Zhang^*, Hanjie Ma, Jie Feng, Mingfeng Jiang
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3379-3392, 2023, DOI:10.32604/cmc.2023.041538 - 08 October 2023
Abstract Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The More >

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Wenran Jia¹, Simin Ma¹, Peng Geng¹, Yan Sun^2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3393-3411, 2023, DOI:10.32604/cmc.2023.040091 - 08 October 2023
（This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
Abstract Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a… More >

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Seyoung Lee¹, Wonsuk Choi¹, Insup Kim², Ganggyu Lee², Dong Hoon Lee^1,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583 - 08 October 2023
（This article belongs to the Special Issue: Advances in Information Security Application)
Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These… More >

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Ala Kana, Imtiaz Ahmad^*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3443-3464, 2023, DOI:10.32604/cmc.2023.040775 - 08 October 2023
Abstract A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout… More >

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Yu-Rim Lee¹, Na-Eun Park¹, Seo-Yi Kim², Il-Gu Lee^1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3465-3482, 2023, DOI:10.32604/cmc.2023.041196 - 08 October 2023
（This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Abstract With the introduction of 5G technology, the application of Internet of Things (IoT) devices is expanding to various industrial fields. However, introducing a robust, lightweight, low-cost, and low-power security solution to the IoT environment is challenging. Therefore, this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment. The first method, compressed sensing and learning (CSL), compresses an event log in a bitmap format to quickly detect attacks. Then, the attack log is detected using a machine-learning classification model. The second method, precise re-learning… More >

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Asma A. Alhashmi¹, Abdulbasit A. Darem^1,*, Sultan M. Alanazi¹, Abdullah M. Alashjaee², Bader Aldughayfiq³, Fuad A. Ghaleb^4,5, Shouki A. Ebad¹, Majed A. Alanazi¹
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3483-3498, 2023, DOI:10.32604/cmc.2023.041038 - 08 October 2023
Abstract In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model’s design capitalizes on the benefits of both static and dynamic analysis to extract… More >

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Tengda Li, Gang Wang, Qiang Fu^*, Xiangke Guo, Minrui Zhao, Xiangyu Liu
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253 - 08 October 2023
Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the… More >

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Fida Mohammad^1,*, Mukhtaj Khan¹, Safdar Nawaz Khan Marwat², Naveed Jan³, Neelam Gohar⁴, Muhammad Bilal³, Amal Al-Rasheed⁵
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202 - 08 October 2023
（This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep… More >

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Bahera H. Nayef^1,*, Siti Norul Huda Sheikh Abdullah², Rossilawati Sulaiman², Ashwaq Mukred Saeed³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3549-3567, 2023, DOI:10.32604/cmc.2023.039528 - 08 October 2023
Abstract Text extraction from images using the traditional techniques of image collecting, and pattern recognition using machine learning consume time due to the amount of extracted features from the images. Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results. This study proposes using Dual Maxpooling and concatenating convolution Neural Networks (CNN) layers with the activation functions Relu and the Optimized Leaky Relu (OLRelu). The proposed method works by dividing the word image into slices that contain characters.… More >

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Jiayi Ma, Tianhao Sun^*, Xiaodong Zhang
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3569-3584, 2023, DOI:10.32604/cmc.2023.040005 - 08 October 2023
（This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
Abstract Sequential recommendation based on a multi-interest framework aims to analyze different aspects of interest based on historical interactions and generate predictions of a user’s potential interest in a list of items. Most existing methods only focus on what are the multiple interests behind interactions but neglect the evolution of user interests over time. To explore the impact of temporal dynamics on interest extraction, this paper explicitly models the timestamp with a multi-interest network and proposes a time-highlighted network to learn user preferences, which considers not only the interests at different moments but also the possible… More >

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