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Muhammad Abdullah Khan¹, Muhammad Usman², Yuhong Zhao^1,3,4,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 1905-1952, 2024, DOI:10.32604/cmc.2024.054691 - 18 November 2024
（This article belongs to the Special Issue: Optimization Design for Material Microstructures)
Abstract This comprehensive review examines the structural, mechanical, electronic, and thermodynamic properties of Mg-Li-Al alloys, focusing on their corrosion resistance and mechanical performance enhancement. Utilizing first-principles calculations based on Density Functional Theory (DFT) and the quasi-harmonic approximation (QHA), the combined properties of the Mg-Li-Al phase are explored, revealing superior incompressibility, shear resistance, and stiffness compared to individual elements. The review highlights the brittleness of the alloy, supported by B/G ratios, Cauchy pressures, and Poisson’s ratios. Electronic structure analysis shows metallic behavior with varied covalent bonding characteristics, while Mulliken population analysis emphasizes significant electron transfer within the… More >

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133

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Jingbo Zhang¹, Qiong Wu^1,*, Pingyi Fan², Qiang Fan³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 1953-1998, 2024, DOI:10.32604/cmc.2024.057006 - 18 November 2024
Abstract Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user’s data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We… More >

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66

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Altaf Hussain¹, Shuaiyong Li², Tariq Hussain³, Xianxuan Lin^4,*, Farman Ali^5,*, Ahmad Ali AlZubi⁶
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 1999-2051, 2024, DOI:10.32604/cmc.2024.056183 - 18 November 2024
Abstract Devices and networks constantly upgrade, leading to rapid technological evolution. Three-dimensional (3D) point cloud transmission plays a crucial role in aerial computing terminology, facilitating information exchange. Various network types, including sensor networks and 5G mobile networks, support this transmission. Notably, Flying Ad hoc Networks (FANETs) utilize Unmanned Aerial Vehicles (UAVs) as nodes, operating in a 3D environment with Six Degrees of Freedom (6DoF). This study comprehensively surveys UAV networks, focusing on models for Light Detection and Ranging (LiDAR) 3D point cloud compression/transmission. Key topics covered include autonomous navigation, challenges in video streaming infrastructure, motivations for More >

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Monirah Al-Ajlan^*, Mourad Ykhlef
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024
Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

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40

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Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024
（This article belongs to the Special Issue: Advances in Pattern Recognition Applications)
Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

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77

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Mitra Madanchian^1,*, Hamed Taherdoost^1,2,3,4
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024
（This article belongs to the Special Issue: AI and Advanced High-Tech Research and Development)
Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

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124

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Mujahid Ali^*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024
Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

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83

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ARTICLE

Ting Chen¹, Shuna Jiang², Xin Fan^3,*, Jianchuan Xia², Xiujuan Zhang², Chuanwen Luo³, Yi Hong³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2195-2217, 2024, DOI:10.32604/cmc.2024.056960 - 18 November 2024
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract In blockchain-based unmanned aerial vehicle (UAV) communication systems, the length of a block affects the performance of the blockchain. The transmission performance of blocks in the form of finite character segments is also affected by the block length. Therefore, it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems, especially in wireless environments involving UAVs. This paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during transmission. In our scheme, using a friendly jamming UAV… More >

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53

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ARTICLE

Jian Su¹, Haijian Shao^1,2,*, Xing Deng¹, Yingtao Jiang²
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2219-2242, 2024, DOI:10.32604/cmc.2024.057604 - 18 November 2024
Abstract The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems. This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain, which reduces the model’s depth and accelerates activity inference. Unlike traditional pruning methods that focus on the spatial domain and the importance of filters, this… More >

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Hina Bhanbhro^1,*, Yew Kwang Hooi¹, Mohammad Nordin Bin Zakaria¹, Worapan Kusakunniran², Zaira Hassan Amur¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2243-2259, 2024, DOI:10.32604/cmc.2024.052138 - 18 November 2024
Abstract Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to… More >

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49

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ARTICLE

Haemin Jung¹, Heesung Park², Hae Sun Jung³, Kwangyon Lee^4,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2261-2279, 2024, DOI:10.32604/cmc.2024.057118 - 18 November 2024
Abstract The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to… More >

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49

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Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024
Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

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Bassam Mohammad Elzaghmouri¹, Yosef Hasan Fayez Jbara², Said Elaiwat³, Nisreen Innab^4,*, Ahmed Abdelgader Fadol Osman⁵, Mohammed Awad Mohammed Ataelfadiel⁵, Farah H. Zawaideh⁶, Mouiad Fadeil Alawneh⁷, Asef Al-Khateeb⁸, Marwan Abu-Zanona⁸
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836 - 18 November 2024
Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >

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79

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Xiaojun Jin¹, Chao Ma^2,*, Song Luo², Pengyi Zeng¹, Yifei Wei¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2317-2336, 2024, DOI:10.32604/cmc.2024.055344 - 18 November 2024
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be… More >

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ARTICLE

Van-Truong Nguyen^1,*, Dai-Nhan Duong¹, Dinh-Hieu Phan¹, Thanh-Lam Bui¹, Xiem HoangVan², Phan Xuan Tan³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2337-2356, 2024, DOI:10.32604/cmc.2024.055412 - 18 November 2024
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract This paper proposes an adaptive nonlinear proportional-derivative (ANPD) controller for a two-wheeled self-balancing robot (TWSB) modeled by the Lagrange equation with external forces. The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative (NPD) controller and a genetic algorithm, in which the proportional-derivative (PD) parameters are updated online based on the tracking error and the preset error threshold. In addition, the genetic algorithm is employed to adaptively select initial controller parameters, contributing to system stability and improved control accuracy. The proposed controller is basic in design yet simple to implement. The… More >

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Altaf Hussain¹, Shuaiyong Li², Tariq Hussain³, Razaz Waheeb Attar⁴, Farman Ali^5,*, Ahmed Alhomoud⁶, Babar Shah⁷
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2357-2394, 2024, DOI:10.32604/cmc.2024.056694 - 18 November 2024
Abstract Ad hoc networks offer promising applications due to their ease of use, installation, and deployment, as they do not require a centralized control entity. In these networks, nodes function as senders, receivers, and routers. One such network is the Flying Ad hoc Network (FANET), where nodes operate in three dimensions (3D) using Unmanned Aerial Vehicles (UAVs) that are remotely controlled. With the integration of the Internet of Things (IoT), these nodes form an IoT-enabled network called the Internet of UAVs (IoU). However, the airborne nodes in FANET consume high energy due to their payloads and… More >

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74

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ARTICLE

Deema Alsekait¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2395-2436, 2024, DOI:10.32604/cmc.2024.055469 - 18 November 2024
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract The widespread adoption of Internet of Things (IoT) devices has resulted in notable progress in different fields, improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks. Further, the study suggests using an advanced approach that utilizes machine learning, specifically the Wide Residual Network (WRN), to identify hidden malware in IoT systems. The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices, using the MalMemAnalysis dataset. Moreover, thorough experimentation provides evidence for the effectiveness of the WRN-based strategy, resulting in… More >

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Bing Shi^1,*, Jianhua Zhao¹, Bin Ma¹, Juan Huan², Yueping Sun³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2437-2456, 2024, DOI:10.32604/cmc.2024.056377 - 18 November 2024
Abstract Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture. Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses. To address this issue, an improved algorithm based on the You Only Look Once v5s (YOLOv5s) lightweight model has been proposed. This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module (CBAM) to achieve high recognition accuracy. Furthermore, the model introduces the α-SIoU loss function, which combines the α-Intersection over Union (α-IoU) and… More >

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Yucheng Liang^1,2,*, Ke Niu^1,2,*, Yingnan Zhang^1,2, Yifei Meng^1,2, Fangmeng Hu^1,2
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2457-2492, 2024, DOI:10.32604/cmc.2024.056438 - 18 November 2024
Abstract To address the challenges of video copyright protection and ensure the perfect recovery of original video, we propose a dual-domain watermarking scheme for digital video, inspired by Robust Reversible Watermarking (RRW) technology used in digital images. Our approach introduces a parameter optimization strategy that incrementally adjusts scheme parameters through attack simulation fitting, allowing for adaptive tuning of experimental parameters. In this scheme, the low-frequency Polar Harmonic Transform (PHT) moment is utilized as the embedding domain for robust watermarking, enhancing stability against simulation attacks while implementing the parameter optimization strategy. Through extensive attack simulations across various… More >

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Sameema Tariq¹, Ata-Ur- Rehman^2,3, Maria Abubakar², Waseem Iqbal⁴, Hatoon S. Alsagri⁵, Yousef A. Alduraywish⁵, Haya Abdullah A. Alhakbani^5,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024
Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >

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81

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Wenming Wang^1,2,3,*, Zhiquan Liu¹, Shumin Zhang¹, Guijiang Liu¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2517-2545, 2024, DOI:10.32604/cmc.2024.055184 - 18 November 2024
Abstract Advancements in the vehicular network technology enable real-time interconnection, data sharing, and intelligent cooperative driving among vehicles. However, malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users. Trust mechanisms serve as an effective solution to this issue. In recent years, many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes, incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area. In this paper, we propose a distributed vehicular network scheme based on trust scores. Specifically, the designed architecture partitions multiple More >

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ARTICLE

Gajjala Savithri^1,2, N. Raghavendra Sai^1,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2547-2569, 2024, DOI:10.32604/cmc.2024.055827 - 18 November 2024
Abstract In the world of wireless sensor networks (WSNs), optimizing performance and extending network lifetime are critical goals. In this paper, we propose a new model called DTLR-Net (Deep Temporal LSTM Regression Network) that employs long-short-term memory and is effective for long-term dependencies. Mobile sinks can move in arbitrary patterns, so the model employs long short-term memory (LSTM) networks to handle such movements. The parameters were initialized iteratively, and each node updated its position, mobility level, and other important metrics at each turn, with key measurements including active or inactive node ratio, energy consumption per cycle,… More >

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Keke Zhou, Guoqiang Zheng^*, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863 - 18 November 2024
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >

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Mehwash Farooqui¹, Atta Rahman^2,*, Latifa Alsuliman¹, Zainab Alsaif¹, Fatimah Albaik¹, Cadi Alshammari¹, Razan Sharaf¹, Sunday Olatunji¹, Sara Waslallah Althubaiti¹, Hina Gull³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2587-2605, 2024, DOI:10.32604/cmc.2024.055262 - 18 November 2024
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract The proposed study focuses on the critical issue of corrosion, which leads to significant economic losses and safety risks worldwide. A key area of emphasis is the accuracy of corrosion detection methods. While recent studies have made progress, a common challenge is the low accuracy of existing detection models. These models often struggle to reliably identify corrosion tendencies, which are crucial for minimizing industrial risks and optimizing resource use. The proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network (CNN), as well as two pretrained… More >

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Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024
Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… More >

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Chen Chen, Kai Qiao, Jie Yang, Jian Chen, Bin Yan^*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2629-2642, 2024, DOI:10.32604/cmc.2024.056038 - 18 November 2024
Abstract Computed Tomography (CT) is a commonly used technology in Printed Circuit Boards (PCB) non-destructive testing, and element segmentation of CT images is a key subsequent step. With the development of deep learning, researchers began to exploit the “pre-training and fine-tuning” training process for multi-element segmentation, reducing the time spent on manual annotation. However, the existing element segmentation model only focuses on the overall accuracy at the pixel level, ignoring whether the element connectivity relationship can be correctly identified. To this end, this paper proposes a PCB CT image element segmentation model optimizing the semantic perception… More >

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Rongwei Yu, Xiya Guo^*, Peihao Zhang, Kaijuan Zhang
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2643-2664, 2024, DOI:10.32604/cmc.2024.056165 - 18 November 2024
Abstract Encrypted traffic plays a crucial role in safeguarding network security and user privacy. However, encrypting malicious traffic can lead to numerous security issues, making the effective classification of encrypted traffic essential. Existing methods for detecting encrypted traffic face two significant challenges. First, relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic. Second, machine learning and convolutional neural network methods lack sufficient network expression capabilities, hindering the full exploration of traffic’s potential characteristics. To address these limitations, this study introduces a traffic classification method that utilizes… More >

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Kusum Yadav¹, Yasser Alharbi¹, Eissa Jaber Alreshidi¹, Abdulrahman Alreshidi¹, Anuj Kumar Jain², Anurag Jain³, Kamal Kumar⁴, Sachin Sharma⁵, Brij B. Gupta^6,7,8,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2665-2683, 2024, DOI:10.32604/cmc.2024.053565 - 18 November 2024
（This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract In today’s world, image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images. Automated analysis of medical images is essential for doctors, as manual investigation often leads to inter-observer variability. This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework. The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization (MIWPSO) and Fuzzy C-Means clustering (FCM) algorithms. Traditional FCM does not incorporate spatial neighborhood features, making More >

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Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024
Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

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Jinlin Xu^1,2, Wansu Pan^1,*, Haibo Tan^1,2, Longle Cheng¹, Xiaofeng Li^1,2
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2709-2726, 2024, DOI:10.32604/cmc.2024.056925 - 18 November 2024
Abstract The traffic within data centers exhibits bursts and unpredictable patterns. This rapid growth in network traffic has two consequences: it surpasses the inherent capacity of the network’s link bandwidth and creates an imbalanced network load. Consequently, persistent overload situations eventually result in network congestion. The Software Defined Network (SDN) technology is employed in data centers as a network architecture to enhance performance. This paper introduces an adaptive congestion control strategy, named DA-DCTCP, for SDN-based Data Centers. It incorporates Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) to establish congestion awareness and an ECN marking model.… More >

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Ahamad B. Alkodre¹, Nour Mahmoud Bahbouh², Sandra Sendra³, Adnan Ahmed Abi Sen^4,*, Yazed Alsaawy¹, Saad Said Alqahtany¹, Abdallah Namoun¹, Hani Almoamari¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2727-2751, 2024, DOI:10.32604/cmc.2024.053706 - 18 November 2024
Abstract In Saudi Arabia, drones are increasingly used in different sensitive domains like military, health, and agriculture to name a few. Typically, drone cameras capture aerial images of objects and convert them into crucial data, alongside collecting data from distributed sensors supplemented by location data. The interception of the data sent from the drone to the station can lead to substantial threats. To address this issue, highly confidential protection methods must be employed. This paper introduces a novel steganography approach called the Shuffling Steganography Approach (SSA). SSA encompasses five fundamental stages and three proposed algorithms, designed… More >

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Yong Xu^1,2, Xuewei Zhang¹, Wenlong Xie^2,*, Shihong Zhang², Xinyue Huang³, Yaqiang Tian¹, Liansheng Chen¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2753-2768, 2024, DOI:10.32604/cmc.2024.055408 - 18 November 2024
（This article belongs to the Special Issue: Emerging Trends in Fuzzy Logic)
Abstract The design of the loading path is one of the important research contents of the tube hydroforming process. Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization. In this paper, the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object. Fuzzy control was used to optimize the loading path, and the fuzzy rule base was established based on FEM. The minimum wall thickness and wall thickness reduction rate were determined as input membership functions, and the axial feeds variable value… More >

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Yameng Yin¹, Lieping Zhang^2,*, Xiaoxu Shi¹, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou⁴
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2769-2790, 2024, DOI:10.32604/cmc.2024.056791 - 18 November 2024
Abstract By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value… More >

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Isha Sood^*, Varsha Sharma
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2791-2818, 2024, DOI:10.32604/cmc.2024.055463 - 18 November 2024
Abstract Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases, rendering them less effective against evolving ransomware families. This paper introduces TLERAD (Transfer Learning for Enhanced Ransomware Attack Detection), a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains, enabling robust detection of both known and unknown ransomware variants. The proposed method More >

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Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Sarvenaz Sadat Khatami³, Diego Martín^2,*, Sepehr Soltani⁴, Sina Aghakhani⁵
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2819-2843, 2024, DOI:10.32604/cmc.2024.056823 - 18 November 2024
Abstract In the evolving landscape of the smart grid (SG), the integration of non-organic multiple access (NOMA) technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management. However, the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages, especially when broadcasted from a neighborhood gateway (NG) to smart meters (SMs). This paper introduces a novel approach based on reinforcement learning (RL) to fortify the performance of secrecy. Motivated by the need for efficient and effective training of the fully connected layers in the RL… More >

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Shanjita Akter Prome¹, Md Rafiqul Islam^2,*, Md. Kowsar Hossain Sakib¹, David Asirvatham¹, Neethiahnanthan Ari Ragavan³, Cesar Sanin², Edward Szczerbicki⁴
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2845-2871, 2024, DOI:10.32604/cmc.2024.055311 - 18 November 2024
（This article belongs to the Special Issue: Artificial Intelligence Current Perspectives and Alternative Paths: From eXplainable AI to Generative AI and Data Visualization Technologies)
Abstract Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception or lie detection systems are non-intrusive, cost-effective, and mobile by identifying facial expressions. Over the last decade, numerous studies have been conducted on deception detection using several advanced techniques. Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception. So, it could be challenging to spot trends, practical approaches, gaps, and chances for contribution.… More >

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Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui¹, Amr Abdussalam², Muhammad Asim³, Naveed Ahmed⁴, Mohammed Ali Alshara^4,5
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2873-2894, 2024, DOI:10.32604/cmc.2024.054841 - 18 November 2024
Abstract Image captioning has gained increasing attention in recent years. Visual characteristics found in input images play a crucial role in generating high-quality captions. Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image, improving the effectiveness of identifying relevant image regions at each step of caption generation. However, providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features. Consequently, this leads to enhanced captioning network performance. In light… More >

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Jian Feng^*, Tian Liu, Cailing Du
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2895-2909, 2024, DOI:10.32604/cmc.2024.056434 - 18 November 2024
Abstract Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph representation learning to eliminate the dependence of labels. However, existing studies neglect positional information when learning discrete snapshots, resulting in insufficient network topology learning. At the same time, due to the lack of appropriate data augmentation methods, it is difficult to capture the evolving patterns of the network effectively. To address the above problems, a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs. Firstly, the global snapshot is built based on the historical snapshots… More >

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Sanxia Shi, Yinglei Song^*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2911-2934, 2024, DOI:10.32604/cmc.2024.055363 - 18 November 2024
Abstract Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.… More >

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Alonso Alvarez, Borja Bordel Sánchez^*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2935-2966, 2024, DOI:10.32604/cmc.2024.056990 - 18 November 2024
Abstract Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering. Despite the impact of these transformations on organizations, they have not been extensively studied in academia. We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations, including organizations undergoing transformations (“final organizations”) and companies supporting these processes (“consultants”). The study aims to understand the motivations, objectives, and factors driving and challenging these transformations. Over 700 responses were collected to the question and categorized into 32 objectives. The findings show More >

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Mohana Priya Govindarajan^*, Sangeetha Subramaniam Karuppaiya Bharathi
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2967-2986, 2024, DOI:10.32604/cmc.2024.055536 - 18 November 2024
Abstract In the present research, we describe a computer-aided detection (CAD) method aimed at automatic fetal head circumference (HC) measurement in 2D ultrasonography pictures during all trimesters of pregnancy. The HC might be utilized toward determining gestational age and tracking fetal development. This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited. The CAD system is divided into two steps: to begin, Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull. We identified the HC using dynamic programming,… More >

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Tanvir Fatima Naik Bukht¹, Abdulwahab Alazeb², Naif Al Mudawi², Bayan Alabdullah³, Khaled Alnowaiser⁴, Ahmad Jalal¹, Hui Liu^5,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2987-3002, 2024, DOI:10.32604/cmc.2024.053547 - 18 November 2024
Abstract In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT More >

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Hira Akhtar Butt¹, Khoula Said Al Harthy², Mumtaz Ali Shah³, Mudassar Hussain^2,*, Rashid Amin^4,*, Mujeeb Ur Rehman¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3003-3031, 2024, DOI:10.32604/cmc.2024.057185 - 18 November 2024
Abstract Detecting sophisticated cyberattacks, mainly Distributed Denial of Service (DDoS) attacks, with unexpected patterns remains challenging in modern networks. Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking (SDN) environments. While Machine Learning (ML) models can distinguish between benign and malicious traffic, their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining. In this paper, we propose a novel DDoS detection framework that combines Machine Learning (ML) and Ensemble Learning (EL) techniques to improve DDoS attack detection and mitigation in SDN environments. Our model… More >

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Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, Xiao Sui¹, Junzo Watada³
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024
Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

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Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024
Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

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Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024
（This article belongs to the Special Issue: Artificial Neural Networks and its Applications)
Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

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Nguyen Long Giang¹, Tran Thanh Dai², Le Hoang Son³, Tran Thi Ngan⁴, Nguyen Nhu Son¹, Cu Nguyen Giap^5,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3097-3124, 2024, DOI:10.32604/cmc.2024.057383 - 18 November 2024
（This article belongs to the Special Issue: Advanced Data Mining Techniques: Security, Intelligent Systems and Applications)
Abstract Attribute reduction through the combined approach of Rough Sets (RS) and algebraic topology is an open research topic with significant potential for applications. Several research works have introduced a strong relationship between RS and topology spaces for the attribute reduction problem. However, the mentioned recent methods followed a strategy to construct a new measure for attribute selection. Meanwhile, the strategy for searching for the reduct is still to select each attribute and gradually add it to the reduct. Consequently, those methods tended to be inefficient for high-dimensional datasets. To overcome these challenges, we use the… More >

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Sonia Goel^1,#, Meena Tushir¹, Jyoti Arora², Tripti Sharma², Deepali Gupta³, Ali Nauman^4,#, Ghulam Muhammad^5,*
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3125-3145, 2024, DOI:10.32604/cmc.2024.054476 - 18 November 2024
Abstract In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear More >

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Nataliya Shakhovska^1,2,*, Mykola Medykovskyi¹, Oleksandr Gurbych^1,3, Mykhailo Mamchur^1,3, Mykhailo Melnyk¹
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024
（This article belongs to the Special Issue: Artificial Neural Networks and its Applications)
Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

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Samsul Huda^1,*, Yasuyuki Nogami², Maya Rahayu², Takuma Akada², Md. Biplob Hossain², Muhammad Bisri Musthafa², Yang Jie², Le Hoang Anh²
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3165-3187, 2024, DOI:10.32604/cmc.2024.058144 - 18 November 2024
Abstract Global food security is a pressing issue that affects the stability and well-being of communities worldwide. While existing Internet of Things (IoT) enabled plant monitoring systems have made significant strides in agricultural monitoring, they often face limitations such as high power consumption, restricted mobility, complex deployment requirements, and inadequate security measures for data access. This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings. Our system strategically combines power efficiency, portability, and secure access capabilities, assisting farmers in monitoring and tracking crop environmental conditions. The proposed system includes a… More >

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