Open Access

ARTICLE

Xueli He^1,, Shengkun Lv^1,, Ruifeng Dou^1,*, Yanying Zhang¹, Junsheng Wang², Xunliang Liu¹, Zhi Wen¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2873-2895, 2024, DOI:10.32604/cmc.2024.052571 - 15 August 2024
（This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract Four typical theories on the formation of thermal tears: strength, liquid film, intergranular bridging, and solidification shrinkage compensation theories. From these theories, a number of criteria have been derived for predicting the formation of thermal cracks, such as the stress-based Niyama, Clyne, and RDG (Rapaz-Dreiser-Grimaud) criteria. In this paper, a mathematical model of horizontal centrifugal casting was established, and numerical simulation analysis was conducted for the centrifugal casting process of cylindrical Al-Cu alloy castings to investigate the effect of the centrifugal casting process conditions on the microstructure and hot tearing sensitivity of alloy castings by… More >

View

892

Download

526

Open Access

ARTICLE

Fang Hu¹, Siyi Qiu², Xiaolian Yang¹, Chaolei Wu¹, Miguel Baptista Nunes³, Hui Chen^4,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2897-2915, 2024, DOI:10.32604/cmc.2024.052570 - 15 August 2024
（This article belongs to the Special Issue: Trustworthy Wireless Computing Power Networks Assisted by Blockchain)
Abstract As the volume of healthcare and medical data increases from diverse sources, real-world scenarios involving data sharing and collaboration have certain challenges, including the risk of privacy leakage, difficulty in data fusion, low reliability of data storage, low effectiveness of data sharing, etc. To guarantee the service quality of data collaboration, this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning, termed FL-HMChain. This system is composed of three layers: Data extraction and storage, data management, and data application. Focusing on healthcare and medical data, a healthcare and… More >

View

1178

Download

558

Open Access

ARTICLE

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024
（This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

View

1047

Download

580

Open Access

ARTICLE

Kexin Wang^*, Yingdong Gou, Dingrui Xue^*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893 - 15 August 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 collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >

View

752

Download

439

Open Access

ARTICLE

Yunxiang Liu, Haili Ma, Jianlin Zhu^*, Qiangbo Zhang
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710 - 15 August 2024
（This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >

View

649

Download

364

Open Access

ARTICLE

Wei Liu^*, Tengteng Ren
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2979-3000, 2024, DOI:10.32604/cmc.2024.053627 - 15 August 2024
Abstract Feature Selection (FS) is a key pre-processing step in pattern recognition and data mining tasks, which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models. In recent years, meta-heuristic algorithms have been widely used in FS problems, so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization (HBCSSDBO) algorithm is proposed in this paper to improve the effect of FS. In this hybrid algorithm, the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem. By combining the… More >

View

836

Download

492

Open Access

ARTICLE

Zhibo Yang, Xiaohan Huang, Bingdong Wang, Bin Hu, Zhenyong Zhang^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3001-3019, 2024, DOI:10.32604/cmc.2024.053369 - 15 August 2024
Abstract With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible More >

View

555

Download

311

Open Access

ARTICLE

Fangzhou He^1,3, Ke Ding^1,2, Dingjiang Yan³, Jie Li^3,*, Jiajun Wang^1,2, Mingzhe Chen^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3021-3045, 2024, DOI:10.32604/cmc.2024.053632 - 15 August 2024
Abstract Massive computational complexity and memory requirement of artificial intelligence models impede their deployability on edge computing devices of the Internet of Things (IoT). While Power-of-Two (PoT) quantization is proposed to improve the efficiency for edge inference of Deep Neural Networks (DNNs), existing PoT schemes require a huge amount of bit-wise manipulation and have large memory overhead, and their efficiency is bounded by the bottleneck of computation latency and memory footprint. To tackle this challenge, we present an efficient inference approach on the basis of PoT quantization and model compression. An integer-only scalar PoT quantization (IOS-PoT)… More >

View

919

Download

365

Open Access

ARTICLE

Jianfeng Lu^1,2, Caijin Li¹, Xiangye Huang¹, Chen Cui³, Mahmoud Emam^1,2,4,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.053680 - 15 August 2024
Abstract The widespread availability of digital multimedia data has led to a new challenge in digital forensics. Traditional source camera identification algorithms usually rely on various traces in the capturing process. However, these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms. Convolutional Neural Networks (CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices. However, their performances is not ideal in case of distinguishing between individual devices of the same model, because cameras of the same model typically use the same… More >

View

1156

Download

506

Open Access

ARTICLE

Peicong He, Yang Xin^*, Yixian Yang
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777 - 15 August 2024
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

View

618

Download

317

Open Access

ARTICLE

Yousef Ibrahim Daradkeh^1,*, Volodymyr Gorokhovatskyi², Iryna Tvoroshenko^2,*, Medien Zeghid^1,3
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3085-3106, 2024, DOI:10.32604/cmc.2024.051709 - 15 August 2024
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors. The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency. The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations. It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.… More >

View

882

Download

335

Open Access

ARTICLE

Ruijin Xue^1,2, Shuang Feng^1,2,*, Qi Wang^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600 - 15 August 2024
Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >

View

514

Download

362

Open Access

ARTICLE

Saket Sarin¹, Sunil K. Singh¹, Sudhakar Kumar¹, Shivam Goyal¹, Brij Bhooshan Gupta^2,3,4,8,*, Wadee Alhalabi⁵, Varsha Arya^6,7
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599 - 15 August 2024
Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >

View

1234

Download

574

Open Access

REVIEW

AFM Zainul Abadin^1,2,*, Rossilawati Sulaiman¹, Mohammad Kamrul Hasan¹
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3139-3171, 2024, DOI:10.32604/cmc.2024.050834 - 15 August 2024
Abstract Image steganography is one of the prominent technologies in data hiding standards. Steganographic system performance mostly depends on the embedding strategy. Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image. The randomization strategies in data embedding techniques may utilize random domains, pixels, or region-of-interest for concealing secrets into a cover image, preventing information from being discovered by an attacker. The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility, but it is challenging. A systematic approach is More >

View

3318

Download

903

Open Access

ARTICLE

Jingtao Sun^1,2, Fengling Zhang^1,2,*, Huanqi Liu^1,2, Wenyan Hou^1,2
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3173-3192, 2024, DOI:10.32604/cmc.2024.051705 - 15 August 2024
Abstract As image manipulation technology advances rapidly, the malicious use of image tampering has alarmingly escalated, posing a significant threat to social stability. In the realm of image tampering localization, accurately localizing limited samples, multiple types, and various sizes of regions remains a multitude of challenges. These issues impede the model’s universality and generalization capability and detrimentally affect its performance. To tackle these issues, we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization. Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain, and captures richer traces of tampering… More >

View

614

Download

457

Open Access

ARTICLE

Linghong Kuang^1,2, Wenlong Shi^1,2, Jing Zhang^1,2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3193-3219, 2024, DOI:10.32604/cmc.2024.054377 - 15 August 2024
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing… More >

View

669

Download

411

Open Access

ARTICLE

Ayesha Khaliq¹, Salman Afsar Awan¹, Fahad Ahmad^2,*, Muhammad Azam Zia¹, Muhammad Zafar Iqbal³
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488 - 15 August 2024
Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >

View

688

Download

420

Open Access

ARTICLE

Jiahan He¹, Guangjun Liang^1,2,3,*, Meng Li⁴, Kefan Yao¹, Bixia Wang¹, Lu Li⁵
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3243-3261, 2024, DOI:10.32604/cmc.2024.051710 - 15 August 2024
Abstract In this paper, considering the cost of base station, coverage, call quality, and other practical factors, a multi-objective optimal site planning scheme is proposed. Firstly, based on practical needs, mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives, coverage objectives, and quality objectives. Then, a multi-objective optimization model was established by combining threshold and traffic volume constraints. In order to reduce the time complexity of optimization, a non-dominated sorting genetic algorithm (NSGA) is used to solve the multi-objective optimization problem of site planning. Finally, a strategy for clustering… More >

View

548

Download

448

Open Access

ARTICLE

Shibin Zhao^*, Junhu Zhu, Jianshan Peng
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3263-3287, 2024, DOI:10.32604/cmc.2024.041949 - 15 August 2024
（This article belongs to the Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract In recent years, the rapid development of computer software has led to numerous security problems, particularly software vulnerabilities. These flaws can cause significant harm to users’ privacy and property. Current security defect detection technology relies on manual or professional reasoning, leading to missed detection and high false detection rates. Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes, reducing missed alarms and false alarms. So, this project aims to study Java source code defect detection methods for defects like null pointer… More >

View

941

Download

411

Open Access

REVIEW

Sameer Qazi^1,*, Muhammad Bilal Kadri², Muhammad Naveed^1,*, Bilal A. Khawaja³, Sohaib Zia Khan⁴, Muhammad Mansoor Alam^5,6,7, Mazliham Mohd Su’ud⁶
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893 - 15 August 2024
Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >

View

4064

Download

1178

Like

248

Open Access

ARTICLE

Asifa Mehmood Qureshi¹, Abdul Haleem Butt¹, Abdulwahab Alazeb², Naif Al Mudawi², Mohammad Alonazi³, Nouf Abdullah Almujally⁴, Ahmad Jalal¹, Hui Liu^5,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3315-3332, 2024, DOI:10.32604/cmc.2024.052582 - 15 August 2024
（This article belongs to the Special Issue: Multimodal Learning in Image Processing)
Abstract Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management. However, vehicles come in a range of sizes, which is challenging to detect, affecting the traffic monitoring system’s overall accuracy. Deep learning is considered to be an efficient method for object detection in vision-based systems. In this paper, we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5 (YOLOv5) detector combined with a segmentation technique. The model consists of six steps. In the first step, all the extracted traffic sequence images are subjected… More >

View

1111

Download

392

Open Access

ARTICLE

Ruobing Zuo¹, Xiaohan Huang¹, Xuguo Jiao^2,3, Zhenyong Zhang^1,4,5,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3333-3349, 2024, DOI:10.32604/cmc.2024.050544 - 15 August 2024
Abstract In the rapidly evolving urban landscape, outdoor parking lots have become an indispensable part of the city’s transportation system. The growth of parking lots has raised the likelihood of spontaneous vehicle combustion, a significant safety hazard, making smoke detection an essential preventative step. However, the complex environment of outdoor parking lots presents additional challenges for smoke detection, which necessitates the development of more advanced and reliable smoke detection technologies. This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots. First, we develop a novel… More >

View

771

Download

497

Open Access

ARTICLE

Tuğba Çelikten¹, Aytuğ Onan^2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3351-3377, 2024, DOI:10.32604/cmc.2024.051574 - 15 August 2024
Abstract The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The… More >

View

759

Download

377

Open Access

ARTICLE

Pirapat Arunyanart, Nithitorn Kongkaew, Supattarachai Sudsawat^*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3379-3403, 2024, DOI:10.32604/cmc.2024.054337 - 15 August 2024
Abstract This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method (DEM) simulation, design of experiments (DOE), and metaheuristic optimization algorithms. Specifically, the study employs the firefly algorithm (FA), a metaheuristic optimization technique, to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions. The experimental methodology involves several key steps: screening experiments to identify significant factors affecting bucket elevator operation, central composite design (CCD) experiments to further explore these factors, and response surface methodology (RSM)… More >

View

857

Download

607

Open Access

ARTICLE

Zhuoyifan Zhang¹, Lu Zhang², Liang Wang³, Haoming Wu^2,*
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3405-3420, 2024, DOI:10.32604/cmc.2024.052144 - 15 August 2024
Abstract Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions. The NeRF (Neural Radiance Fields) algorithm, suitable for underwater scenes or scattering media, is also evolving. Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency. This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction. First, we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium… More >

View

513

Download

399