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  • Open Access

    ARTICLE

    An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

    Zinah Khalid Jasim Jasim*, Sefer Kurnaz*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4643-4662, 2024, DOI:10.32604/cmc.2024.055195

    Abstract This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use… More >

  • Open Access

    ARTICLE

    Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

    Fangfang Shan1,2,*, Zhenyu Wang1,2, Mengyao Liu1,2, Menghan Zhang1,2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3881-3902, 2024, DOI:10.32604/cmc.2024.055167

    Abstract In response to the challenges of generating Attribute-Based Access Control (ABAC) policies, this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents. This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model. The manual retrieval and analysis involved in this transition are inefficient, prone to errors, and costly. Most organizations have high-level specifications defined for security policies that include a set of access control policies, which often exist in the form of natural language documents. Utilizing… More >

  • Open Access

    ARTICLE

    Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost Framework

    Sheng Xu1, Shixiong Xiang2, Feiyu Meng1, Qiang Wu1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4197-4218, 2024, DOI:10.32604/cmc.2024.055157

    Abstract In Unsupervised Domain Adaptation (UDA) for person re-identification (re-ID), the primary challenge is reducing the distribution discrepancy between the source and target domains. This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain. Implicit construction is difficult due to the absence of intermediate state supervision, making smooth knowledge transfer from the source to the target domain a challenge. To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,… More >

  • Open Access

    ARTICLE

    Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRL

    Weiqiang Jin1,#, Xingwu Tian1,#, Bohang Shi1, Biao Zhao1,*, Haibin Duan2, Hao Wu3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125

    Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

  • Open Access

    ARTICLE

    HWD-YOLO: A New Vision-Based Helmet Wearing Detection Method

    Licheng Sun1, Heping Li2,3, Liang Wang1,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115

    Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

  • Open Access

    ARTICLE

    Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

    Atta Rahman1,*, Mustafa Youldash2, Ghaida Alshammari2, Abrar Sebiany2, Joury Alzayat2, Manar Alsayed2, Mona Alqahtani2, Noor Aljishi2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106

    Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More >

  • Open Access

    ARTICLE

    Phenotypic Image Recognition of Asparagus Stem Blight Based on Improved YOLOv8

    Shunshun Ji, Jiajun Sun, Chao Zhang*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4017-4029, 2024, DOI:10.32604/cmc.2024.055038

    Abstract Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus… More >

  • Open Access

    ARTICLE

    Machine Fault Diagnosis Using Audio Sensors Data and Explainable AI Techniques-LIME and SHAP

    Aniqua Nusrat Zereen1, Abir Das2, Jia Uddin3,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886

    Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

  • Open Access

    ARTICLE

    A Low Complexity ML-Based Methods for Malware Classification

    Mahmoud E. Farfoura1,*, Ahmad Alkhatib1, Deema Mohammed Alsekait2,*, Mohammad Alshinwan3,7, Sahar A. El-Rahman4, Didi Rosiyadi5, Diaa Salama AbdElminaam6,7

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849

    Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

  • Open Access

    ARTICLE

    Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

    Neng Wan1,2, Guangping Zeng1,*, Xianwei Zhou1

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4281-4302, 2024, DOI:10.32604/cmc.2024.054794

    Abstract With the development of vehicles towards intelligence and connectivity, vehicular data is diversifying and growing dramatically. A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle (ICV) applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points (NCPs). Considering the amount of task data and the idle resources of NCPs, a computing resource scheduling model for NCPs is established. Taking the heterogeneous task execution delay threshold as a constraint, the optimization problem is described as the problem of maximizing the utilization of computing resources by NCPs. The… More >

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