Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (122)
  • Open Access

    ARTICLE

    Unmasking Social Robots’ Camouflage: A GNN-Random Forest Framework for Enhanced Detection

    Weijian Fan1,*, Chunhua Wang2, Xiao Han3, Chichen Lin4

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 467-483, 2025, DOI:10.32604/cmc.2024.056930 - 03 January 2025

    Abstract The proliferation of robot accounts on social media platforms has posed a significant negative impact, necessitating robust measures to counter network anomalies and safeguard content integrity. Social robot detection has emerged as a pivotal yet intricate task, aimed at mitigating the dissemination of misleading information. While graph-based approaches have attained remarkable performance in this realm, they grapple with a fundamental limitation: the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles. To unravel this challenge and thwart the camouflage tactics, this work proposed an innovative social… More >

  • Open Access

    ARTICLE

    Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

    Liyao Yang1, Hongyan Ma1,2,3,*, Yingda Zhang1, Wei He1

    Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

    Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

  • Open Access

    ARTICLE

    Overfitting in Machine Learning: A Comparative Analysis of Decision Trees and Random Forests

    Erblin Halabaku, Eliot Bytyçi*

    Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 987-1006, 2024, DOI:10.32604/iasc.2024.059429 - 30 December 2024

    Abstract Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data. This paper presents a comprehensive analysis of machine learning algorithms, focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models. It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP (ACV-CCP). This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter, leveraging cross-validation within the pruning process to achieve More >

  • Open Access

    ARTICLE

    A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

    Surbhi Bhatia Khan1,2,*, Mohammad Alojail3, Mahesh Thyluru Ramakrishna4, Hemant Sharma5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4895-4914, 2024, DOI:10.32604/cmc.2024.057585 - 19 December 2024

    Abstract In Intelligent Manufacturing, Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors, ensuring more informed decision-making and adaptive system management. It also promotes decision making and provides scientific analysis to enhance the efficiency of the operation, cost reduction, maximizing the process of production and so on. Various methods are employed to enhance productivity, yet achieving sustainable manufacturing remains a complex challenge that requires careful consideration. This study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid… More >

  • Open Access

    ARTICLE

    Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques: An Explainable Artificial Intelligence Approach

    Avi Deb Raha1, Fatema Jannat Dihan2, Mrityunjoy Gain1, Saydul Akbar Murad3, Apurba Adhikary2, Md. Bipul Hossain2, Md. Mehedi Hassan1, Taher Al-Shehari4, Nasser A. Alsadhan5, Mohammed Kadrie4, Anupam Kumar Bairagi1,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024

    Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >

  • Open Access

    ARTICLE

    Securing Web by Predicting Malicious URLs

    Imran Khan, Meenakshi Megavarnam*

    Journal of Cyber Security, Vol.6, pp. 117-130, 2024, DOI:10.32604/jcs.2024.048332 - 06 December 2024

    Abstract A URL (Uniform Resource Locator) is used to locate a digital resource. With this URL, an attacker can perform a variety of attacks, which can lead to serious consequences for both individuals and organizations. Therefore, attackers create malicious URLs to gain access to an organization’s systems or sensitive information. It is crucial to secure individuals and organizations against these malicious URLs. A combination of machine learning and deep learning was used to predict malicious URLs. This research contributes significantly to the field of cybersecurity by proposing a model that seamlessly integrates the accuracy of machine More >

  • Open Access

    ARTICLE

    GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

    Mohammad Shoab*, Loiy Alsbatin*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 625-642, 2024, DOI:10.32604/cmc.2024.053721 - 15 October 2024

    Abstract In recent years, machine learning (ML) and deep learning (DL) have significantly advanced intrusion detection systems, effectively addressing potential malicious attacks across networks. This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things (IoT) environment, leveraging the NSL-KDD dataset. To achieve high accuracy, the authors used the feature extraction technique in combination with an auto-encoder, integrated with a gated recurrent unit (GRU). Therefore, the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization (PSO), and PSO has been employed for training the features. The More >

  • Open Access

    ARTICLE

    Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

    Mohammad Ababneh*, Ayat Al-Droos, Ammar El-Hassan

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1171-1191, 2024, DOI:10.32604/csse.2024.052875 - 13 September 2024

    Abstract With the high level of proliferation of connected mobile devices, the risk of intrusion becomes higher. Artificial Intelligence (AI) and Machine Learning (ML) algorithms started to feature in protection software and showed effective results. These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’ malware, especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly. In this article, we present a framework for mobile malware detection based on a… More >

  • Open Access

    ARTICLE

    Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

    Shengdong Cheng1, Juncheng Gao1,*, Hongning Qi2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 871-892, 2024, DOI:10.32604/cmes.2024.052830 - 20 August 2024

    Abstract Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments… More > Graphic Abstract

    Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

  • Open Access

    ARTICLE

    Detecting XSS with Random Forest and Multi-Channel Feature Extraction

    Qiurong Qin, Yueqin Li*, Yajie Mi, Jinhui Shen, Kexin Wu, Zhenzhao Wang

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 843-874, 2024, DOI:10.32604/cmc.2024.051769 - 18 July 2024

    Abstract In the era of the Internet, widely used web applications have become the target of hacker attacks because they contain a large amount of personal information. Among these vulnerabilities, stealing private data through cross-site scripting (XSS) attacks is one of the most commonly used attacks by hackers. Currently, deep learning-based XSS attack detection methods have good application prospects; however, they suffer from problems such as being prone to overfitting, a high false alarm rate, and low accuracy. To address these issues, we propose a multi-stage feature extraction and fusion model for XSS detection based on… More >

Displaying 1-10 on page 1 of 122. Per Page