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  • Open Access

    ARTICLE

    ONTDAS: An Optimized Noise-Based Traffic Data Augmentation System for Generalizability Improvement of Traffic Classifiers

    Rongwei Yu1, Jie Yin1,*, Jingyi Xiang1, Qiyun Shao2, Lina Wang1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 365-391, 2025, DOI:10.32604/cmc.2025.064438 - 09 June 2025

    Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >

  • Open Access

    ARTICLE

    Dual-Classifier Label Correction Network for Carotid Plaque Classification on Multi-Center Ultrasound Images

    Louyi Jiang1,#, Sulei Wang1,#, Jiang Xie1, Haiya Wang2, Wei Shao3,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5445-5460, 2025, DOI:10.32604/cmc.2025.061759 - 19 May 2025

    Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >

  • Open Access

    ARTICLE

    Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

    Yawar Abbas1, Aisha Ahmed Alarfaj2, Ebtisam Abdullah Alabdulqader3, Asaad Algarni4, Ahmad Jalal1,5, Hui Liu6,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025

    Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >

  • Open Access

    ARTICLE

    Deep Learning and Machine Learning Architectures for Dementia Detection from Speech in Women

    Ahlem Walha1, Amel Ksibi2,*, Mohammed Zakariah3,*, Manel Ayadi2, Tagrid Alshalali2, Oumaima Saidani2, Leila Jamel2, Nouf Abdullah Almujally2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2959-3001, 2025, DOI:10.32604/cmes.2025.060545 - 03 March 2025

    Abstract Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains… More >

  • Open Access

    ARTICLE

    Enhanced Particle Swarm Optimization Algorithm Based on SVM Classifier for Feature Selection

    Xing Wang1,*, Huazhen Liu1, Abdelazim G. Hussien2, Gang Hu1, Li Zhang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2791-2839, 2025, DOI:10.32604/cmes.2025.058473 - 03 March 2025

    Abstract Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. Particle Swarm Optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary… More >

  • Open Access

    REVIEW

    Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

    Suneel Kumar Rath1, Madhusmita Sahu1, Shom Prasad Das2, Junali Jasmine Jena3, Chitralekha Jena4, Baseem Khan5,6,7,*, Ahmed Ali7, Pitshou Bokoro7

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

    Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

  • Open Access

    ARTICLE

    An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

    Saad M. Darwish1,*, Abdul Rahman M. Sabri2, Dhafar Hamed Abd2, Adel A. Elzoghabi1

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1595-1624, 2024, DOI:10.32604/csse.2024.054615 - 22 November 2024

    Abstract The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author’s beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models… More >

  • Open Access

    ARTICLE

    Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

    Farida Asriani1,2, Azhari Azhari1,*, Wahyono Wahyono1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

    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 >

  • Open Access

    ARTICLE

    Optimizing Internet of Things Device Security with a Globalized Firefly Optimization Algorithm for Attack Detection

    Arkan Kh Shakr Sabonchi*

    Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

    Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

  • Open Access

    ARTICLE

    Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

    Fahim Nasir1, Abdulghani Ali Ahmed1,*, Mehmet Sabir Kiraz1, Iryna Yevseyeva1, Mubarak Saif2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

    Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

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