Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,046)
  • Open Access

    PROCEEDINGS

    Quantitative Characterization of Microstructural Inhomogeneity: Integrating Ultrasonic Scattering Mechanisms from Multi-Features in Additive Manufactured Microstructures

    Junfei Tai1, Zheng Fan1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011059

    Abstract The non-destructive characterization of material microstructures presents a significant and enduring challenge in the field. The sensitivity of elastic waves to the nuances of microstructural parameters positions ultrasound as a viable and potent method for non-destructive evaluation. However, enhancing the interaction between elastic waves and the internal microstructure typically involves utilizing wavelengths larger than the microstructural features, thereby rendering ultrasonic scattering as the predominant mechanism. This interaction is complicated by the fact that fundamental microstructural characteristics, such as grain size, morphology, and texture intensity, exert considerable and intertwined effects on ultrasonic scattering, complicating their separate… 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

    Performance Analysis of Machine Learning-Based Intrusion Detection with Hybrid Feature Selection

    Mohammad Al-Omari1, Qasem Abu Al-Haija2,*

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1537-1555, 2024, DOI:10.32604/csse.2024.056257 - 22 November 2024

    Abstract More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique’s foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. 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

    A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

    Qi Wang1, Chenxin Li1,*, Chichen Lin2, Weijian Fan3, Shuang Feng1, Yuanzhong Wang4

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3351-3369, 2024, DOI:10.32604/cmc.2024.057191 - 18 November 2024

    Abstract News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem. Most previous works only extract features and evaluate media from one dimension independently, ignoring the interconnections between different aspects. This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features. This framework models the relationship and interaction between media bias and factuality, utilizing this relationship to assist in the prediction of profiling results. Our approach extracts features independently while aligning and fusing them through recursive convolution and More >

  • Open Access

    ARTICLE

    An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

    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

    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 >

  • Open Access

    ARTICLE

    YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

    Chenghai Yu, Zhilong Lu*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3261-3280, 2024, DOI:10.32604/cmc.2024.056413 - 18 November 2024

    Abstract Railway turnouts often develop defects such as chipping, cracks, and wear during use. If not detected and addressed promptly, these defects can pose significant risks to train operation safety and passenger security. Despite advances in defect detection technologies, research specifically targeting railway turnout defects remains limited. To address this gap, we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments. To enhance detection accuracy, we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU (YOLO-VSI). The model employs a state-space model (SSM) to enhance the C2f module in the YOLOv8… More >

  • Open Access

    ARTICLE

    A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

    Alaa Thobhani1,*, Beiji Zou1, Xiaoyan Kui1, Amr Abdussalam2, Muhammad Asim3, Naveed Ahmed4, Mohammed Ali Alshara4,5

    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 >

  • Open Access

    ARTICLE

    Robust Human Interaction Recognition Using Extended Kalman Filter

    Tanvir Fatima Naik Bukht1, Abdulwahab Alazeb2, Naif Al Mudawi2, Bayan Alabdullah3, Khaled Alnowaiser4, Ahmad Jalal1, Hui Liu5,*

    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 >

  • Open Access

    ARTICLE

    Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

    Amjad Rehman1, Muhammad Mujahid1, Robertas Damasevicius2,*, Faten S Alamri3, Tanzila Saba1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

    Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Displaying 1-10 on page 1 of 1046. Per Page