Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (4)
  • Open Access

    ARTICLE

    Integrating Attention Mechanisms in YOLOv8 for Improved Fall Detection Performance

    Nizar Zaghden1, Emad Ibrahim2, Mukaram Safaldin2,*, Mahmoud Mejdoub3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

    Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

  • Open Access

    ARTICLE

    Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

    N. M. Balamurugan1,*, K. Maithili2, T. K. S. Rathish Babu3, M. Adimoolam4

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 499-514, 2023, DOI:10.32604/iasc.2023.031661 - 29 September 2022

    Abstract Diabetic Eye Disease (DED) is a fundamental cause of blindness in human beings in the medical world. Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy (DR). The Machine Learning (ML) and the Deep Learning (DL) algorithms are the predominant techniques to project and explore the images of DR. Even though some solutions were adapted to challenge the cause of DR disease, still there should be an efficient and accurate DR prediction to be adapted to refine its performance. In this work, a hybrid technique was proposed for classification… More >

  • Open Access

    ARTICLE

    Sign Language to Sentence Formation: A Real Time Solution for Deaf People

    Muhammad Sanaullah1,*, Muhammad Kashif2, Babar Ahmad2, Tauqeer Safdar2, Mehdi Hassan3, Mohd Hilmi Hasan4, Amir Haider5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2501-2519, 2022, DOI:10.32604/cmc.2022.021990 - 29 March 2022

    Abstract Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words… More >

  • Open Access

    ARTICLE

    COVID-19 Automatic Detection Using Deep Learning

    Yousef Sanajalwe1,2,*, Mohammed Anbar1, Salam Al-E’mari1

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 15-35, 2021, DOI:10.32604/csse.2021.017191 - 10 June 2021

    Abstract The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this… More >

Displaying 1-10 on page 1 of 4. Per Page