Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (94)
  • Open Access

    ARTICLE

    A Support Vector Machine (SVM) Model for Privacy Recommending Data Processing Model (PRDPM) in Internet of Vehicles

    Ali Alqarni*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

    Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >

  • Open Access

    ARTICLE

    Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

    A. Robert Singh1, Suganya Athisayamani2, Gyanendra Prasad Joshi3, Bhanu Shrestha4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 299-327, 2025, DOI:10.32604/cmes.2024.055599 - 17 December 2024

    Abstract Myocardial perfusion imaging (MPI), which uses single-photon emission computed tomography (SPECT), is a well-known estimating tool for medical diagnosis, employing the classification of images to show situations in coronary artery disease (CAD). The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks (CNNs). This paper uses a SPECT classification framework with three steps: 1) Image denoising, 2) Attenuation correction, and 3) Image classification. Image denoising is done by a U-Net architecture that ensures effective image denoising. Attenuation correction is implemented by a convolution neural network model that… More >

  • Open Access

    REVIEW

    Tetralogy of Fallot: Anatomy, Physiology, and Outcomes

    Edo Bedzra1,*, Eli Contorno2, Herra Javed2, Amna Qasim3, James St. Louis4, Taufiek Konrad Rajab2

    Congenital Heart Disease, Vol.19, No.6, pp. 541-562, 2024, DOI:10.32604/chd.2025.059788 - 27 January 2025

    Abstract Since the first identification of Tetralogy of Fallot in 1671, consisting of a combination of anatomical defects including biventricular origin of the aorta, maligned ventricular septal defect, overriding aorta, and narrowing or atresia of the pulmonary outflow tract. The first successful operation consisted of a shunt between the left subclavian artery and pulmonary artery. Following this palliative procedure, complete repair is performed once the patient reaches indicative criteria. Since the first attempts at surgical palliation and repair, techniques and outcomes have improved drastically. Definitive repair of Tetralogy of Fallot consists of a multi-patch closure of More >

  • Open Access

    PROCEEDINGS

    Towards High-Fidelity and Efficient Computation for Diagnosis and Treatment of Cardiovascular Disease

    Lei Wang1,*, Blanca Rodriguez2, Xiaoyu Luo3, Charles Augarde4

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.013350

    Abstract Cardiovascular disease is the leading cause of death worldwide. Disease-specific software, like FFRct from HeartFlow, and high-fidelity computational models within a general-purpose software, like Living Heart Project within Abaqus, are essential to revolutionise diagnosis and treatment of cardiovascular disease for clinicians and design of medical devices for industries. This talk presents our past researches on computational modelling of tear propagation in the aortic dissection [1-2] and of electromechanical coupling in the human heart with the finite element method [3], and our current exploration on high-fidelity and efficient computation and software development for diagnosis and treatment More >

  • Open Access

    REVIEW

    Advancement in CFD and Responsive AI to Examine Cardiovascular Pulsatile Flow in Arteries: A Review

    Priyambada Praharaj1,*, Chandrakant R. Sonawane2,*, Arunkumar Bongale2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2021-2064, 2024, DOI:10.32604/cmes.2024.056289 - 31 October 2024

    Abstract This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics (CFD) in biomedical applications. Beyond its various engineering applications, CFD has started to establish a presence in the biomedical field. Cardiac abnormality, a familiar health issue, is an essential point of investigation by research analysts. Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood. The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities. Numerous studies have explored the rheological behavior of blood experimentally and numerically.… More >

  • Open Access

    ARTICLE

    Heart-Net: A Multi-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

    Deema Mohammed Alsekait1, Ahmed Younes Shdefat2, Ayman Nabil3, Asif Nawaz4,*, Muhammad Rizwan Rashid Rana4, Zohair Ahmed5, Hanaa Fathi6, Diaa Salama AbdElminaam6,7,8

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

    Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

  • Open Access

    ARTICLE

    Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

    Tajim Md. Niamat Ullah Akhund1,2,*, Waleed M. Al-Nuwaiser3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

    Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

  • Open Access

    ARTICLE

    Cardiovascular Disease Prediction Using Risk Factors: A Comparative Performance Analysis of Machine Learning Models

    Adil Hussain1,*, Ayesha Aslam2

    Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277 - 21 May 2024

    Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

  • Open Access

    REVIEW

    Therapeutic and regenerative potential of different sources of mesenchymal stem cells for cardiovascular diseases

    YARA ALZGHOUL, HALA J. BANI ISSA, AHMAD K. SANAJLEH, TAQWA ALABDUH, FATIMAH RABABAH, MAHA AL-SHDAIFAT, EJLAL ABU-EL-RUB*, FATIMAH ALMAHASNEH, RAMADA R. KHASAWNEH, AYMAN ALZU’BI, HUTHAIFA MAGABLEH

    BIOCELL, Vol.48, No.4, pp. 559-569, 2024, DOI:10.32604/biocell.2024.048056 - 09 April 2024

    Abstract Mesenchymal stem cells (MSCs) are ideal candidates for treating many cardiovascular diseases. MSCs can modify the internal cardiac microenvironment to facilitate their immunomodulatory and differentiation abilities, which are essential to restore heart function. MSCs can be easily isolated from different sources, including bone marrow, adipose tissues, umbilical cord, and dental pulp. MSCs from various sources differ in their regenerative and therapeutic abilities for cardiovascular disorders. In this review, we will summarize the therapeutic potential of each MSC source for heart diseases and highlight the possible molecular mechanisms of each source to restore cardiac function. More >

  • Open Access

    ARTICLE

    Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

    Afnan M. Alhassan*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427 - 27 February 2024

    Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has… More >

Displaying 1-10 on page 1 of 94. Per Page