Lei Wang^1,*, 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 >

Priyambada Praharaj^1,*, Chandrakant R. Sonawane^2,*, 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 >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

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 >

Tajim Md. Niamat Ullah Akhund^1,2,*, 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 >

Adil Hussain^1,*, 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 >

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 >

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 >

Nadeem Ahmed^1,*, Khalid Mohammadani², Ali Kashif Bashir^3,4,5, Marwan Omar⁶, Angel Jones⁷, Fayaz Hassan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 633-659, 2024, DOI:10.32604/cmes.2023.031342 - 30 December 2023

Abstract Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles (IoV). However, intricate security challenges are intertwined with technological progress: Vehicular ad hoc Networks (VANETs), a core component of IoV, face security issues, particularly the Black Hole Attack (BHA). This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability; also, BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether. Recognizing the importance of this challenge, we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier… More >

Cheng Wang¹, Haoran Zhu^2,*, Congjun Rao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 691-718, 2024, DOI:10.32604/cmes.2023.029258 - 22 September 2023

Abstract Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk More >

Ghawar Said¹, Ata Ullah², Anwar Ghani^1,*, Muhammad Azeem¹, Khalid Yahya³, Muhammad Bilal⁴, Sayed Chhattan Shah^5,*

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 83-99, 2023, DOI:10.32604/iasc.2023.036079 - 26 January 2024

Abstract The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table More >