Sami Alrabie^#,*, Ahmed Barnawi^#

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 2025

Abstract In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

Amrutanshu Panigrahi¹, Abhilash Pati¹, Bibhuprasad Sahu², Ashis Kumar Pati³, Subrata Chowdhury⁴, Khursheed Aurangzeb^5,*, Nadeem Javaid⁶, Sheraz Aslam^7,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1633-1657, 2025, DOI:10.32604/cmc.2025.063562 - 09 June 2025

Abstract The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy… More >

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

Kunrong Shen^*, Wanchun Jin, Jin Wang

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1361-1379, 2024, DOI:10.32604/fhmt.2024.057372 - 30 October 2024

Abstract The modified Siemens method is the dominant process for the production of polysilicon, yet it is characterised by high energy consumption. Two models of laboratory-grade Siemens reduction furnace and 12 pairs of rods industrial-grade Siemens chemical vapor deposition (CVD) reduction furnace were established, and the effects of factors such as the diameter of silicon rods, the surface temperature of silicon rods, the air inlet velocity and temperature on the heat transfer process inside the reduction furnace were investigated by numerical simulation. The results show that the convective and radiant heat losses in the furnace increased… 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 >

R. Sujatha¹, Jyotir Moy Chatterjee², NZ Jhanjhi³, Thamer A. Tabbakh⁴, Zahrah A. Almusaylim^5,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 115-129, 2022, DOI:10.32604/iasc.2022.019133 - 26 October 2021

Abstract Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often… More >

Osama S. Faragallah^1,*, Ghada Abdel-Aziz², Hala S. El-sayed³, Gamal G. N. Geweid^4,5

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 575-588, 2021, DOI:10.32604/iasc.2021.019023 - 11 August 2021

Abstract As a regular tool for assessing and diagnosing cardiovascular disease (CVD), medical professionals and health care centers, are highly dependent on cardiac imaging. The purpose of dividing the cardiac images is to paint the inner and outer walls of the heart to divide all or part of the limb’s boundaries. In order to enhance cardiologist in the process of cardiac segmentation, new and accurate methods are needed to divide the selected object, which is the left ventricle (LV). Segmentation techniques aim to provide a fast segmentation process and improve the reliability of the process. In… More >

Lionel Fabian Fourie¹, Lynndle Square^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.2, pp. 225-235, 2020, DOI:10.32604/fdmp.2020.08771 - 21 April 2020

Abstract A heat transfer study was conducted, in the framework of Computational Fluid Dynamics (CFD), on a Hot-Wire Chemical Vapour Deposition (HWCVD) reactor chamber to determine a safe deposition distance for atomic hydrogen produced by HWCVD. The objective of this study was to show the feasibility of using heat transfer simulations in determining a safe deposition distance for deposition of this kind. All CFD simulations were set-up and solved within the framework of the CFD packages of OpenFOAM namely; snappyHexMesh for mesh generation, buoyantSimpleFoam and rhoSimpleFoam as the solvers and paraView as the post-processing tool. Using More >

Po Ting Lin, Hae Chang Gea, Yogesh Jaluria^*

Frontiers in Heat and Mass Transfer, Vol.1, No.1, pp. 1-20, 2010, DOI:10.5098/hmt.v1.1.3003

Abstract Thermal systems play significant roles in the engineering practice and our lives. To improve those thermal systems, it is necessary to model and optimize the design and the operating conditions. More importantly, the design uncertainties should be considered because the failures of the thermal systems may be very dangerous and produce large loss. This review paper focuses on a systematic strategy of modeling and optimizing of the thermal systems with the considerations of the design uncertainties. To demonstrate the proposed strategy, one of the complicated thermal systems, Chemical Vapor Deposition (CVD), is simulated, parametrically modeled,… More >

Joseph G. Lawrence, John P. Dismukes, Arunan Nadarajah¹

FDMP-Fluid Dynamics & Materials Processing, Vol.3, No.3, pp. 255-264, 2007, DOI:10.3970/fdmp.2007.003.255

Abstract The atmospheric chemical vapor deposition process on continuous glass sheets is a well developed one and the parameters that affect it are relatively well understood. When this process is converted to coat discrete glass plates it introduces a new variable, the gap between the glass plates, which can significantly impact the quality of the coatings. In this study a 2D pseudo steady state model of the process was developed to study the effect of the gap, and the ratio of outlet to inlet gas flow rates (called the bias), on the coating quality. The model… More >