Maie Aboghazalah^1,*, Passent El-kafrawy², Abdelmoty M. Ahmed³, Rasha Elnemr⁵, Belgacem Bouallegue³, Ayman El-sayed⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, DOI:10.32604/cmc.2023.039936

Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several More >

Yutong Tian¹, Minghan Gao², Qiang Gao^1,*, Xiao-Hong Peng³

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 175-188, 2024, DOI:10.32604/iasc.2024.042762

Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple… More >

Fei Wang, Qinpeng Meng, Jinchi Zhao, Xin Wang, Yuhong Liu, Qianru Zhang^*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.4, pp. 829-846, 2024, DOI:10.32604/fdmp.2023.026917

Abstract Buildings with large open spaces in which chemicals are handled are often exposed to the risk of explosions. Computational fluid dynamics is a useful and convenient way to investigate contaminant dispersion in such large spaces. The turbulent Schmidt number (Sc_t) concept has typically been used in this regard, and most studies have adopted a default value. We studied the concentration distribution for sulfur hexafluoride (SF₆) assuming different emission rates and considering the effect of Sc_t. Then we examined the same problem for a light gas by assuming hydrogen gas (H₂) as the contaminant. When SF₆ was considered as More >

S. Jayanthi^*, S. Prasanna Devi

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975

Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate… More >

Muhammad Aleem Raza¹, Muhammad Anwar², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Usman Ahmed Raza¹, Sadiq Ali Khan⁴, Fahad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275

Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute More >

Charis Tan^1,2 , Diana Zannino³, Carley Clendenning³, Sophie Offen⁴, Thomas L. Gentles⁵, Julian Ayer⁶, David Tanous⁷, Vishva Wijesekera⁸, Leeanne Grigg⁹, David Celermajer^2,4,10, Mark McGuire^2,4 , Yves d’Udekem^3,11,12, Rachael Cordina^2,4,10,*

Congenital Heart Disease, Vol.18, No.5, pp. 507-523, 2023, DOI:10.32604/chd.2023.028829

Abstract Objective: Sudden cardiac death (SCD) and malignant ventricular arrhythmia (VA) are increasingly recognized as important issues for people living with a Fontan circulation, but data are lacking. We sought to characterize the cohort who had sudden cardiac death, most likely related to VA and/or documented VA in the Australia and New Zealand Fontan Registry including risk factors and clinical outcomes. Methods: A retrospective cohort study was performed. Inclusion criteria were documented non-sustained ventricular tachycardia, sustained ventricular tachycardia, ventricular fibrillation, resuscitated cardiac arrest or SCD > 30 days post-Fontan completion. Results: Of 1611 patients, 20 (1.2%) had VA;… More >

Vitaliy Suvorov^1,2,*, Olga Loboda², Maria Balakina¹, Igor Kulczycki²

Congenital Heart Disease, Vol.18, No.5, pp. 491-505, 2023, DOI:10.32604/chd.2023.030583

Abstract Background: Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes. The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case. Methods: We performed the prospective cohort study which included 29 children with congenital heart defects. The hearts and the great vessels were modeled and printed out. Measurements of the same cardiac areas were taken in the… More > Graphic Abstract

N. Vijaya^a,*, Y. Hari Krishna^a , K. Kalyani^b, G.V.R. Reddy^a

Frontiers in Heat and Mass Transfer, Vol.11, pp. 1-11, 2018, DOI:10.5098/hmt.11.19

Abstract The present paper deals with the thermo physical properties of a Casson fluid through an oscillating vertical wall embedded through porous medium under the influence transverse magnetic field, radiation, constant heat source and first order chemical reaction. The radiative heat loss is modelled by using Rosseland approximation. Similarity variables were used to convert the partial differential equations into ordinary differential equation. The transformed ordinary differential equations are solved numerically using Runge - Kutta -Fehlberg method with shooting technique. In order to get perfect perception of the flow pattern we obtain the graphs of axial velocity, More >

S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211

Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and… More >

Fatma Taher¹, Hamoud Alshammari², Lobna Osman³, Mohamed Elhoseny⁴, Abdulaziz Shehab^5,2,*, Eman Elayat⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118

Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.… More >