K. Sinivasan¹, N. Vishnu Ganesh^1,*, G. Hirankumar², M. Al-Mdallal Qasem^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1029-1049, 2025, DOI:10.32604/fdmp.2025.064503 - 30 May 2025

Abstract In this study, an analytical investigation is carried out to assess the impact of magnetic field-dependent (MFD) viscosity on the momentum and heat transfers inside the boundary layer of a Jeffrey fluid flowing over a horizontally elongating sheet, while taking into account the effects of ohmic dissipation. By applying similarity transformations, the original nonlinear governing equations with partial derivatives are transformed into ordinary differential equations. Analytical expressions for the momentum and energy equations are derived, incorporating the influence of MFD viscosity on the Jeffrey fluid. Then the impact of different parameters is assessed, including magnetic More >

Nguyen Khac Toan¹, Ho Nguyen Anh Tuan², Nguyen Truong Thinh^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1273-1295, 2025, DOI:10.32604/cmes.2025.064218 - 30 May 2025

Abstract This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods. The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery. The approach leverages advanced image processing techniques, 3D Morphable Models (3DMM), and deformation techniques to overcome the limitations of deep learning models, particularly addressing the interpretability issues commonly encountered in medical applications. The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm. Sub-landmarks… More > Graphic Abstract

Xinchao Han^1,2, Aojun Zhang^1,2, Runchuan Li^1,2,*, Shengya Shen³, Di Zhang^1,2, Bo Jin^1,2, Longfei Mao^1,2, Linqi Yang^1,2, Shuqin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403 - 17 February 2025

Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >

Manouk H. C. Linderhof^1,#, Hoang H. Nguyen^1,2,#, Annemien E. van den Bosch¹, Mathijs S. van Schie¹, Vehpi Yildirim¹, Yannick J. H. J. Taverne³, Natasja M. S. de Groot^1,*

Congenital Heart Disease, Vol.19, No.6, pp. 577-592, 2024, DOI:10.32604/chd.2025.062129 - 27 January 2025

Abstract Surgical ablation (SA) has become an essential rhythm-control strategy for managing tachyarrhythmias in patients with congenital heart disease. Atrial tachyarrhythmias, such as atrial flutter and atrial fibrillation, are prevalent in congenital heart disease, affecting up to 50% of patients, and pose significant risks, including increased morbidity and mortality. Ventricular tachyarrhythmias, though less common, can lead to sudden cardiac death, particularly in conditions like Tetralogy of Fallot. Prior studies suggested that SA for tachyarrhythmias in patients with congenital heart disease offers significant benefits, including superior long-term rhythm control compared to catheter ablation (CA). Atrial tachyarrhythmia burden… More > Graphic Abstract

Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

Sahar Anwar¹, Ghulam Rasool^2,*, Muhammad Ashraf¹, Uzma Ahmad¹, Tao Sun^2,*

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1323-1341, 2024, DOI:10.32604/fhmt.2024.055314 - 30 October 2024

Abstract The present investigation centers on the impact of viscous dissipation and ohmic heating on the plume generated by a line heat source under the impact of an aligned magnetic field. In this study, the flow model is adapted to incorporate ohmic heating and viscous dissipation by including the respective terms in the energy equation. A mathematical model is formulated as a system of coupled partial differential equations to analyze the flow problem. Subsequently, a numerical solution is derived with stream function formulation for the system of coupled partial differential equations, which transmutes it into ordinary… More > Graphic Abstract

Abbas Ali Hassan, Fardin Abdali-Mohammadi^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >

Haris Alam Zuberi¹, Madan Lal¹, Shivangi Verma¹, Nurul Amira Zainal^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1137-1163, 2024, DOI:10.32604/cmes.2024.055493 - 27 September 2024

Abstract Motivated by the widespread applications of nanofluids, a nanofluid model is proposed which focuses on uniform magnetohydrodynamic (MHD) boundary layer flow over a non-linear stretching sheet, incorporating the Casson model for blood-based nanofluid while accounting for viscous and Ohmic dissipation effects under the cases of Constant Surface Temperature (CST) and Prescribed Surface Temperature (PST). The study employs a two-phase model for the nanofluid, coupled with thermophoresis and Brownian motion, to analyze the effects of key fluid parameters such as thermophoresis, Brownian motion, slip velocity, Schmidt number, Eckert number, magnetic parameter, and non-linear stretching parameter on… More > Graphic Abstract

Saket Sarin¹, Sunil K. Singh¹, Sudhakar Kumar¹, Shivam Goyal¹, Brij Bhooshan Gupta^2,3,4,8,*, Wadee Alhalabi⁵, Varsha Arya^6,7

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599 - 15 August 2024

Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >