Multiviewunet: A Deep Learning Surrogate for Wall Shear Stress Prediction in Aortic Aneurysmal Diseases DOI
Md. Ahasan Atick Faisal, Onur Mutlu, Sakib Mahmud

et al.

Published: Jan. 1, 2023

Computational Fluid Dynamics (CFD) analysis is widely used to simulate hemodynamics and investigate the biofluid mechanics of different tissue, whole organs, tissue–medical device interactions. However, CFD simulations are time-consuming computationally expensive; hence not readily available practical for patient-specific time-sensitive clinical applications prohibiting quick responses from clinicians. Disturbed known influence progression many cardiac conditions. Aorta main blood artery in body diseases this vessel very common. One such condition Abdominal Aortic Aneurysm (AAA), where abdominal aorta widens has risk rupture. Precise determination Wall Shear Stress (WSS) on aneurysmal wall essential assess rupture tissue. In study, we have proposed a Deep Learning (DL) surrogate estimating aortic WSS distribution. The DL model was created trained receive input output distributions directly, bypassing procedure. A novel way analyzing geometry-to-geometry problems also using domain transformation, which compatible with existing state-of-the-art Neural Networks (NN). framework, MultiViewUnet, 23 real 230 synthetic geometries. algorithm predicted stress an average Normalized Mean Absolute Error (NMAE) 0.362%. We believe our will open up new dimensions precise levels important.

Language: Английский

Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network DOI
Mohammad Shahin,

Mazdak Maghanaki,

Ali Hosseinzadeh

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 133(11-12), P. 5343 - 5419

Published: July 2, 2024

Language: Английский

Citations

9
The Transformative Trajectory of Artificial Intelligence in Education: The Two Decades of Bibliometric Retrospect DOI

K. Kavitha,

V. P. Joshith

Journal of Educational Technology Systems, Journal Year: 2024, Volume and Issue: 52(3), P. 376 - 405

Published: March 1, 2024

Integrating artificial intelligence (AI) stands out as the most dynamic and innovative breakthrough in introducing disruptive paths varied domains of education. This bibliometric analysis delved into trajectory AI’s evolving landscape within educational settings over two decades, encompassing 324 articles published from 2003 to 2023, sourced Scopus database. The study uncovers a substantial surge publications with steep increase 2020, peaking 2023. Notably, while established nations like China US lead publications, notable contributions other developing countries, including Saudi Arabia, India, Malaysia, underscored global shift. Key terms, students, machine learning, AI higher education, underpin central focus research areas emerging themes “generative AI” chatbots “chatgpt” mark trends. Further, prompts sustained partnerships, interdisciplinary collaborations, continued exploration technologies catalyze advancements.

Language: Английский

Citations

7
Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD DOI Creative Commons
Md. Ahasan Atick Faisal, Onur Mutlu, Sakib Mahmud

et al.

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone Therefore, precise WSS determination on the aneurysm is crucial for rupture assessment. Computational fluid dynamics (CFD) powerful approach calculations, but they computationally intensive, hindering time-sensitive clinical decision-making. In this study, we propose deep learning (DL) surrogate, MultiViewUNet, rapidly predict time-averaged (TAWSS) distributions abdominal aortic (AAA). Our novel employs domain transformation technique translate complex geometries into representations compatible with state-of-the-art neural networks. MultiViewUNet was trained $$\varvec{23}$$ 23 real and $$\varvec{230}$$ 230 synthetic AAA geometries, demonstrating an average normalized mean absolute error (NMAE) just $$\varvec{0.362\%}$$ 0.362 % in prediction. This framework potential streamline hemodynamic analysis other scenarios where fast accurate quantification essential. Graphical abstract

Language: Английский

Citations

0
Interpretable and ethical learning assessment transformer (IELAT): an explainable transformer model for personalized student assessments DOI Creative Commons

S. Hariharasitaraman,

Amudhavel Jayavel,

S. Gnanasekaran

et al.

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 8, 2025

Language: Английский

Citations

0
A Plug-in for Cognitive Diagnosis Method based on Correlation Representation under Long-tailed Distribution DOI
Yuhong Zhang, Tiancheng He, Shengyu Xu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127952 - 127952

Published: April 1, 2025

Language: Английский

Citations

0
Assessing english Language teachers’ pedagogical effectiveness using convolutional neural networks optimized by modified virus colony search algorithm DOI Creative Commons
Zhang Li

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 1, 2025

Language: Английский

Citations

0
University Education of the Future: Students’ Perspective DOI
Tatiana Dyukina, Iuliia Diukina, Т. В. Терентьева

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 165 - 177

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mathematics intelligent tutoring systems with handwritten input: a scoping review DOI
Luiz Rodrigues, Filipe Dwan Pereira, Marcelo Marinho

et al.

Education and Information Technologies, Journal Year: 2023, Volume and Issue: 29(9), P. 11183 - 11209

Published: Oct. 18, 2023

Language: Английский

Citations

7
Blending Shapley values for feature ranking in machine learning: an analysis on educational data DOI
Pratiyush Guleria

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(23), P. 14093 - 14117

Published: May 4, 2024

Language: Английский

Citations

2
Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms DOI Creative Commons
Xiaochen Zhang,

Yiran Ding,

Xiaoyu Huang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5487 - 5487

Published: Aug. 24, 2024

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in few studies focus on the AI developing smart classrooms. This systematic review classifies sensors used classrooms explores current applications from both hardware software perspectives. It delineates how different enhance educational outcomes crucial role play. highlights technology improves physical classroom environment, monitors physiological behavioral data, widely boost student engagements, manage attendance, provide personalized learning experiences. Additionally, it shows that combining algorithms with not only enhances data processing analysis efficiency but also expands capabilities, enriching article addresses challenges such as privacy protection, cost, algorithm optimization associated emerging proposing future directions advance technologies.

Language: Английский

Citations

2