SwinRes: A hybrid model that effectively diagnoses COVID‐19 through x‐ray lung images DOI

Xuanlong He,

Hong Yang,

Jipan Xu

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(3)

Published: May 1, 2024

Abstract COVID‐19 has been ravaging the world for a long time, and although its effects are currently same as those of cold or fever, timely diagnosis in elderly patients with related illnesses is still matter great urgency. To address this challenge, we propose model that combines strengths Swin Transformer ResNet34 architectures to efficiently diagnose vulnerable patients. In paper, design integrates transformer resnet34, which not only advantages CNN but also achieves excellent performance image classification problem. Moreover, pre‐processing method proposed increase accuracy 99.08%. experiments were conducted on Kaggle's publicly available three‐classification four‐classification datasets, respectively, three main evaluation metrics Accuracy, Precision, Recall, first dataset obtained 98.81%, 99.49%, 97.99%, while second 88.82%, 88.92%, 86.38%. These findings highlight validity potential our diagnosing presence absence

Language: Английский

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering DOI Creative Commons
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(21), P. 12655 - 12699

Published: May 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Language: Английский

Citations

21
A machine vision approach with temporal fusion strategy for concrete vibration quality monitoring DOI
Tan Li, Hong Wang,

Dongxu Pan

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 160, P. 111684 - 111684

Published: May 1, 2024

Language: Английский

Citations

6
Research on Titanic Survival Prediction Based on Machine Learning Method DOI Creative Commons

Yongzhong Liao,

Shimiao Zhang,

Zixin Zhang

et al.

Advances in Economics Management and Political Sciences, Journal Year: 2025, Volume and Issue: 152(1), P. 152 - 162

Published: Jan. 6, 2025

On April 15, 1912, the British luxury passenger ship Titanic sank on its maiden voyage from Southampton to New York because of a collision with an iceberg, resulting in death 1502 out 2224 passengers and crew. This article gains insight into factors that influence survival rate establish model hard voting consisting logistic regression, random forest decision tree predict what sort people are more likely survive this catastrophe. The process involves dealing missing values, creating new variables by feature engineering fitting dataset. overall performs well accuracy 87.64%. By applying navigation field, data can be collected precise predictions made. results also help individuals risk try decrease them as much possible while robustness stability still need refined.

Language: Английский

Citations

0
Classifying chest x-rays for COVID-19 through transfer learning: a systematic review DOI
Devanshi Mallick, Arshdeep Singh, E. Y. K. Ng

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

Language: Английский

Citations

2
SwinRes: A hybrid model that effectively diagnoses COVID‐19 through x‐ray lung images DOI

Xuanlong He,

Hong Yang,

Jipan Xu

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(3)

Published: May 1, 2024

Abstract COVID‐19 has been ravaging the world for a long time, and although its effects are currently same as those of cold or fever, timely diagnosis in elderly patients with related illnesses is still matter great urgency. To address this challenge, we propose model that combines strengths Swin Transformer ResNet34 architectures to efficiently diagnose vulnerable patients. In paper, design integrates transformer resnet34, which not only advantages CNN but also achieves excellent performance image classification problem. Moreover, pre‐processing method proposed increase accuracy 99.08%. experiments were conducted on Kaggle's publicly available three‐classification four‐classification datasets, respectively, three main evaluation metrics Accuracy, Precision, Recall, first dataset obtained 98.81%, 99.49%, 97.99%, while second 88.82%, 88.92%, 86.38%. These findings highlight validity potential our diagnosing presence absence

Language: Английский

Citations

0