Generative Adversarial Networks in Time Series Analysis and Change Detections Using Remote Sensing DOI
Rufai Yusuf Zakari, Wasswa Shafik

Advances in geospatial technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 257 - 290

Published: April 30, 2025

This chapter explores the application of generative adversarial networks (GANs) in time series analysis and change detection using remote sensing imagery. It provides an overview GANs, covering their architecture, training, applications, before discussing importance for monitoring environmental changes like deforestation urban expansion. The demonstrates how GANs can be adapted tasks such as data augmentation, anomaly detection, predictive modeling, addressing challenges scarcity. also examines integrating with imagery enhances subtle temporal changes. Practical aspects, including preprocessing, model selection, performance evaluation, are discussed, along ethical concerns privacy bias. concludes by highlighting GANs' potential to transform proposing future research directions.

Language: Английский

A comprehensive study on the interplay between dataset characteristics and oversampling methods DOI
Yue Yang, Tian Fang,

Jinyang Hu

et al.

Journal of the Operational Research Society, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 16, 2025

Language: Английский

Citations

0
SMOTE oversampling algorithm based on generative adversarial network DOI
Yu Liu, Qicheng Liu

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0
Hybrid oversampling technique for imbalanced pattern recognition: Enhancing performance with borderline synthetic minority oversampling and generative adversarial networks DOI Creative Commons
Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu

et al.

Machine Learning with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 100637 - 100637

Published: March 1, 2025

Language: Английский

Citations

0
Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging DOI Creative Commons
Tran Anh Tuan, Tal Zeevi, Seyedmehdi Payabvash

et al.

BioMedInformatics, Journal Year: 2025, Volume and Issue: 5(2), P. 20 - 20

Published: April 14, 2025

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, timely clinical decisions. Despite its promise, challenges such as image heterogeneity across different centers, variability in acquisition protocols scanners, sensitivity to artifacts hinder the reliability integration of models. Addressing these issues is critical for ensuring accurate practical AI-powered neuroimaging applications. We reviewed summarized strategies improving robustness generalizability segmentation classification neuroimages. This review follows a structured protocol, comprehensively searching Google Scholar, PubMed, Scopus studies on neuroimaging, task-specific applications, model attributes. Peer-reviewed, English-language brain imaging were included. The extracted data analyzed evaluate implementation effectiveness techniques. study identifies key enhance including regularization, augmentation, transfer learning, uncertainty estimation. These approaches address major domain shifts, consistent performance diverse settings. technical this can improve their real-world practice.

Language: Английский

Citations

0
Generative Adversarial Networks in Time Series Analysis and Change Detections Using Remote Sensing DOI
Rufai Yusuf Zakari, Wasswa Shafik

Advances in geospatial technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 257 - 290

Published: April 30, 2025

This chapter explores the application of generative adversarial networks (GANs) in time series analysis and change detection using remote sensing imagery. It provides an overview GANs, covering their architecture, training, applications, before discussing importance for monitoring environmental changes like deforestation urban expansion. The demonstrates how GANs can be adapted tasks such as data augmentation, anomaly detection, predictive modeling, addressing challenges scarcity. also examines integrating with imagery enhances subtle temporal changes. Practical aspects, including preprocessing, model selection, performance evaluation, are discussed, along ethical concerns privacy bias. concludes by highlighting GANs' potential to transform proposing future research directions.

Language: Английский

Citations

0