Application of machine learning for antibiotic resistance in water and wastewater: A systematic review DOI
Maryam Foroughi,

Afrooz Arzehgar,

Seyedeh Nahid Seyedhasani

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 358, P. 142223 - 142223

Published: May 2, 2024

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
A critical review of safe reinforcement learning strategies in power and energy systems DOI
Van‐Hai Bui, Sina Mohammadi,

Srijita Das

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 143, P. 110091 - 110091

Published: Jan. 22, 2025

Language: Английский

Citations

1
Digital twin-inspired methods for rotating machinery intelligent fault diagnosis and remain useful life prediction: A state-of-the-art review and future challenges DOI
Kun Yu, Caizi Fan, Yongchao Zhang

et al.

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 232, P. 112770 - 112770

Published: April 21, 2025

Language: Английский

Citations

1
Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning DOI

Pengyi Deng,

Xiaohua Wu,

Jialuo Yang

et al.

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 300, P. 117921 - 117921

Published: Nov. 29, 2023

Language: Английский

Citations

22
Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization DOI
Xiaobing Yu,

Yangchen Lu

Energy, Journal Year: 2023, Volume and Issue: 284, P. 129300 - 129300

Published: Oct. 6, 2023

Language: Английский

Citations

20
Synergy between Artificial Intelligence and Hyperspectral Imagining—A Review DOI Creative Commons
Svetlana N. Khonina, Nikolay L. Kazanskiy, Ivan Oseledets

et al.

Technologies, Journal Year: 2024, Volume and Issue: 12(9), P. 163 - 163

Published: Sept. 13, 2024

The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields. By leveraging AI, the processing interpretation vast complex data generated by HSI are significantly enhanced, allowing for more accurate, efficient, insightful analysis. This powerful combination has to revolutionize key areas such as agriculture, environmental monitoring, medical diagnostics providing precise, real-time insights that were previously unattainable. In instance, AI-driven can enable precise crop monitoring disease detection, optimizing yields reducing waste. this technology track changes in ecosystems with unprecedented detail, aiding conservation efforts disaster response. diagnostics, AI-HSI could earlier accurate improving patient outcomes. As AI algorithms advance, their integration is expected drive innovations enhance decision-making various sectors. continued development these technologies likely open new frontiers scientific research practical applications, accessible tools wider range users.

Language: Английский

Citations

8
Flexible recommendation for optimizing the debt collection process based on customer risk using deep reinforcement learning DOI

Keerthana Sivamayilvelan,

R Elakkiya,

V. Subramaniyaswamy

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124951 - 124951

Published: July 30, 2024

Language: Английский

Citations

6
Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach DOI
Xuesong Xu, Kai Xu, Ziyang Zeng

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 375, P. 123923 - 123923

Published: Aug. 13, 2024

Language: Английский

Citations

6
Transportation mode detection through spatial attention-based transductive long short-term memory and off-policy feature selection DOI Creative Commons

Mahsa Merikhipour,

Shayan Khanmohammadidoustani,

Muhammad Daud Abbasi

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 267, P. 126196 - 126196

Published: Dec. 25, 2024

Language: Английский

Citations

6
Principles of artificial intelligence in radiooncology DOI Creative Commons
Yixing Huang, Ahmed M. Gomaa,

Daniel Höfler

et al.

Strahlentherapie und Onkologie, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 6, 2024

Abstract Purpose In the rapidly expanding field of artificial intelligence (AI) there is a wealth literature detailing myriad applications AI, particularly in realm deep learning. However, review that elucidates technical principles learning as relevant to radiation oncology an easily understandable manner still notably lacking. This paper aims fill this gap by providing comprehensive guide specifically tailored toward oncology. Methods light extensive variety AI methodologies, selectively concentrates on specific domain It emphasizes principal categories models and delineates methodologies for training these effectively. Results initially distinctions between well supervised unsupervised Subsequently, it fundamental major models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent (RNNs), transformers, generative adversarial (GANs), diffusion-based reinforcement For each category, presents representative alongside their Moreover, outlines critical factors essential such data preprocessing, loss functions, optimizers, other pivotal parameters including rate batch size. Conclusion provides overview enhance understanding AI-based research software applications, thereby bridging complex technological concepts clinical practice

Language: Английский

Citations

5