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Mojtaba Zaresefat and Reza Derakhshani
Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater managemen...
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Mohamed Chetoui and Moulay A. Akhloufi
The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. ...
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Thomas J. Tewes, Michael C. Welle, Bernd T. Hetjens, Kevin Saruni Tipatet, Svyatoslav Pavlov, Frank Platte and Dirk P. Bockmühl
Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibil...
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Kyriakos Apostolidis, Christos Kokkotis, Evangelos Karakasis, Evangeli Karampina, Serafeim Moustakidis, Dimitrios Menychtas, Georgios Giarmatzis, Dimitrios Tsiptsios, Konstantinos Vadikolias and Nikolaos Aggelousis
Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained partici...
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Laurent Risser, Agustin Martin Picard, Lucas Hervier and Jean-Michel Loubes
The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applic...
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