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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Raziyeh AmirTeymoori, Seyed AbdolMajid Jalaee, Mohsen ZayandehRoodi
Pág. 124 - 134
The synchronization of business cycles is one of the new topics that have been raised in recent decades in the field of international business at the same time of increased economic integration between countries. Accordingly, considering the influenced I...
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Jingbei Sun, Huimin Li, Wenming Lin and Yijun He
Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly...
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Jun Li, Javed Iqbal Tanoli, Miao Zhou and Filip Gurkalo
Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis f...
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Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user?s dissatisfactio...
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Haobin Wen, Long Zhang and Jyoti K. Sinha
On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often results in secondary...
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Vinh Pham, Maxim Tyan, Tuan Anh Nguyen and Jae-Woo Lee
Multi-fidelity surrogate modeling (MFSM) methods are gaining recognition for their effectiveness in addressing simulation-based design challenges. Prior approaches have typically relied on recursive techniques, combining a limited number of high-fidelity...
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Saile Zhang, Qingzhen Yang, Rui Wang and Xufei Wang
The use of traditional optimization methods in engineering design problems, specifically in aerodynamic and infrared stealth optimization for engine nozzles, requires a large number of objective function evaluations, therefore introducing a considerable ...
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Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
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Sofía Ramos-Pulido, Neil Hernández-Gress and Gabriela Torres-Delgado
Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on infor...
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