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Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis and Omiros Ragos
Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive per...
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Huang Feng and Yu Zhang
Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput...
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Chunru Cheng, Linbing Wang, Xingye Zhou and Xudong Wang
As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores f...
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Junling Zhang, Min Mei, Jun Wang, Guangpeng Shang, Xuefeng Hu, Jing Yan and Qian Fang
The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engine...
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Jiale Li, Jiayin Guo, Bo Li and Lingxin Meng
The deep learning method has been widely used in the engineering field. The availability of the training dataset is one of the most important limitations of the deep learning method. Accurate prediction of pavement performance plays a vital role in road ...
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Leonardo Emiro Contreras Bravo, Nayibe Nieves-Pimiento, Karolina Gonzalez-Guerrero
Pág. e19514
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Leonardo Emiro Contreras Bravo, Nayibe Nieves-Pimiento, Karolina Gonzalez-Guerrero
Pág. e19514
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Lauren M. Paladino, Alexander Hughes, Alexander Perera, Oguzhan Topsakal and Tahir Cetin Akinci
Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing mac...
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Thao-Trang Huynh-Cam, Long-Sheng Chen and Khai-Vinh Huynh
The learning performance of international students and students with disabilities has increasingly attracted many theoretical and practical researchers. However, previous studies used questionnaires, surveys, and/or interviews to investigate factors affe...
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Chaity Mondol, F. M. Javed Mehedi Shamrat, Md. Robiul Hasan, Saidul Alam, Pronab Ghosh, Zarrin Tasnim, Kawsar Ahmed, Francis M. Bui and Sobhy M. Ibrahim
Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural...
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Ke Li, Hai Li, Shaopeng Li and Zengshun Chen
The shape of a bluff body section is of high importance to its aerostatic performance. Obtaining the aerostatic performance of a specific shape based on wind tunnel tests and CFD simulations takes a lot of time, which affects evaluation efficiency. This ...
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Gexue Bai, Yunlong Hou, Baofeng Wan, Ning An, Yihao Yan, Zheng Tang, Mingchun Yan, Yihan Zhang and Daoyuan Sun
This study provides a straightforward method to determine the machine learning model with the best predictive performance and demonstrates a complete model building solution for predicting the factor of safety in slope engineering.
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Ahmet Emin Tatar and Dilek Düstegör
Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individu...
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Sunbok Lee and Jae Young Chung
A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. Howev...
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Jingjing Liu, Jinkun Yang, Kexiu Liu and Lingyu Xu
Ocean current (OC) prediction plays an important role for carrying out ocean-related activities. There are plenty of studies for OC prediction with deep learning to pursue better prediction performance, and the attention mechanism was widely used for the...
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Eunwoo Kim
Multi-task learning (MTL) is a learning strategy for solving multiple tasks simultaneously while exploiting commonalities and differences between tasks for improved learning efficiency and prediction performance. Despite its potential, there remain sever...
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Juan Murillo-Morera, Carlos Castro-Herrera, Javier Arroyo, Ruben Fuentes-Fernandez
Pág. 114 - 137
Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding so...
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Kanghyeok Lee, Changhyun Choi, Do Hyoung Shin and Hung Soo Kim
Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was ...
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Runjuan Zhou, Kuo Zhang and Ming Zhang
A back-propagation neural network (BPNN) was used to model and optimize the process of hydroxylamine (HA)-enhanced Fe2+ activating peroxymonosulfate (PMS). Using HA-enhanced Fe2+ to activate PMS is a cost-effective method to degrade orange II (AO7). We i...
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Tiago Tamagusko and Adelino Ferreira
Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically ...
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