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M.H.J.P. Gunarathna, Kazuhito Sakai, Tamotsu Nakandakari, Kazuro Momii and M.K.N. Kumari
Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for...
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Jianan Yin, Mingwei Zhang, Yuanyuan Ma, Wei Wu, He Li and Ping Chen
Airport arrival and departure movements are characterized by high dynamism, stochasticity, and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi time accurately and scientifically. This paper conducts a comprehensi...
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Fariha Imam, Petr Musilek and Marek Z. Reformat
Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To...
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Juan Ma, Qiang Yang, Mingzhi Zhang, Yao Chen, Wenyi Zhao, Chengyu Ouyang and Dongping Ming
Accurately predicting landslide deformation based on monitoring data is key to successful early warning of landslide disasters. Landslide displacement?time curves offer an intuitive reflection of the landslide motion process and deformation predictions o...
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Rejath Jose, Faiz Syed, Anvin Thomas and Milan Toma
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for...
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Arturs Kempelis, Inese Polaka, Andrejs Romanovs and Antons Patlins
Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor m...
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Cai Wu, Yanwen Wang, Jiong Wang, Menno-Jan Kraak and Mingshu Wang
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised...
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Maryam Badar and Marco Fisichella
Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, et...
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Luana Conte, Emanuele Rizzo, Tiziana Grassi, Francesco Bagordo, Elisabetta De Matteis and Giorgio De Nunzio
Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a co...
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Luis Zuloaga-Rotta, Rubén Borja-Rosales, Mirko Jerber Rodríguez Mallma, David Mauricio and Nelson Maculan
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation in ...
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