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Zeqin Tian, Dengfeng Chen and Liang Zhao
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large ...
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Wenny Hojas-Mazo, Francisco Maciá-Pérez, José Vicente Berná Martínez, Mailyn Moreno-Espino, Iren Lorenzo Fonseca and Juan Pavón
Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce...
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Weihan Huang, Ke Gao and Yu Feng
Predicting earthquakes through reasonable methods can significantly reduce the damage caused by secondary disasters such as tsunamis. Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamic...
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Dthenifer Cordeiro Santana, Gustavo de Faria Theodoro, Ricardo Gava, João Lucas Gouveia de Oliveira, Larissa Pereira Ribeiro Teodoro, Izabela Cristina de Oliveira, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Job Teixeira de Oliveira and Paulo Eduardo Teodoro
Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processin...
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Sorin Zoican, Roxana Zoican, Dan Galatchi and Marius Vochin
This paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. Neural networks based on graphs have the advantage of capturi...
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