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Sipho G. Thango, Georgios A. Drosopoulos, Siphesihle M. Motsa and Georgios E. Stavroulakis
A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex d...
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Luis Adán Félix-Salazar, Emigdio Marín-Enríquez, Eugenio Alberto Aragón-Noriega and Jorge Saul Ramirez-Perez
During the last 50 years, the increase in the efforts of the longline fleet in the Eastern Pacific Ocean (EPO) resulted in an increase in the capture of the swordfish Xiphias gladius. We analyzed a historical database of swordfish catches (1980?2020) rep...
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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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Mili Turic, Stipe Celar, Srdjana Dragicevic and Linda Vickovic
Effort estimation is always quite a challenge, especially for agile software development projects. This paper describes the process of building a Bayesian network model for effort prediction in agile development. Very few studies have addressed the appli...
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George Stamatellos and Tassos Stamatelos
In spite of the significant developments in machine learning methods employed for short-term electrical load forecasting on a Country level, the complexity and diversity of the problem points to the need for investing more research effort in the selectio...
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Cem Ünlübayir, Ulrich Hermann Mierendorff, Martin Florian Börner, Katharina Lilith Quade, Alexander Blömeke, Florian Ringbeck and Dirk Uwe Sauer
This research paper presents a data-based energy management method for a vessel that predicts the upcoming load demands based on data from weather information and its automated tracking system. The vessel is powered by a hybrid propulsion system consisti...
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Parisa Mahya and Johannes Fürnkranz
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretabl...
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Tobias Nießner, Stefan Nießner and Matthias Schumann
How can useful information extracted from unstructured data be used to contribute to a better prediction of corporate failure or bankruptcy? In this research, we examine a data set of 2,163,147 financial statements of German companies that are triple cla...
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Enrico Aymerich, Barbara Cannas, Fabio Pisano, Giuliana Sias, Carlo Sozzi, Chris Stuart, Pedro Carvalho, Alessandra Fanni and the JET Contributors
Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (...
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Roman Putter, Andre Neubohn, Andre Leschke and Roland Lachmayer
Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehi...
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