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Viacheslav Moskalenko, Vyacheslav Kharchenko, Alona Moskalenko and Sergey Petrov
Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on m...
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Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri and Paulo Cortez
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening pr...
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Aleksei Vakhnin and Evgenii Sopov
Modern real-valued optimization problems are complex and high-dimensional, and they are known as ?large-scale global optimization (LSGO)? problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dim...
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Mathias Kühn, Michael Völker and Thorsten Schmidt
Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domai...
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Miguel del Alamo, Housen Li, Axel Munk and Frank Werner
Many modern statistically efficient methods come with tremendous computational challenges, often leading to large-scale optimisation problems. In this work, we examine such computational issues for recently developed estimation methods in nonparametric r...
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