We focus on the problem of locating one sink on balanced binary tree networks with uniform edge capacities, all while minimizing the total evacuation time for all evacuees (minsum criterion). The challenge with sink l...
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One of the unspoken challenges of tractography is choosing the right parameters for a given dataset or bundle. In order to tackle this challenge, we explore the multi-dimensional parameter space of tractography using ...
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This paper addresses graph topology identification for applications where the underlying structure of systems like brain and social networks is not directly observable. Traditional approaches based on signal matching ...
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Given a directed graph, we present and experimentally validate new heuristics to find good linear orderings of its vertices so to obtain Feedback Arc Sets which are minimal, i.e. such that none of the arcs can be rein...
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We study a natural application of contract design in the context of sequential exploration problems. In our principal-agent setting, a search task is delegated to an agent. The agent performs a sequential exploration ...
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Artificial intelligence and machine learning have been on a sharp rise in the last few years. One reason behind their popularity, is their usefulness as they can be seen in multiple fields including medicine and biolo...
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This paper aims to contribute to the literature on planning and implementing mathematical modelling tasks in the higher education curriculum. Specifically, we propose and discuss a lesson plan carried out within the c...
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The development of digital payment technology, such as Gopay, has provided convenience for the Indonesian public. To understand user satisfaction and identify key issues, this study analyzes Gopay user reviews using a...
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This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ...
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This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.
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