Titlebook: Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices; 34th International C Hamido Fujita,Ali Selamat,Moonis Al

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Boekblok Cli?nt & medezeggenschap in de zorgequent episodes and to derive episode rules from them with respect to a given frequency function and its properties such as the anti-monotony. However, the interpretation of these rules is often difficult as their occurrences are allowed to overlap. To address this issue, this paper studies the nove

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https://doi.org/10.1007/978-90-313-8646-8onsidered. Mining high utility sequential patterns (HUSP) is an emerging issue and the existing HUSP algorithms can mine the order of items and they do not consider the time interval between the successive items. In real-world applications, time interval patterns provide more useful information than

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Artikelsgewijze toelichting WMCZ,iven tasks continuously. A DNN is typically trained by a single dataset, and continuous learning of subsequent datasets causes the problem of catastrophic forgetting. Previous studies have reported results for consolidation learning methods in recognition tasks and reinforcement learning problems. H

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Artikelsgewijze toelichting WMCZ,ia, severe acute respiratory syndrome, septic shock, failure of multiple organs, and even death. Although such kind of images could be obtained at low cost, the lacking of qualified radiologists limits the exploitation of the X-Ray imaging technology. For these reasons, researchers have proposed the

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https://doi.org/10.1007/978-90-313-8646-8an have inherent biases. We investigate the effects of the interaction between mechanisms designed to preserve privacy and to mitigate biases. The mechanisms employed for this investigation were .-anonymity for de-identification and re-weighting for bias mitigation. The experiments were threefold. F

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Artikelsgewijze toelichting WMCZ,from the cross-correlation between the feature representations of the search image and the target image extracted using CNNs, and tracks based on it. The tracking performance of SiamFC tends to degrade when there are similar distractors to the object or when the target object is deformed. On the oth

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https://doi.org/10.1007/978-90-313-8646-8curacy and resource usage. It has been used for convolution, classification, and regression, producing interpretable rules. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. We combined the value iteration algorithm with the regression Tsetlin M