Titlebook: Explainable AI and Other Applications of Fuzzy Techniques; Proceedings of the 2 Julia Rayz,Victor Raskin,Vladik Kreinovich Conference proce

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Privacy in Location-Sensing Technologiesnd we also analyze which fuzzy techniques are most appropriate for this purpose. Interestingly, it turns out that the answer depends on what problem we are solving: e.g., different “and”- and “or”-operations are preferable when we are controlling a single object and when we are controlling a group of objects.

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Why Fuzzy Techniques in Explainable AI? Which Fuzzy Techniques in Explainable AI?,nd we also analyze which fuzzy techniques are most appropriate for this purpose. Interestingly, it turns out that the answer depends on what problem we are solving: e.g., different “and”- and “or”-operations are preferable when we are controlling a single object and when we are controlling a group of objects.

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Conference proceedings 2022 make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general..

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Set-Valued and Lyapunov Methods for MPC the tree are tuned using a genetic algorithm (GA). The results of the approach are quantified using accuracy, F. score, true positive rate, and true negative rate, then compared to the results obtained by a variety of different algorithms on the same dataset.

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Simulation in Force Spectroscopy,churning (given that they are unsatisfied with the delivery). The prediction algorithms for negative emotions are shown to achieve good results when tested with out-of-sample comments coming from social networks, and the complete methodology obtains significant savings when applied for e-commerce customer support.

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A Genetic Fuzzy Approach for the Prediction of Heart Failure, the tree are tuned using a genetic algorithm (GA). The results of the approach are quantified using accuracy, F. score, true positive rate, and true negative rate, then compared to the results obtained by a variety of different algorithms on the same dataset.