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Titlebook: Advances in Artificial Intelligence; 20th Conference of t Amparo Alonso-Betanzos,Bertha Guijarro-Berdi?as,Al Conference proceedings 2024 Th

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樓主: 毛發(fā)
21#
發(fā)表于 2025-3-25 05:01:02 | 只看該作者
22#
發(fā)表于 2025-3-25 11:28:30 | 只看該作者
23#
發(fā)表于 2025-3-25 12:18:57 | 只看該作者
24#
發(fā)表于 2025-3-25 17:14:13 | 只看該作者
25#
發(fā)表于 2025-3-25 19:58:02 | 只看該作者
26#
發(fā)表于 2025-3-26 01:11:35 | 只看該作者
,Deep Variational Auto-Encoder for?Model-Based Water Quality Patrolling with?Intelligent Surface Vehithm, exploiting the submodularity of the problem, demonstrates a 41% and 55% performance improvement over algorithms without UNet-VAE. This method enhances monitoring coverage and intensification of high-interest areas, providing a promising approach for hydrological resource surveillance.
27#
發(fā)表于 2025-3-26 05:43:54 | 只看該作者
28#
發(fā)表于 2025-3-26 10:28:39 | 只看該作者
29#
發(fā)表于 2025-3-26 14:31:17 | 只看該作者
A Surrogate Assisted Approach for Fitness Computation in Robust Optimization over Time,t this approach can achieve significantly superior performances to the existing framework, especially for specific surrogate model configurations. Furthermore, we show that in certain cases where our algorithms are less efficient than the existing approach, such inefficiency is compensated by improvements in error.
30#
發(fā)表于 2025-3-26 20:15:14 | 只看該作者
,An Experimental Comparison of?Qiskit and?Pennylane for?Hybrid Quantum-Classical Support Vector Machnce of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning.
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