Titlebook: Urban Intelligence and Applications; Second International Xiaohui Yuan,Mohamed Elhoseny,Jianfang Shi Conference proceedings 2020 Springer N

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Axon895

A Task-Aware Network for Multi-task Learningthods by reducing the average error by as much as 25%. Our method also exhibits a greater consistency for different tasks. By training variations of our proposed MTN, we observed that MTN-3 achieved the best performance with a cumulative error rate of 1.87% and 5.7% reduction in average error.

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Recommendation Based on Attention Degree and Entropyon L-HUMCF recommended to improve, as a result, joined the user of information entropy, EL-HUMCF algorithm is proposed, and experimental verification algorithm is effective. Experimental results show that the proposed algorithm is better than other personalized recommendation algorithms.

眼界

Point Set Registration of Large Deformation Using Auxiliary Landmarksdegree of deformations. In particular, quantitative results show our method is 49% better than the second best result (from the state-of-the-art methods). Finally, we demonstrate the importance of using correct landmark correspondences in registration by showing good registration results in large an

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Brain Tumor Segmentation Algorithm Based on Attention Mechanism and Hybrid Cascaded Networksegmentation results. Brain MRI images of 285 patients which were provided by the BraTS2017 Competition were used to segment brain tumors as well as their sub-regions. The accuracy of segmenting the whole tumor region, the tumor core region, and the enhancing tumor region were 0.907, 0.836, and 0.74

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