Analytics of an Imperfect Four-Layer Production Inventory Model Under Two-Level Credit Period Using Branch-and-Bound Technique

Panja, Subrata; Mondal, Shyamal Kumar

doi:10.1007/s40305-020-00300-1

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…When the number of component filters is increased to 8, the accuracy reaches 95.67%, and the accuracy of human motion recognition is improved by more than 11%. However, when the number of filters reaches more than 8, the improvement of accuracy is no longer obvious, which also brings some In order to solve the problem of excessive workload of DPM artificial design features, this paper integrates Branch and Bound (BB) algorithm [21] into DPM model to locate human body. Branch represents that the whole image is divided into several small regions, and the score value of each branch in the small region is calculated respectively.…”

Section: Proposed Feature Extractionmentioning

confidence: 99%

Feature extraction of dance movement based on deep learning and deformable part model

Gao¹,

Wang²

2018

ICST Transactions on Scalable Information Systems

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In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.

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Section: Proposed Feature Extractionmentioning

confidence: 99%