MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network

Xuanyi Wang

doi:10.47297/wspciWSP2516-252704.20220606

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MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network

Volume 6, Issue 6

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MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network
MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network
Jinling College of Nanjing University,Jiangsu,Nanjing,P.R. China,210000
Vol. 6, Issue 6, Pages: 20-26(2022)
Published： 2022 ，
DOI：10.47297/wspciWSP2516-252704.20220606
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XUANYI WANG. (2022). MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network. Creativity and Innovation, 6(6), 20-26..
DOI：

XUANYI WANG. (2022). MF-NET: The Application of Male and Female Identification in Innovative Catering with FM Neural Network. Creativity and Innovation, 6(6), 20-26.. DOI： 10.47297/wspciWSP2516-252704.20220606.

摘要

Abstract

Nowadays

with the development of technology. Many useful techniques and devices were invented improving the quality of people’s lives. However

traditional catering still stick to old ways. There are many waiters in the restaurants

which costs the owners plentiful money. Besides

people take the good services for granted and most of customers will not appeal to the good services. Also

waiters get tired easily and are emotional

which cannot guarantee the quality of service is stable. So

the money spent is meaningless. In additional

traditional industries need combination of the creativity

which stimulates the consumption. The Male and Female Identification Neural Network (MF-NET) can avoid unnecessary cost and is qualified to do this job. Without the restriction of hardware performance

machine learning and deep learning are improving their performance

which has already used in production

truly improving capacity of the society. In this paper

based on the public large-scale data of the people’s face labelled with the true gender label. We get the efficient network MF-NET after it gets trained many times with high precise rate. The kernels of MF-NET are K-nearest Neighbor (KNN) and Convolutional neural network (CNN)

which has been used widely in classifying photos with high efficiency. In the experiment

we debug the different parameters

including the epoch

learning rate and the batch size. Comparing different results with the different parameters

we get the high accuracy model

which can be combined with the robots easily. Extensive experiments on the LWF dataset demonstrate that MF-NET has a good performance in classifying males and females

which can truly help the traditional catering.

关键词

Keywords

CNNKNNMachine learningDeep learning

references

Guo, G., Hu, Y., & Fang, Y. (2020). Transformation and sustainable development of traditional catering industry after COVID-19. In 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020). Atlantis. 254-257.[2] Peng, Z. (2022). New media marketing strategy optimization in the catering industry based on deep machine learning algorithms. Journal of Mathematics, 2022: 1-10.[3] Byrd, K., Alei, F., Her, E., et al. (2021). Robot vs. human: expectations, performances and gaps in off-premise restaurant service modes. International Journal of Contemporary Hospitality Management, 2021: 3996-4016.[4] Burton, A. M., Bruce, V., & Dench, N. (1993). What’s the difference between men and women? Evidence from facial measurement. Perception, 22(2): 153-176.[5] El Naqa, I., & Murphy, M. J. (2015). What is machine learning?. In El Naqa, I., & Murphy, M. J. (Eds.), Machine learning in radiation oncology. Springer, Berlin, Heidelberg. 3-11.[6] Guo, Y., Liu, Y., Oerlemans, A., et al. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187: 27-48.[7] Huang, S., Cai, N., Pacheco, P. P., et al. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15(1): 41-51.[8] Guo, G., Wang, H., Bell, D., et al. (2003). KNN model-based approach in classification. In Meersman, R., Dillon, T., & Herrero, P. (Eds.) OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. Springer, Berlin, Heidelberg. 986-996.[9] Lei, X., Pan, H., & Huang, X. (2019). A dilated CNN model for image classification. IEEE Access, 7: 124087-124095.[10]Kumar, N., Berg, A., Belhumeur, P.N., et al. (2011). Describable visual attributes for face verification and image search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10): 1962-1977.

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