A Semantic Segmentation based POI Coordinates Generating Framework for On-demand Food Delivery Service

2021 ◽  
Author(s):  
Yatong Song ◽  
Jiawei Li ◽  
Liying Chen ◽  
Shuiping Chen ◽  
Renqing He ◽  
...  
Author(s):  
Tiurida Lily Anita ◽  
Arif Zulkarnain ◽  
Amia Luthfia ◽  
Sari Ramadanty ◽  
Abdul Rauf Ridzuan

2021 ◽  
pp. 107871
Author(s):  
Aysun Bozanta ◽  
Mucahit Cevik ◽  
Can Kavaklioglu ◽  
Eray M. Kavuk ◽  
Ayse Tosun ◽  
...  

2013 ◽  
Vol 17 ◽  
pp. 96-103
Author(s):  
Rong-Chang Chen ◽  
Chih-Hui Shieh ◽  
Kai-Ting Chan ◽  
Shin-Yi Chiu ◽  
Jyun-You Fan ◽  
...  

Author(s):  
Mrinal Malkar ◽  
Sai Karthik Nandigama ◽  
Anudeepa Kholapure ◽  
Yash Pawse ◽  
Vaibhavi Amal

2016 ◽  
Vol 35 (Special_Issue) ◽  
pp. 201-206
Author(s):  
Satoshi TSUCHIYA ◽  
Keishi TANIMOTO ◽  
Hiromi KURAMOCHI

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Irfan Nasrullah ◽  
Rila Mandala

In this research, the case of intent classification for Customer Relation Management (CRM) how to handle complaints as a domain to be followed up, where datasets are extracted from the conversation on Twitter. The research objectives support three key findings to comparing the CNNs and BRNNs model to intent recognition by vectorization text: (1) Which architecture performs better (accuracy) depends on how important it is to semantically understand the whole sequence and (2) Learning rate changes performance relatively smoothly, while the optimal result iterated by change hidden size and batch size result in large fluctuations. (3) Last, how word vectorization is able to define sub-domain of the complaints by word vector classification.


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