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 ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Food Delivery ◽  
Delivery Service ◽  
On Demand

Digital Literacy Approach in Food Delivery Service Application

2021 ◽  
Author(s):  
Tiurida Lily Anita ◽  
Arif Zulkarnain ◽  
Amia Luthfia ◽  
Sari Ramadanty ◽  
Abdul Rauf Ridzuan
Keyword(s):  
Digital Literacy ◽  
Food Delivery ◽  
Delivery Service ◽  
Service Application

Courier Routing and Assignment for Food Delivery Service Using Reinforcement Learning

2021 ◽  
pp. 107871
Author(s):  
Aysun Bozanta ◽  
Mucahit Cevik ◽  
Can Kavaklioglu ◽  
Eray M. Kavuk ◽  
Ayse Tosun ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Food Delivery ◽  
Delivery Service

scholarly journals A Systematic Approach to Order Fulfillment of On-demand Delivery Service for Bento Industry

2013 ◽  
Vol 17 ◽  
pp. 96-103
Author(s):  
Rong-Chang Chen ◽  
Chih-Hui Shieh ◽  
Kai-Ting Chan ◽  
Shin-Yi Chiu ◽  
Jyun-You Fan ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Order Fulfillment ◽  
Delivery Service ◽  
On Demand

scholarly journals Automation of Food Delivery Service in Railways

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Mrinal Malkar ◽  
Sai Karthik Nandigama ◽  
Anudeepa Kholapure ◽  
Yash Pawse ◽  
Vaibhavi Amal
Keyword(s):  
Food Delivery ◽  
Delivery Service

scholarly journals A Study on Improvement Effect of Shopping Environment by the Food Delivery Service

2016 ◽  
Vol 35 (Special_Issue) ◽  
pp. 201-206
Author(s):  
Satoshi TSUCHIYA ◽  
Keishi TANIMOTO ◽  
Hiromi KURAMOCHI
Keyword(s):  
Food Delivery ◽  
Delivery Service ◽  
Improvement Effect

scholarly journals COMPARISON INTENT RECOGNITION ON FOOD DELIVERY SERVICE COMPLAINT IN TWITTER WITH RECURRENT AND CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Irfan Nasrullah ◽  
Rila Mandala
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Food Delivery ◽  
Learning Rate ◽  
Batch Size ◽  
Optimal Result ◽  
Intent Recognition ◽  
Delivery Service ◽  
Customer Relation Management ◽  
Large Fluctuations

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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