Predicting the Demand of Prescribed Medicines in Bangladesh using Artificial Intelligent (AI) based Long Short-Term Memory (LSTM) Model-Reference-Cited by-同舟云学术

Predicting the Demand of Prescribed Medicines in Bangladesh using Artificial Intelligent (AI) based Long Short-Term Memory (LSTM) Model

Published:2020-01-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Computing Advancements
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Author:

Hafiz Muhtasim¹,Sazzad Md Sabir Ibna¹,Hasan Khalid Ibne¹,Hasnat Jamil¹,Mishu Mahbub C.¹

Affiliation:

1. Dept. of Computer Science American International University-Bangladesh, Dhaka, Bangladesh

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3377049.3377056

Reference20 articles.

1. A. Molina B. Ponte J. Parreño D. De la Fuente J. Costas "Forecasting erratic demand of medicines in a public hospital: A comparison of artificial neural networks and ARIMA models" Int'l Conf. Artificial Intelligence 2016 A. Molina B. Ponte J. Parreño D. De la Fuente J. Costas "Forecasting erratic demand of medicines in a public hospital: A comparison of artificial neural networks and ARIMA models" Int'l Conf. Artificial Intelligence 2016

2. Gökçe Candan Mehmet Fatih Taşkin Harun Reşit Yazgan "Demand Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Neuro-Fuzzy Approach" Journal of Military and Information Science 2014 Gökçe Candan Mehmet Fatih Taşkin Harun Reşit Yazgan "Demand Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Neuro-Fuzzy Approach" Journal of Military and Information Science 2014

3. The efficacy of using judgmental versus quantitative forecasting methods in practice

4. The forecasting of International Expo tourism using quantitative and qualitative techniques

5. S. Lakshmi Anusha Swati Alok Ashiff Shaik "Demand Forecasting for the Indian Pharmaceutical Retail: A Case Study" Journal of Supply Chain Management Systems April 2014 S. Lakshmi Anusha Swati Alok Ashiff Shaik "Demand Forecasting for the Indian Pharmaceutical Retail: A Case Study" Journal of Supply Chain Management Systems April 2014

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