Deep Learning in IoT: An LSTM Approach for NDVI Forecasting-Reference-Cited by-同舟云学术

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Deep Learning in IoT: An LSTM Approach for NDVI Forecasting

Published:2023-10-07 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electronic Engineering (ICEEM)
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Author:

Sherif Khaled¹,Azmy Mohamed²,Alakkari Khder³,Abotaleb Mostafa⁴,El-Kenawy El-Sayed M.¹

Affiliation:

1. Delta Higher Institute,Department of Communications,Mansoura,Egypt

2. Delta Higher Institute,Department of Civil Engineering,Mansoura,Egypt

3. University of Tishreen,Department of Statistics and Programming Faculty of Economics,Tartous,Syria

4. South Ural State University,Department of System Programming,Chelyabinsk,Russia,454080

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10319473/10319474/10319616.pdf?arnumber=10319616

Reference32 articles.

1. Digital Twin Technology Challenges and Applications: A Comprehensive Review

2. Optimized LSTM model for future climate prediction;fu;Scientific Reports,2016

3. An Efficient Density-Based Clustering Algorithm Using Reverse Nearest Neighbour

4. MDST-DBSCAN: A Density-Based Clustering Method for Multidimensional Spatiotemporal Data

5. AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Smart Alarm IoT System: Monitoring Elevator Traffic and Meteorological Data on Job Sites Using MQTT and InfluxDB integrated with Grafana;2024 15th International Conference on Information and Communication Systems (ICICS);2024-08-13

2. STL-BiLSTM-Based Segmental Prediction Model for Wheat NDVI;2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI);2024-05-24

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