Diseased Surface Assessment of Maize Cercospora Leaf Spot Using Hybrid Gaussian Quantum-Behaved Particle Swarm and Recurrent Neural Network-Reference-Cited by-同舟云学术

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Diseased Surface Assessment of Maize Cercospora Leaf Spot Using Hybrid Gaussian Quantum-Behaved Particle Swarm and Recurrent Neural Network

Published:2021-04-21 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)
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Author:

Concepcion Ronnie,Dadios Elmer,Alejandrino Jonnel,Mendigoria Christan Hail,Aquino Heinrick,Alajas Oliver John

Funder

Department of Science and Technology (DOST)

De La Salle University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9422411/9422480/09422515.pdf?arnumber=9422515

Cited by 27 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. Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases;Journal of Big Data;2024-08-10

2. An optimal feature selection-based deep learning approach for wheat disease identification;Multimedia Tools and Applications;2024-06-06

3. An optimal model for identification and classification of corn leaf disease using hybrid 3D-CNN and LSTM;Biomedical Signal Processing and Control;2024-06

4. Enhancing Maize Leaf Disease Detection Using Cat Swarm Optimization with Deep Learning Model;2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS);2024-04-17

5. rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting;Journal of Advanced Computational Intelligence and Intelligent Informatics;2024-01-20

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