Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production-Reference-Cited by-同舟云学术

Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production

Published:2021-11-28 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
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Author:

Aquino Heinrick L.¹,Concepcion Ronnie S.²,Mayol Andres Philip³,Bandala Argel A.¹,Culaba Alvin³,Cuello Joel⁴,Dadios Elmer P.²,Ubando Aristotle T.³,San Juan Jayne Lois G.⁵

Affiliation:

1. De La Salle University,Department of Electronics and Computer Engineering,Manila,Philippines

2. De La Salle University,Department of Manufacturing Engineering and Management,Manila,Philippines

3. De La Salle University,Department of Mechanical Engineering,Manila,Philippines

4. The University of Arizona,Agricultural and Biosystems Engineering Department,Arizona,USA

5. De La Salle University,Department of Industrial and Systems Engineering,Manila,Philippines

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9731800/9731833/09731926.pdf?arnumber=9731926

Reference29 articles.

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2. Lactuca sativa leaf extract concentration optimization using evolutionary strategy as photosensitizer for TiO2-filmed Grätzel cell;concepcion;Optik,2021

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4. Tomato Septoria Leaf Spot Necrotic and Chlorotic Regions Computational Assessment Using Artificial Bee Colony-Optimized Leaf Disease Index

5. Lettuce Leaf Necrotic and Chlorotic Surface Defect Assessment Using RNN Optimized by EM;concepcion,2020

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