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

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@InProceedings{Aquino:2021:HNICEM,

• author = "Heinrick L. Aquino and Ronnie S. Concepcion and Andres Philip Mayol and Argel A. Bandala and Alvin Culaba and Joel Cuello and Elmer P. Dadios and Aristotle T. Ubando and Jayne Lois G. {San Juan}",
• booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)",
• title = "Prediction of Moisture Content of Chlorella vulgaris Microalgae Using Hybrid Evolutionary Computing and Neural Network Variants for Biofuel Production",
• year = "2021",
• month = "28-30 " # nov,
• address = "Manila, Philippines",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-6654-0168-5",
• DOI = "doi:10.1109/HNICEM54116.2021.9731926",
• abstract = "Moisture content is an imperative indicator of biofuel lipid content in microalgae. This paper developed a reliable, computationally cost-effective combination of artificial neurons and an optimization tool for moisture content concentration prediction using computational intelligence. A total of 83 data of microalgae var. Chlorella vulgaris moisture content parameter factors were used. Using feed-forward, recurrent, and deep neural networks as prediction models, their MSE and R2 values were analyzed. Genetic programming GPTIPSv2, a multigene symbolic regression genetic programming (MSRGP) tool, was used to create objective functions of the ANNs. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered to suggest the optimal quantity of neurons in each of the hidden layers in neural network architecture. The feed-forward artificial neural network with 22 neurons in its layer was recommended using the Levenberg-Marquardt training tool. The MSE (5.27e-6) and R2 (0.9999) results of this model surpassed the other neural networks models. Hence, it implies that the developed optimized Levenberg-Marquardt-based feed-forward neural network is an effective moisture content predictor as it provided highly accurate and sensitive results at a low cost.",
• notes = "Also known as \cite{9731926}",

}

Genetic Programming entries for Heinrick L Aquino Ronnie S Concepcion II Andres Philip Mayol Argel A Bandala Alvin Culaba Joel Cuello Elmer Jose P Dadios Aristotle T Ubando Jayne Lois G San Juan

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