Optimization of Bioelectrochemical Systems with Power of Artificial Intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Mehta:2023:HTC,

• author = "Jimil Mehta and M. T. Shah",
• booktitle = "2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC)",
• title = "Optimization of Bioelectrochemical Systems with Power of Artificial Intelligence",
• year = "2023",
• pages = "494--499",
• abstract = "Bioelectrochemical systems (BESs) are sophisticated and advanced systems that use exoelectrogenic microbes to generate bioenergy. The integration of Artificial Intelligence (AI) plays a crucial role in comprehending, establishing connections, modelling, and predicting both microbial diversity and process parameters, ultimately enhancing the performance of BESs. This approach uses cutting-edge computational algorithms that are tailored to the specific architecture of BESs, saving time and improving efficiency compared to outdated manual methods. To achieve optimal outcomes, this study aims to examine and compare existing research endeavors while emphasizing the implementation of AI concepts in the field of bioelectrochemical systems. The AI techniques implemented to predict and optimise the behaviour of BES are Artificial Neural Network (ANN), Fuzzy Logic (FL), Multi Gene Genetic Programming (MGGP), and Support Vector Regression (SVR).",
• keywords = "genetic algorithms, genetic programming, Support vector machines, SVM, Fuzzy logic, Renewable energy sources, System performance, Supervised learning, Fuel cells, Artificial neural networks, ANN, Artificial Intelligence, Bioenergy, Bioelectrochemical System, Microbial fuel cell",
• DOI = "doi:10.1109/R10-HTC57504.2023.10461898",
• ISSN = "2572-7621",
• month = oct,
• notes = "Also known as \cite{10461898}",

}

Genetic Programming entries for Jimil Mehta M T Shah

Citations