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In the present study, lasso, support vector machine regression (svmr), and artificial neural networks (ann) with different structures were examined to predict the capacitance of the supercapacitor. For instance, they increase specific capacitance by raising the specific surface area, regulating the pore structure, and adding functional groups on the surface. Machine learning models were developed to predict the output of the supercapacitor
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Artificial neural network model had achieved the highest performance The physicochemical features used in this work to predict the specific capacitance of the scs electrode material include Machine learning provides optimum material composition for better output.
