Gunay M.Koseoglu M.2024-08-042024-08-0420209781728190907https://doi.org/10.1109/ISMSIT50672.2020.9255047https://hdl.handle.net/11616/922584th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- 165025In this paper, at first, existing classification methods for classifying hand drawn circuits and circuit components are briefly introduced by examining advantages and disadvantages of each classification method. The handled methods are Support Vector Machine method (SVM), nearest neighbor method (KNN) and convolutional neural network method (CNN). Then, in the experimental part of the study, four different hand-drawn circuit components, which are diode, capacitor, resistor and ac voltage source, have been tried to be classified by using CNN method which is a highly recommended classification method in literature. The classification tables and graphics have been presented for the performed study. According to the obtained results and accuracy rate, it is concluded that CNN method can be used reliably to achieve high performance in the classification of hand-drawn circuit components. © 2020 IEEE.eninfo:eu-repo/semantics/closedAccesscircuit componentclassificationCNNKNNSVMConference Object10.1109/ISMSIT50672.2020.92550472-s2.0-85097678895N/A