JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

Original Article
Convolutional neural network for pothole detection in different road and weather conditions
Aims: To propose a deep learning algorithm for pothole detection and compare the performance of Sigmoid and Softmax activation functions in the creation of Convolutional Neural Network (CNN) algorithms.
Methods: Three different datasets were used to justify the robustness of the CNN model in detecting dry and wet potholes. The CNN algorithms were created separately using the Sigmoid and Softmax activation functions.
Results: The CNN algorithm using the Sigmoid function achieved higher accuracy scores than the CNN algorithm using the Softmax function. Specifically, the Sigmoid algorithm achieved accuracy scores of 91%, 96%, and 83% over datasets 1, 2, and 3, respectively, while the Softmax algorithm achieved scores of 81%, 96%, and 85% over the same datasets.
Conclusion: The results of this study suggest that the CNN algorithm using the Sigmoid activation function is more robust and effective in detecting pothole images compared to the CNN algorithm using the Softmax activation function.

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Volume 1, Issue 1, 2023
Page : 1-4