Abstract
The aim of this paper is to bring together two areas, which are deep learning and machine learning for image classification. This paper checks or compares the accuracy level of any image classification dataset by using machine learning and deep learning algorithms. The target of this paper is the ability of a marked classifier for image classification. Machine learning is a branch of artificial intelligence that helps the system to learn automatically and improve with experience. Deep learning is a subfield of machine learning that involves learning from a large amount of data with complex computation. In this paper, we have used the plant village dataset. Plant village dataset is a collection of more than 50,000 expertly administered images on healthy and infected leaves of different crops. The images are 250 × 250 px. In this paper, machine learning algorithms such as K-nearest neighbors, logistic regression, support vector machines, and deep learning algorithms like ANN, CNN, and RBFNN are applied to the dataset and their performance in classifying the images has been compared.
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Chaturvedi, A., Rajpoot, V., Bansal, M., Das Agrawal, H. (2022). An Analysis of Different Machine Learning Algorithms for Image Classification. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_30
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