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              <text>A Study on the Influence of Geometric Transformations on Image Classification: A Case Study</text>
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              <text>Augmentation; DenseNet; InceptionResNet; Rice plant leaf disease</text>
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              <text>The present research work involves the study of the geometrical transformations which influences the training and validation accuracies of machine learning models. For the study, rice plant leaf disease dataset of 2096 images consisting of 4 classes with 523 images per class were used. The dataset subjected to 24 models out of which three models namely - DenseNet201, Densenet169 and InceptionResNetV2 are selected based on highest training accuracy and less difference between training and validation accuracy. To evaluate the performance of the selected three models, loss functions and accuracies have been computed.  2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Nellisara S.D.; Dutta J.; Cyriac S.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-290, pp. 411-416.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-4486-3_46" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-4486-3_46&lt;/a&gt;
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              <text>ISSN: 23673370; ISBN: 978-981164485-6</text>
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              <text>English</text>
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              <text>Nellisara S.D., Center for Advanced Research and Training, CHRIST (Deemed To Be University), Bangalore, India; Dutta J., Center for Digital Innovation, CHRIST (Deemed To Be University), Bangalore, India; Cyriac S., Center for Digital Innovation, CHRIST (Deemed To Be University), Bangalore, India</text>
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