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Sowmiya, B.
- Classification of Paddy Leaf Diseases With Extended Huber Loss Function Using Convolutional Neural Networks
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Authors
Affiliations
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., IN
2 Department of Computer Science, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India., IN
1 PG and Research Department of Computer Science, A.V.V.M. Sri Pushpam College, Affiliated to Bharathidasan University, India., IN
2 Department of Computer Science, Queens College of Arts and Science for Women, Affiliated to Bharathidasan University, India., IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 3 (2023), Pagination: 2922-2930Abstract
Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters.Keywords
Paddy Disease Detection, Preprocessing, Classification, Huber Loss, Convolutional Neural Network.
References
- Agriculture and Allied Industries, Available at https://www.ibef.org/download/1658816319_Agricultureand-Allied-Industries-June-2022.pdf, Accessed in 2023.-
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- Enhancing Credit Card Fraud Detection in Financial Transactions Through Improved Random Forest Algorithm
Abstract Views :37 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, S.I.V.E.T. College, IN
1 Department of Computer Science, S.I.V.E.T. College, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 1 (2023), Pagination: 3089-3093Abstract
Credit card Fraud detection is a critical task in various industries, including finance and e-commerce, where identifying fraudulent activities can help prevent financial losses and protect users. It begins by combining two datasets containing fraudulent and non-fraudulent transactions to create a comprehensive dataset for analysis. Data is preprocessed by removing unnecessary features, calculating distance metrics, and generating new variables to capture temporal patterns and transaction history. Multicollinearity issues are addressed through feature selection. Improved Random Forest (RF) algorithm is used to improve fraud detection. The experimental results indicate that the improved Random Forest algorithm achieves commendable accuracy in fraud detection. The proposed model achieves 99.87% training accuracy and 99.41% testing accuracy. The Model’s performance is evaluated by measuring precision, recall, F1-score and support. Our research emphasizes the importance of considering improved algorithms to achieve better results. The findings provide valuable insights for organizations aiming to enhance their fraud detection capabilities and make informed decisions to protect their systems and users.Keywords
Credit Card, Fraud detection, Random Forest, Classification, Accuracy, Precision, Recall, and F1 Score.References
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