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Classification of rice varieties with deep learning methods
Scopus
Toplam 222 atıf DOI
Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.
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Atıf Yapan Yayın
Classification of malicious android applications by machine learning methods using permission properties
Scopus
Havuzumuzda
This study applies machine learning methods to classify Android applications as malicious or benign using permission features. A dataset consisting of 2,854 malware and 2,870 non-malware apps with 117 features was used. Classification was performed with Adaboost (AB), random forest (RF), and artificial neural networks (ANN), while the information gain (IG) algorithm was used to select relevant features. The classification process was carried out in three steps: first using all 117 features, second with 60 selected features, and third with 20 selected features. The highest accuracy, 98.4, was achieved using 117 features and ANN. The models were evaluated using precision, recall, F1 score, ROC curve, and AUC metrics. Additionally, the training and testing times of all models were analysed. The study also employed correlation and weighted correlation analysis to assess the importance of permission features.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey