Experiment methodology. by Leonides Medeiros Neto (10429179)

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Example of distance matrices generated by the IGTD in the process of conversion. by Leonides Medeiros Neto (10429179)

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Hyperparameter configuration of the best model returned by the random search, for each Arbovirus data set. by Leonides Medeiros Neto (10429179)

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Image size vs feature overlap. by Leonides Medeiros Neto (10429179)

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Results of the Basic CNN models and the XGBoost proposed by Neto et al. [9] for the Arboviruses/Inconclusive data set. by Leonides Medeiros Neto (10429179)

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The process of conversion of the DeepInsight. by Leonides Medeiros Neto (10429179)

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The CQA process. by Leonides Medeiros Neto (10429179)

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Results of the Tuned DeepInsight-CNN and the model proposed by Neto et al. [9], for the Arboviruses/Inconclusive data set. by Leonides Medeiros Neto (10429179)

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The structure of the Resnet50. by Nour Eldeen Mahmoud Khalifa (19259450)

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Comparison of DeepDate and the other related work. by Nour Eldeen Mahmoud Khalifa (19259450)

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Testing accuracy and performance metrics for different DTL models for every class. by Nour Eldeen Mahmoud Khalifa (19259450)

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Image examples of date fruit dataset for every class. by Nour Eldeen Mahmoud Khalifa (19259450)

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Testing accuracy and performance metrics for different DTL models. by Nour Eldeen Mahmoud Khalifa (19259450)

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The bottleneck residual block for Resnet50. by Nour Eldeen Mahmoud Khalifa (19259450)

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Random sample of dataset image with the testing accuracy for every image with different classes. by Nour Eldeen Mahmoud Khalifa (19259450)

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The consumed time for Resnet50 and DeepDate model for (a) FS and training and (b) testing. by Nour Eldeen Mahmoud Khalifa (19259450)

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Comparison of Resnet50 and DeepDate according to TA, P, R, and F1 score. by Nour Eldeen Mahmoud Khalifa (19259450)

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DeepDate model’s architecture design. by Nour Eldeen Mahmoud Khalifa (19259450)

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Number of images for every class in the date fruit dataset. by Nour Eldeen Mahmoud Khalifa (19259450)

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Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX by Maruf A. Tamal (18947776)

“…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …”