Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

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The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

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The prediction accuracy of simple versus hybrid models. by Uğur Ejder (22683228)

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The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Flow diagram of the proposed model. by Uğur Ejder (22683228)

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Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). by Uğur Ejder (22683228)

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Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

The accuracy score change matrix following model development. by Uğur Ejder (22683228)

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Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

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Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

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ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction. by Uğur Ejder (22683228)

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<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

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The most effective active substances for subfertility treatment. by Uğur Ejder (22683228)

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The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

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Flow diagram of the conversion of drugs into active substances. by Uğur Ejder (22683228)

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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm by Hussein Ali Bardan (21976208)

“…The goal of this </p><p dir="ltr">research is to combine state-of-the-art deep learning techniques with optimization algorithms to develop a precise </p><p dir="ltr">and efficient predictive system for melanoma detection. …”

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

Feature selection results. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”

ANOVA test result. by Doaa Sami Khafaga (21463870)

“…The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. …”