Performance of the bAD-PSO-Guided WOA algorithm compared with another algorithm. حسب Ahmed M. Elshewey (21463867)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging حسب D. Andrew Brown (6584837)

ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants... حسب Shifa Zhong (1685533)

الموضوعات:

ROC curves of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Normalized confusion matrix of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. حسب Md. Sabbir Hossain (9958939)

Normalized confusion matrix of the proposed method for multiclass classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, (F) Ensemble LEARNING. حسب Md. Sabbir Hossain (9958939)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> حسب BRISC Dataset (22559540)

EvoImp’s GA structure example. حسب Antonio Fernando Lavareda Jacob Junior (17816730)

Symmetry test result. حسب Jiyong Moon (16819302)

Super.Complex identifies likely protein complexes within a PPI network using a distributed supervised AutoML method. حسب Meghana Venkata Palukuri (11890672)

Classification baseline performance. حسب 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. حسب 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. حسب 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%. …"

Summary of literature review. حسب 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%. …"

Structure-based antibody paratope prediction with 3D Zernike descriptors and SVM حسب Sebastian Daberdaku (4391767)

Objective performance of fully-automated SLAVV software. حسب Samuel A. Mihelic (11539996)

Pan-cancer machine learning predictions of MEKi response. حسب John P. Lloyd (10196288)

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

"…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"

Data_Sheet_1_Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach.pdf حسب Md. Zahangir Alam (12056864)

"…Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. …"