Feature selection process. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Results of KNN. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

After upsampling. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Results of Extra tree. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Gradient boosting classifier results. by Balraj Preet Kaur (20370832)

“…To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Data_Sheet_1_Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning.PDF by Romena Yasmin (12970919)

“…Five types of input elicitation methods are tested: binary classification (positive or negative); the (x, y)-coordinate of the position participants believe a target object is located; level of confidence in binary response (on a scale from 0 to 100%); what participants believe the majority of the other participants' binary classification is; and participant's perceived difficulty level of the task (on a discrete scale). …”

The overlap of Texas Rio Grande Valley Red–crowned Parrot predicted general use, roost site, and nest site habitat distributions as determined using consensus-based feature ensembl... by Elise Varaela Voltura (17592996)

ROC curves for the test set of four models. by Meng Cao (105914)

“…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”

The AD-PSO-Guided WOA LSTM framework. by Ahmed M. Elshewey (21463867)

“…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …”

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

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

Classification baseline performance. by Doaa Sami Khafaga (21463870)

“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”

Feature selection results. by Doaa Sami Khafaga (21463870)

“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”

ANOVA test result. by Doaa Sami Khafaga (21463870)

“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”

Summary of literature review. by Doaa Sami Khafaga (21463870)

“…To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. …”

Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”

An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

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Medium-scale dataset comparative analysis using fitness and cost values for population size 50 and 100. by Olaide N. Oyelade (14047002)

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Large-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

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Datasets and their properties. by Olaide N. Oyelade (14047002)

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