ROC curve for binary classification. حسب Nicodemus Songose Awarayi (18414494)

"…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …"

Confusion matrix for binary classification. حسب Nicodemus Songose Awarayi (18414494)

"…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …"

The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …"

IRBMO vs. meta-heuristic algorithms boxplot. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …"

IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. …"

Related Work Summary. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Simulation parameters. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Training losses for N = 10. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Normalized computation rate for N = 10. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

Summary of Notations Used in this paper. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"

An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. حسب Zhengdong Wang (35299)

datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf حسب Jan Tönshoff (10192709)

"…We introduce a graph neural network architecture for solving such optimization problems. …"

SHAP bar plot. حسب Meng Cao (105914)

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

Sample screening flowchart. حسب Meng Cao (105914)

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

Descriptive statistics for variables. حسب Meng Cao (105914)

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

SHAP summary plot. حسب Meng Cao (105914)

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

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

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

Display of the web prediction interface. حسب Meng Cao (105914)

"…Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). …"

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

"…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …"

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP حسب Xiaoyuan Wang (492534)

"…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …"