Fig 4 - by Olaide N. Oyelade (14047002)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

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

ROC curves for the test set of four models. by 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). …”

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

Homomorphic binary tree. by Jianhua He (341366)

Proposed Algorithm. by Hend Bayoumi (22693738)

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

Best optimizer results of Adaboost. by Balraj Preet Kaur (20370832)

Best optimizer results of Lightbgm. by Balraj Preet Kaur (20370832)

Random forest with hyperparameter optimization. by Balraj Preet Kaur (20370832)

Best optimizer results of KNN. by Balraj Preet Kaur (20370832)

Best optimizer results of KNN. by Balraj Preet Kaur (20370832)

Comparison in terms of the sensitivity. by Ying Li (38224)

“…<div><p>Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

Parameter sensitivity of BIMGO. by Ying Li (38224)

“…<div><p>Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”