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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

Contour lines of GDOP in parallelogram layout. by Sheng Miao (14248110)

Contour line of GDOP in triangle layout. by Sheng Miao (14248110)

Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction by E. Tejera (7470644)

“…<div><p>The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Spearman correlation coefficient results for classification algorithms (predicted probabilities) and regression algorithms (predicted IC50 values). by Sofia P. Miranda (11418930)

Subjects:

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

Predicting patient drug response from cell-line methylation profiles for Temozolomide (n = 85). by Sofia P. Miranda (11418930)

Subjects:

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”

Line graph for determining K value based on elbow method. by Bing Li (43671)

Contour lines of GDOP of five BSs using DBSS5. by Sheng Miao (14248110)

Contour lines of GDOP of four BSs using DBSS4. by Sheng Miao (14248110)

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

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. 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. …”