Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Average number of selected features. by Guangwei Liu (181992)

“…Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. …”

Average number of selected features. by Guangwei Liu (181992)

“…Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. …”

Input features. by Nicola Lazzarini (3528197)

Age feature importance. by Nicola Lazzarini (3528197)

Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”

Most important predictive features. by Nicola Lazzarini (3528197)

Schematic representation of univariate ensemble-based feature selection. by Nanavath Kiran Singh Nayak (22184565)

Optimized model prediction performance. by Guang Tu (22054865)

S1 Data - by Guangwei Liu (181992)

“…Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. …”

Data_Sheet_1_How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.ZIP by Hao Wu (65943)

Variable importance of features included in the gradient boosting machine learning (XGB) algorithm for predicting postinduction hypotension. by Manabu Yoshimura (11040049)

Data_Sheet_1_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF by Josefa Díaz-Álvarez (5572427)

Data_Sheet_2_Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.PDF by Josefa Díaz-Álvarez (5572427)

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

“…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …”

Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. …”

Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. …”

Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

“…These are then compared with other algorithms, such as vector autoregression, in portfolio optimisation tests. …”

Mean scores of the last 100 episodes using PPO and DQN in Unity environment and ViZDoom. by Enoch Solomon (21416703)

Subjects:

Mean reward of using visual information, visual information with raw audio samples in Unity ML environment. by Enoch Solomon (21416703)

Subjects: