<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”

Optimized process of the random forest algorithm. by Hongxia Li (493545)

“…Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. …”

Sample of the comparison of inference given by the adaboost algorithm and the real data. by Raphael Souza de Oliveira (12647110)

The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

The weight optimization process. by Guomei Cui (20721578)

“…The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. …”

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…By integrating a heuristic search mechanism, the algorithm can quickly converge to potential high utility patterns and effectively reduce redundant computational processes. …”

The Search process of the genetic algorithm. by Wenguang Li (6528113)

“…The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model’s predictive accuracy. …”

Statistics of GI data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

Subjects:

Statistics of HEWL data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

Subjects:

Simulated solution and the original spectral data of the actual water sample. by Wang hongmei (22598690)

Relationship between the three key elements in the sampling process. by Xiaochen He (455300)

Description of optimal model parameters. by Libo Liu (552605)

Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)

Parameter optimization results of the RF model improved by FCM clustering. by Xini Fang (20861990)

Three sensor sample data with noise data. by Tang Ruipeng (20141334)

Weights of sample data. by Yixuan Lu (9987980)

The construction process of the CGO prediction model. by Wenbing Shi (5806160)

The CGO risk prediction process based on data augmentation and neuroevolution. by Wenbing Shi (5806160)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Construction process of RF. by Xini Fang (20861990)

“…Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. …”