Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Data and code resources. by Sam Hall-McMaster (10343795)

Schematic diagram of elements in the delivery area. by Yingbo Mao (22522192)

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

Subjects:

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

The optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

Optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

Impact of multiunit merging on neural information encoding. by Daniel Suárez-Barrera (22676654)

Subjects:

Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" by Yize Wang (19535173)

“…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…”

CNN model evaluation. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

ROC curve CNN. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

code_4 by Olga Ovtsarenko (20432033)

The genetic algorithm. by Viktoria Zemliak (19500335)

Algorithm training accuracy experiments. by Yingying Liu (360782)

Kappa coefficients for different algorithms. by Yingying Liu (360782)

Confusion matrix evaluation of CNN. by Azhar Imran (17720751)

“…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …”

Temporal response patterns determined by Dirichlet Process-based clustering algorithm across amplitudes and stimuli. by Dinh K. Tang (20469082)

DT Algorithm. by Nashmia Khalid (20496403)

The actual change detection efficiency of algorithms. by Yingying Liu (360782)

Confusion matrix analysis for different algorithms. by Yingying Liu (360782)