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

Results of using the algorithm to find the chemical constituents for compounds using dual energy and mass attenuation coefficient. by Dong Hyeok Choi (12562048)

Results of using the algorithm to find the chemical constituents for mixtures using dual energy and mass attenuation coefficient. by Dong Hyeok Choi (12562048)

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

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)

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)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Code for "Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset" by Yufeng Ke (16539882)

The folder containing the code is provided as supportive documentation associated with this paper. by Quxi Kuang (22493270)