List of Abbreviations by Gursimran Singh (575288)

“…For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. …”

The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

“…For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. …”

Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" by Neal Munyebvu (19690954)

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. The experiment involving twenty participants has been conducted, and the results show that in continuously high-risk driving scenarios, compared to traditional reinforcement learning algorithms without human participation, the proposed algorithm can maintain a cautious driving policy and avoid potential collisions, validated with both proximal surrogate indicators and success rates. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Additionally, a Genetic Algorithm (GA) was applied in each iteration to optimize the hyperparameters of the XGBoost model, which is crucial for enhancing both the efficiency and robustness of the model (Zhong and Liu, 2024; Zou et al., 2024). …”

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”