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

“…<p>The average neuronal activity for each cluster of the Contact <b>(a)</b>, Rough <b>(b)</b> and Smooth stimulus <b>(c)</b> were determined using the Dirichlet Process Mixture Model (DPM) clustering algorithm. …”

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

Algorithm used for the determination of the model parametric values. by Jonathan Côté (527819)

“…<p>Algorithm used for the determination of the model parametric values.…”

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

Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase... by Zhao Li (300229)

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

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

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Source Code and Dataset by Swathi Ganesan (14394924)

Determining the importance of factors in quantitative modeling by the RF algorithm. by Mahboobe Hojati (21702672)

Determining the importance of factors in qualitative modeling by the RF algorithm. by Mahboobe Hojati (21702672)

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

Pseudo code. by Xuwei Liu (6034592)

“…The experiment was validated using project datasets covering different regions, scales, and types of prefabricated components. …”

The algorithms and score criterion used in DBN hybrid structure learning. by Aslı Yaman (22567074)

Data and code availability: Machine Learning on systematically curated data reveals key determinants of magnetic hyperthermia performance by Edgar Vega (21981590)

“…Twelve machine learning models were evaluated and refined using Bayesian hyperparameter optimization. The CatBoost algorithm emerged as the most effective model, achieving the lowest mean absolute error (20.92 W/g) and root mean squared error (39.41 W/g), along with a high coefficient of determination (R² = 0.98). …”

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

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The genome coding scheme. by Wenbing Shi (5806160)

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Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Comparison of different algorithms. by Jiandong Qiu (20389944)

A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

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

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