Selected features via our feature selection algorithm for baseline vs. VNS at 10 Hz (first scenario) and organized in a descending sequence of significance, as established by the R... by Mahmoud Zeydabadinezhad (12289570)

Selected features via our feature selection algorithm for baseline vs. VNS at 30 Hz (second scenario) and organized in a descending sequence of significance, as established by the... by Mahmoud Zeydabadinezhad (12289570)

Structure determination statistics of HEWL and GI. by Ki Hyun Nam (5813819)

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Molecular replacement (MR) solution and electron density maps of HEWL at various input CTDDs. by Ki Hyun Nam (5813819)

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MR solution and electron density maps of GI at various input CTDDs. by Ki Hyun Nam (5813819)

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Schematic representation of the methodology used to evaluate the ML algorithms for predicting the relative density, surface roughness, and hardness of AlSi10Mg parts produced using... by Fatma Alamri (20855088)

Relative popularity data. by Panayiotis D. Ziakas (9510346)

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The robustness test results of the model. 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. …”

The process of searching the literature. by Xu Tian (179192)

Example of R code with data for calculations. by Panayiotis D. Ziakas (9510346)

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Performance of the sliding window algorithm with different clustering methods applied to the full data set. by Michael D. Kupperman (13961871)

Data set presentation. 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. …”

Demonstration of data imbalance. 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. …”

Computation of mental representation from single training example and subsequent comparison to test objects. by Spencer Caplan (10188380)

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Example learning and evaluation trial under the NGM. by Spencer Caplan (10188380)

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Major patterns to be captured by models of word learning and generalization. by Spencer Caplan (10188380)

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Implementation of distance computation between an object and mental representation under the NGM. by Spencer Caplan (10188380)

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Chart of all seven training configurations. by Spencer Caplan (10188380)

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Test-grid from Xu & Tanenbaum. by Spencer Caplan (10188380)

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Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior - Data, Analysis Code and Appendix by Nele Albers (11596000)