ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

Model-Based Clustering of Categorical Data Based on the Hamming Distance by Raffaele Argiento (647076)

Confusion_Matrix_Data.zip by Mohammad Farhad Bulbul (21003494)

Mean squared Error on all unseen data. by Edward Antonian (21453161)

“…Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

Possible graph filter functions. by Edward Antonian (21453161)

“…Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

The notational conventions used in this paper. by Edward Antonian (21453161)

“…Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

16S rRNA sequencing raw data from a thermophilic trickle bed reactor for biogas upgrading by Getachew Birhanu Abera (20402390)

“…The DNA extraction followed by method adapted from Jensen et al. (2023), utilizing the DNeasy 96 PowerSoil Pro QIAcube HT Kit. …”

3D Deformable cell based model. by Roland M. Dries (6620132)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”

Supporting files for thesis "Deep-learning-based Morphological Modelling: Case Study in Soft Robot Control, Shape Sensing and Deformation" by Yingqi Li (9151304)

“…The algorithm of deep deterministic policy gradient (DDPG) along with domain randomization and offline retraining facilitates fast initialization and stable path following, even under varying tip load, demonstrating its advantages over Jacobian model-based and supervised-learning-based control methods. …”