Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 71 for “"Unsupervised clustering"”.
-
Innovative Supply Chain Cyber Risk Analytics: Unsupervised Clustering and Reinforcement Learning Approaches
… chain. The newly proposed approach is based on unsupervised clustering techniques applied to intuitive supply chain features of the respective software companies. The clustering approach is applied to a self-constructed dataset of over 4,600 software companies, and the model partitions the …
-
A study on the utility of temporal derivatives and unsupervised clustering in brain-computer interfaces
… performance in classification of an ERP, and (b) unsupervised clustering of ERPs. Both investigations tackle the problem of mining properties of unknown neuro-signals. Theoretical investigations carried out on in each topic are performed using synthetic signals to assess the expected behaviour. …
-
Liquid News - A Semantic-Relational Model for Enhanced Understanding
… navigational aids. Semantic segmentation and unsupervised clustering are the core machine-learning tasks underpinning Liquid News. Thus far, many state-of-the-art (SoTA) large language models provide building blocks for both tasks. However, more research needs to be done on combining large …
-
Identifying inventory excess and service risk in medical devices : a simulation approach
… find the right level of inventory, we first used unsupervised clustering method to find demand pattern uncertainty for each product. Then, we developed a simulation-based approach to determine the required inventory to achieve a required service level guarantee. We further explored policy changes …
-
Unsupervised discovery and validation of affective engagement states using synchronized EEG and eye-tracking during digital learning
… proxies. Instead of predefined emotion labels, unsupervised clustering methods were applied to identify latent engagement patterns directly from EEG features. To ensure robustness, non-overlapping parity analysis and hold-out validation were performed. Statistical tests were conducted to compare …
-
Representation Learning Associates Patients’ Risks for Metabolic Diseases with Features of Their Lipocytes
… using a convolutional autoencoder, we perform unsupervised clustering on the learnt representations to identify different cell states. We analyze the distribution of these cell states in different individuals and associate their PRS to the observed cell state distributions. Finally, we show …
-
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques
… the data, therefore avoiding reliance on unsupervised clustering methods that may not accurately group data by label. We design BELA with strategies to avoid bias that could be introduced through this adaptive partitioning. We evaluate BELA on labeling of four datasets and find that it …
-
Feature Identification in Wooden Boards Using Color Image Segmentation
… image segmentation problem, two conventional clustering procedures were selected for examination. Experiments that were performed clearly showed that these procedures, ones that are similar in flavor to other unsupervised clustering methods, are unsuitable for wood color image segmentation. …
-
A Study of Visual Attention on Gestures and Motion during Infancy
… trajectories from these videos, and then uses unsupervised clustering to group the trajectories into multiple groups. These groups are then analyzed to explore potential correlations between the motions of the parents and the attention of the child. The proposed tool will enable researchers to …
-
Cognitive, Clinical, and Biomarker Correlates of Insight in Obsessive–Compulsive Disorder: preliminary results of a cross-sectional study
… after correction for multiple testing. Although unsupervised clustering failed to separate subtypes clearly, supervised models achieved high accuracy (F1 = 0.9), identifying BABS conviction as the strongest discriminator. These findings highlight metacognitive flexibility and sleep physiology as …
-
Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance
… pathways such as calcium and cAMP signaling. Unsupervised clustering identifies three tumor subtypes—responder-dominant, non-responder-dominant, and an intermediate group—suggesting plastic or transitional immune states. Survival analyses confirm the clinical relevance of these clusters and …
-
Enabling Proactive Quality in Commercial Airplanes using Natural Language Processing
… of such quality data. We investigate both an unsupervised clustering method and a supervised classification method to group these reports by the broader "quality topic" they pertain to, using semantic relationship-maintaining text "embeddings" as features. We find success in supervised …
-
Permutation-based Significance Tests for Multi-modal Hierarchical Dirichlet Processes with Application to Audio-visual Data
… parameters. Because of its non-parametric and unsupervised clustering nature, it can be difficult to quantify the significance of the learned mmHDP structure. We propose a novel permutation testing framework that empirically measures the significance of the mmHDP structure and demonstrate its …
-
Path planning of agricultural UAVs for combined coverage and spot spraying application
… farming, the proposed approach leverages unsupervised clustering, spatial statistics, and optimization techniques to identify weed-dense regions and minimize non-target spraying. Incorporating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with principal component …
-
PASSIVE RADAR TRACK CLUSTERING: HIGHER FIDELITY OF TARGET IDENTIFICATION AND CLASSIFICATION OF UNLABELED TRACKS
… of simulated radar waveforms, we evaluated unsupervised learning (USL) and supervised learning (SL) approaches across three SLQ-32 preprocessed datasets: Raw Magnitude, Full Spectrogram, and Max Hold Spectrogram. We implemented SSL pipelines using K–Means and Gaussian Mixture Models (GMMs) …
-
Data-driven Synchrotron X-ray Microscopy Characterization of Functional Thin Films
… and optimize analysis time. By integrating unsupervised clustering, deep learning, and physics-aware automatic differentiation, the proposed methodologies enable rapid analysis of thin film structural morphology, which plays a critical role in fundamental materials properties. We demonstrate …
-
Experimental and analytical techniques for studying mechanotransduction in articular cartilage
… supervised time series classifiers, and unsupervised clustering via a variational autoencoder to identify and categorize cell phenotypes. Time series data collected from thousands of chondrocytes \textit{in situ} during and after impact allow me to probe responses through the lenses of …
-
Remote Sensing for Precision Agriculture: Within -Field Spatial Variability Analysis and Mapping With Aerial Digital Multispectral Images
Unsupervised clustering of color infrared (CIR) image of a field soil was able to identify soil mapping units with an average accuracy of 76%. Spectral reflectance from a crop field was highly correlated to the chlorophyll reading. A regression model developed to predict nitrogen stress in corn …
-
Identification of Anxiety Endophenotypes Using Multidimensional Measures of Attention
… in-lab (N = 28) or online (N = 121), we used an unsupervised clustering approach (k-means clustering) to assign individual cases to clusters, depending upon their performance on measures of attention. We used a supervised machine learning approach (random forest), to cross-validate the …
-
Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques
… The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with …
Page 1 of 4