Essays about: "neural clustering"

Showing result 1 - 5 of 90 essays containing the words neural clustering.

  1. 1. Domain Knowledge and Representation Learning for Centroid Initialization in Text Clustering with k-Means : An exploratory study

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : David Yu; [2023]
    Keywords : Natural language processing; Sentiment analysis; Clustering; Language model; Transformer; Heuristic; Språkteknologi; Sentimentanalys; Klustering; Språkmodell; Transformer; Heuristik;

    Abstract : Text clustering is a problem where texts are partitioned into homogeneous clusters, such as partitioning them based on their sentiment value. Two techniques to address the problem are representation learning, in particular language representation models, and clustering algorithms. READ MORE

  3. 2. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Luca Colasanti; [2023]
    Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

    Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE

  4. 3. Evaluating Process Mining Techniques on PACS Command Usage Data : Exploring common process mining techniques and evaluating their applicability on PACS event log data for domain-specific workflow analysis

    University essay from Linköpings universitet/Institutionen för datavetenskap

    Author : Axel Ekblom; Jacob Karlén; [2023]
    Keywords : process mining; workflow mining; data mining; radiology; PACS;

    Abstract : Many software companies today collect command usage data by monitoring and logging user interactions within their applications. This is not always utilised to its full potential, but with the use of state-of-the-art process mining techniques, this command usage log data can be used to gain insights about the users' workflows. READ MORE

  5. 4. Identification of Fibers in Micro-CT Images of Paperboard Using Deep Learning

    University essay from Lunds universitet/Hållfasthetslära; Lunds universitet/Institutionen för byggvetenskaper

    Author : David Rydgård; [2023]
    Keywords : Fiber networks; Paperboard mechanics; Deep learning; Tomography; Image analysis; Technology and Engineering;

    Abstract : This master thesis project explores the possibility of using deep learning to segment individual fibers in three-dimensional tomography images of paperboard fiber networks. We test a method which has previously been used to segment fibers in images of glass fiber reinforced polymers. READ MORE

  6. 5. Fault Detection and Diagnosis for Automotive Camera using Unsupervised Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ziyou Li; [2023]
    Keywords : Unsupervised Learning; Autoencoders; Image Clustering; Fault Detection and Diagnosis; Morphological Operations; Hardware-in-Loop; Advanced DriverAssistance System; Oövervakad inlärning; Autoencoders; Bildklustering; Felfindning och Diagnostik; Morfologiska Operationer; Hardware-in-Loop; Avancerade Förarassistanssystem;

    Abstract : This thesis aims to investigate a fault detection and diagnosis system for automotive cameras using unsupervised learning. 1) Can a front-looking wide-angle camera image dataset be created using Hardware-in-Loop (HIL) simulations? 2) Can an Adversarial Autoencoder (AAE) based unsupervised camera fault detection and diagnosis method be crafted for SPA2 Vehicle Control Unit (VCU) using an image dataset created using Hardware-inLoop? 3) Does using AAE surpass the performance of using Variational Autoencoder (VAE) for the unsupervised automotive camera fault diagnosis model? In the field of camera fault studies, automotive cameras stand out for its complex operational context, particularly in Advanced Driver-Assistance Systems (ADAS) applications. READ MORE