Sökning: "Veselka Boeva"

Visar resultat 1 - 5 av 8 avhandlingar innehållade orden Veselka Boeva.

1. 1. Data Mining Approaches for Outlier Detection Analysis

   Författare :Shahrooz Abghari; Niklas Lavesson; Håkan Grahn; Veselka Boeva; Olga Fink; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; outlier detection; data modelling; machine learning; clustering analysis; data stream mining; Computer Science; Datavetenskap;

   Sammanfattning : Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. LÄS MER

3. 2. Data Modeling for Outlier Detection

   Författare :Shahrooz Abghari; Niklas Lavesson; Håkan Grahn; Veselka Boeva; Anders Holst; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; data modeling; cluster analysis; stream data; outlier detection;

   Sammanfattning : This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains. LÄS MER

4. 3. Resource-Aware and Personalized Federated Learning via Clustering Analysis

   Författare :Ahmed Abbas Mohsin Al-Saedi; Veselka Boeva; Emiliano Casalicchio; György Dán; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Federated Learning; Clustering Analysis; Eccentricity Analysis; Non- IID Data; Model Personalization; Computer Science; Datavetenskap;

   Sammanfattning : Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. LÄS MER

5. 4. Clustering Techniques for Mining and Analysis of Evolving Data

   Författare :Vishnu Manasa Devagiri; Veselka Boeva; Niklas Lavesson; Sindri Magnússon; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Clustering analysis; Concept drift; Evolutionary clustering; Machine learning; Streaming data; Computer Science; Datavetenskap;

   Sammanfattning : The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. LÄS MER

7. 5. Mining Evolving and Heterogeneous Data : Cluster-based Analysis Techniques

   Författare :Vishnu Manasa Devagiri; Veselka Boeva; Niklas Lavesson; Shehroz Khan; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Domain Adaptation; Evolving Clustering; Heterogeneous Data; Multi-View Clustering; Streaming Data; Computer Science; Datavetenskap;

   Sammanfattning : A large amount of data is generated from fields like IoT, smart monitoring applications, etc., raising demand for suitable data analysis and mining techniques. LÄS MER