Search for dissertations about: "Ilir Jusufi"

Found 3 swedish dissertations containing the words Ilir Jusufi.

2. 1. Towards the Visualization of Multivariate Biochemical Networks

   Author : Ilir Jusufi; Andreas Kerren; Heidrun Schumann; Linnéuniversitetet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Information Visualization; Biological Networks; Multivariate Networks; Biological Visualization; Computer Science; Datavetenskap; Information and software visualization; Informations- och programvisualisering;

   Abstract :  Many open challenges exist when dealing with different biological networks. They are crucial for the understanding of living beings. Complete drawings of these typically large networks usually suffer from clutter and visual overload. In order to overcome this issue, the networks are divided into single, hierarchically structured pathways. READ MORE

4. 2. Multivariate Networks : Visualization and Interaction Techniques

   Author : Ilir Jusufi; Andreas Kerren; Jessie Kennedy; Linnéuniversitetet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Information Visualization; Multivariate Networks; Visual Analytics; Exploration; Interaction; Computer Science; Datavetenskap; Information and software visualization; Informations- och programvisualisering;

   Abstract : As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. READ MORE

5. 3. Visual Analytics for Explainable and Trustworthy Machine Learning

   Author : Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

   Abstract : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. READ MORE