Due to the increased use of hyperspectral remote sensing payloads, there has been a rise in the number of hyperspectral remote sensing image archives, resulting in a massive amount of collected data. This highlights the need for a content-based image retrieval system that can manage and enable the use hyperspectral remote-sensing images efficiently. A novel CBHIR system is proposed that aims to define global hyperspectral image representations based on a semantic approach to differentiate background and foreground image content considering both spatial and spectral information. In this way, two spectral content dictionaries are used in the process of modeling hyperspectral images.
Date: 24.01.2024 Place: B-116
It is significant to comprehend the basics of decision making behaviour because people make decisions in their everyday lives. The purpose of this research is to understand individuals’ decision making behaviour under risk and uncertainty using computational cognitive modeling and neuroscience perspectives. Results related to behavioural and neural data analyses and computational cognitive modeling utilizing the collected data from experiments provide explanations for the mechanisms behind decision making under risk and uncertainty cases.
Date: 26.01.2024 Place: A-212
Microservices are increasingly gaining popularity in software design. It is essential for microservice architectures to have low response time variation to design testable and predictable systems. In this study, the aim is to predict the response time variation of microservice call graphs by using their topological features. Following the prediction processes with machine learning models, feature explanations methods are used to investigate which topological features are influential in the machine learning models' outputs regarding response time variation and how these features influence model outputs.
Date: 19.01.2024 Place: A-212
The most interactive field of digital transformation is data science, as it entails a longtime active collaboration among multiple partners. Data scientists seek domain expertise to understand the structure and environment of the data while business users take pains with concepts to exploit analytical solutions. This thesis presents the conceptual design and implementation of CoDS (Collaborative Data Science Framework) as a knowledge management system on which business and data details, modeling procedures, and deployment steps are shared. It mediates and scales ongoing projects, enriches knowledge transfer among stakeholders, facilitates ideation of new products, and supports the onboarding of new developers.
Date: 22.01.2024 Place: II-06
Organizations strive to improve their digital transformation (DX) maturity for market success, utilizing maturity structures such as maturity index. However, these structures face limitations, revealing a research gap. Therefore, this thesis introduces a novel self-diagnostic tool called the DX maturity index (DX-MI) using design science research. DX-MI assists organizations in measuring and advancing their DX maturity. It has a hierarchical structure that includes dimensions, sub-dimensions, and metrics, all underpinned by an assessment approach grounded in evidence or objective quantifiable metrics. Multiple case studies were conducted to check the applicability and usability of the DX-MI, confirming its effectiveness and practicality.
Date: 22.01.2024 Place: II-06