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 / 11:00 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 / 13:00 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 / 14:30 Place: II-06

Steel production serves as the backbone of countless infrastructure projects and industrial applications worldwide. In order to maintain and improve its productivity, quality and environmental sustainability, hyperspectral imaging is a promising technology for steel industry.  A novel, non-destructive approach is presented to quantify the free lime content in steel slag by utilizing an integrated algorithm applied to hyperspectral images. This method includes spectral unmixing for mixture component quantification and endmember extraction of mixture. Methodology involved various experiments with both fresh and six-month-aged steel slag, demonstrating its accuracy compared to the Rietveld Analysis of X-ray Diffraction patterns.

Date: 11.01.2024 / 09:30 Place: B-116

State-of-the-art omics technologies use network-based contextualization methods to give molecular information about different biological contexts, like disease states, patients, and drug changes. In the beginning, this thesis identified challenging issues such as missing points in contextualization, hidden knowledge in omics datasets, bias in reference networks, and noisy interactions with highly connected nodes or hubs. Subsequently, to address these challenges, we developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN). Also, a novel tool, pyPARAGON, contextualized patient datasets by inferring patient-specific networks and complex diseases by constructing disease models, namely breast cancer and autism spectrum disorders.

Date: 22.01.2024 / 14:00 Place: A-108

Despite impressive achievements made by machine learning algorithms (especially in deep learning), they are easily tricked by modified input data. Adversarial attacks target machine learning models severely. Adversarial training is an effective method against adversarial attacks, but it is not suitable for network domains due to network flow characteristics. In this thesis, the autoencoder's reconstruction error is used for detecting adversarial attacks. The IDS model, RAIDS is proposed and achieves respectful results against adversarial attacks.

Date: 17.01.2024 / 14:00 Place: A-108

Global climate change induces lake level fluctuations, impacted by evolving meteorological factors and water use. Input or output changes swiftly affect the water balance equation. This study explores predictive models for climatic and hydrologic variables, assessing their correlations with lake water level and water quality. Using diverse algorithms—Naive Method, ANN, and RNN—LSTM excels in accuracy by RMSE. Comparisons with the Naïve Method confirm ANN and RNN predictive prowess, especially with extended horizons. Correlations with temperature and evaporation highlight lake water quality impacts. Models and metrics construct a decision support tool for water managers.

Date: 19.12.2023 / 13:30 Place: A-212

We developed an efficient and scalable method for identification of signature sequences that can handle thousands of whole genomes for organisms with high mutation rates and genetic diversity. Thermodynamics is the main driving force in our method, which is tested on three highly divergent viruses. The oligonucleotides found can identify 99.9% of 1657 HCV genomes, 99.7% of 11838 HIV genomes, and 95.4% of 4016 Dengue genomes. We also show subspecies identification on genotypes 1-6 of HCV and genotypes 1-4 of the Dengue virus with >99.5% true positive and <0.05% false positive rate. None of the state-of-the-art methods achieve this performance.

Date: 11.09.2023 / 17:00 Place: A-212

Most of the variants in the genome are at the non-coding region. While variations in the coding region effect the protein, variations in non-coding region effect the regulatory mechanism. Therefore, observation of non-coding variations may ensure to identify variations that effect gene expression. eQTL is a popular method used for the purpose to determine the SNPs that effect the gene expression. We have implemented a python based, easy-to-use tool to understand the relationship between the somatic SNPs and gene expression based on eQTL analysis.

Date: 11.09.2023 / 14:00 Place: A-212

In this study, changes in neural activation during joint action were investigated with hyperscanning, using functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) as participants performed the same task individually and then collaborated as a dyad. 62 participants were tested in dyads with a dual n-back task. Results are in support of the social facilitation model, and neural measures including hyperscanning were successful to differentiate between individual and social task settings. Overall, this study is one of the first studies to conduct EEG and fNIRS-hyperscanning during a complex collaboration task that brought together temporal constraints and elements of visuospatial reasoning.

Date: 05.09.2023 / 11:00 Place: A-212