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Item Open Access Mitigating Bias in AI-Based Loan Approval Systems through Fairness-Centric Techniques(SDU University, 2025) Raziyeva S.As artificial intelligence (AI) becomes increasingly embedded in high-stakes decision-making systems, ensuring fairness in algorithmic outcomes has emerged as a critical concern. This thesis investigates bias and fairness in AI-based credit scoring systems, with a particular focus on gender disparities. Using the German Credit Dataset as a case study, the research evaluates the performance and fairness of several supervised machine learning models, including Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine, and Neural Network. The study applies fairness metrics such as Statistical Parity Difference (SPD) and Disparate Impact (DI) to assess group-level inequalities in predicted loan approval outcomes. Results reveal a consistent trade-off between model accuracy and fairness, where high-performing models like Random Forest and XGBoost demonstrate notable biases against female applicants. Even interpretable models, such as Logistic Regression, exhibit fairness issues due to historical and structural biases embedded in the training data. To address these challenges, the thesis highlights the importance of incorporating fairness-aware strategies across the machine learning pipeline, including data pre-processing, fairness evaluation, and potential post-processing mitigation. The use of tools like AIF360 and stratified sampling further strengthens the analysis. This research contributes to the growing discourse on responsible AI by demonstrating that achieving fairness is not merely a technical goal but a sociotechnical imperative. It calls for an interdisciplinary approach that combines ethical reasoning, regulatory compliance, and algorithmic transparency to ensure equitable access to financial services. The findings advocate for the development of AI systems that are not only accurate but also accountable and inclusive.Item Open Access Personalized Career-Path Recommender System for STEM Students(Faculty of Engineering and Natural Science, 2024) Zhalgassova Zh.This dissertation introduces a Personalized Career-Path Recommender System (PCRS) designed to help high school students in Kazakhstan, particularly those interested in STEM (Science, Technology, Engineering, and Mathematics) fields. The system uses the Myers-Briggs Type Indicator (MBTI) personality types and students’ academic performance to offer personalized recommendations for university specializations. The research addresses the common challenges faced by students, such as high dropout rates and frequent changes in majors, often due to the lack of structured career guidance. To tackle these issues, the study collected a variety of data, including students’ demographics, academic records, and personal attributes, as well as detailed profiles of university majors. Advanced machine learning techniques, including content-based filtering, collaborative filtering, fuzzy logic, and hybrid approaches, were used to process this data and generate accurate recommendations. The effectiveness of the PCRS was tested with real data from students at SDU University. The results show that the system can provide relevant and personalized career guidance, significantly improving students’ decision-making processes and satisfaction with their chosen specializations. By combining MBTI personality assessments with academic performance data, this research offers a fresh approach to educational technology and career counseling. The insights and methods developed in this study can be adapted for use in other regions facing similar challenges, ultimately helping more students make informed and satisfying career choices.Item Open Access Integration of Machine Learning for Enhanced Efficiency in Industrial Systems: Analysis, Forecast, and Perspectives(SDU University, 2026) Borangazin T.Contemporary industrial systems exhibit a pronounced dependence on extensive data, thereby requiring sophisticated analytical frameworks to facilitate decisionmaking under conditions of uncertainty. Conventional statistical methodologies are frequently constrained in their ability to encapsulate the non-linear, dynamic, and multi-factorial interactions characteristic of complex industrial ecosystems. This Master’s thesis undertakes an exploration into the integration of advanced machine learning paradigms to augment the efficacy of industrial forecasting and property prediction endeavors, as evidenced by two pertinent case studies. The initial case study undertakes an investigation into the efficacy of supervised learning models for the prediction of international crude oil prices. A suite of algorithms, including linear regression, tree-based ensembles, and recurrent neural networks, were subjected to training and evaluation utilizing historical price data in conjunction with pertinent macroeconomic indicators. The study substantiates that non-linear machine learning models yield superior predictive accuracy relative to traditional baselines, with a notable advantage observed during periods of market volatility. The second case study delves into predicting mineral properties, with a particular emphasis on Mohs hardness, by leveraging tabular datasets that capture chemical composition and physical measurements. The study develops regression models and a basic generative model to evaluate the role of machine learning within materials informatics. The generative model underscores the feasibility of generating synthetic mineral-like feature vectors, representing an early step towards data augmentation and the concept of inverse design. For both case studies, the dissertation outlines a standardized methodology that involves preparing data, crafting features, training models, validating them, and evaluating performance with metrics like MAE, RMSE, and (R2 ) . The findings illustrate how versatile machine learning is for different industrial data types and suggest that data-driven models offer exciting possibilities for integration into current industrial practices.