Browsing by Author "Borangazin T."

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    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.