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Item Open Access Modeling and Forecasting DigitalCurrency Volatility with GARCH(1,1)(SDU University, 2025) Bizhigit Sagidolla; Maral Zholaman; Meruert Bilyalova; Ayagoz SagidollaThe burgeoning field of digital currencies presents unique challenges for predictive modeling due totheir inherent volatility and market dynamics distinct from traditional financial assets.We study the use of the GARCH(1,1) model to characterize and forecast the conditional volatility ofdaily Bitcoin returns. Using standard OHLCV data, we estimate a parsimonious GARCH(1,1) specificationand produce one-step-ahead volatility forecasts. We discuss model assumptions, stability conditions, andpractical considerations for risk metrics (e.g., VaR). The aim is to document a transparent, reproduciblepipeline rather than to compare exhaustively against alternative models. Results illustrate how a standardGARCH(1,1) specification can provide interpretable volatility estimates for Bitcoin, serving as a transparentbaseline rather than a novel predictive breakthrough.Item Open Access Development of sensor systems for floodwater monitoring and alerting(SDU University, 2025) Adilbek Sarsenov; Lyazzat Ilipbayeva; Ulzhalgas SeidaliyevaThis study addresses the systematic prediction of river water levels in Kazakhstan via hy-drological computations, which are essential for forecasting water-related events and formulatingplans for sustainable water resource management. Particular focus is placed on the significance ofprompt and efficient monitoring of river dynamics to alleviate natural disasters such as floods andmudflows, especially in high-risk places like Almaty, situated in geologically unstable mountainouslandscapes. The research focuses the potential of intelligent sensor-based monitoring systems thatcan gather real-time data on water levels, precipitation, soil moisture, and various environmentalconditions. Systems integrated with artificial intelligence and data analysis can substantiallyaugment decision-making processes, facilitate early warning mechanisms, and boost the precisionof forecasts. This method ultimately protects natural ecosystems and local communities from thedetrimental effects of hydrological hazardsItem Open Access Forecasting Student Academic Performance Using Machine Learning(SDU University, 2025) Danial Polat; Azamat SerekEducational data mining depends on accurate student academic outcome forecasting to detect studentswho need help early and receive specific support. Traditional linear models have been used extensively yetthey fail to detect the intricate non-linear patterns which exist in student achievement data. The evaluationof machine learning algorithms and their features for student outcome prediction in Portuguese secondaryeducation remains insufficient because of missing systematic assessments. The research investigates howLinear Regression and Random Forest and K-Nearest Neighbors perform when predicting Portugueselanguage grades from 649 student records containing 30 demographic and social and academic attributes.The evaluation of model performance used three established metrics which included Mean Squared Error(MSE) and R-Squared (R²) and Mean Absolute Error (MAE). The results showed Linear Regressionproduced the most accurate predictions through its lowest MSE (9.00) and MAE (2.30) values but its weakR² value (0.01) indicated poor explanatory power. The error rates of Random Forest matched those of LinearRegression (MSE = 9.48 and MAE = 2.34) yet its negative R² (-0.04) indicated poor generalization becauseof irrelevant features and suboptimal hyperparameters. The KNN model showed the worst results (MSE =11.10 and MAE = 2.57 and R² = -0.21) because it failed to detect important patterns without additionaloptimization. The results show that educational prediction tasks require both optimal feature selectionand parameter adjustment for successful results. The research shows that linear models perform betterthan complex methods in specific situations yet optimized non-linear models demonstrate superior abilityto understand student achievement complexity. The research provides essential guidelines for developingbetter feature engineering and machine learning approaches to predict educational results