The aim of the study is to develop innovative intelligent technology and information systems of distance education for people with impaired vision (PIV). To solve this problem a comprehensive approach has been proposed, which consists in the aggregate of the application of artificial intelligence methods and statistical analysis. Creating an accessible learning environment, identifying the intellectual, physiological, psychophysiological characteristics of perception and information awareness by this category of people is based on cognitive approach. On the basis of fuzzy logic the individually-oriented learning path of PIV is con- structed with the aim of obtaining high-quality engineering education with modern equipment in the joint use laboratories.

Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resilience in the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained success of an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parameters in business, including investments and their impact on the financial health of the company. In this research, explored a variety of time series models and identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE values of 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretability of the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable development and prosperity of the enterprise in the long term.

Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-dimensional target–horizon problem covering three generation families and five forecast horizons: H1, H3, H6, H12, and H24. A leakage-safe data construction protocol generates causal predictors from meteorological observations, generation history, calendar cycles, lagged and rolling statistics, ramp descriptors, and physics-informed transformations. PI-BHTF partitions the 422-dimensional predictor space into solar, wind, temporal-calendar, and cross-context components, encodes them through parallel nonlinear branches, and combines the representations using cross-energy gated fusion. Its neural core uses a heteroscedastic predictive head and Monte Carlo dropout to distinguish input-dependent aleatoric uncertainty from epistemic variability, whereas the final hybrid forecasts are calibrated using residual quantiles computed from a chronologically held-out validation segment. Across three prespecified random seeds, the full PI-BHTF achieved a mean MAE of 0.0797 ± 0.0007 on the internal chronological test and 0.0686 ± 0.0006 on locked external SCADA validation. Performance and calibration varied substantially across target–horizon tasks, including marked short-horizon external undercoverage for wind generation. Component-wise ablation supported semantic feature partitioning and the heteroscedastic head, whereas the physics-informed features, cross-energy gate, and physics-consistency loss did not independently reduce aggregate MAE. PI-BHTF should therefore be interpreted as a reproducibly competitive framework that balances multi-horizon accuracy, structured representation, uncertainty estimation, and external transferability rather than as a universally dominant model.