A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3758 - 3758

Published: March 29, 2025

Wind energy represents a solution for reducing environmental impact. For this reason, research studies the elements that propose optimizing wind production through intelligent solutions. Although there are address optimization of turbine performance or other indirectly related factors in production, remains topic insufficiently explored and synthesized literature. This how machine learning (ML) techniques can be applied to optimize production. aims study systematic applications ML identify analyze key stages optimized Through research, case highlighted by which methods proposed directly target issue power process turbines. From total 1049 articles obtained from Web Science database, most studied models context artificial neural networks, with 478 papers identified. Additionally, literature identifies 224 have random forest 114 incorporated gradient boosting about power. Among these, 60 specifically addressed aspect allows identification gaps The notes previous focused on forecasting, fault detection, efficiency. existing addresses indirect component performance. Thus, paper current discusses algorithms processes, future directions increasing efficiency turbines integrated predictive methods.

Language: Английский

Marketing Strategy Metamorphosis Under the Impact of Artificial Intelligence Services

Systems, Journal Year: 2025, Volume and Issue: 13(4), P. 227 - 227

Published: March 26, 2025

Companies’ marketing decision-making effectiveness depends on the quality of actions and time. In current digital era, any decision making must be timely in response to customers’ feedback, implementing artificial intelligence (AI) technology is one significant option. This paper focuses designing an Algorithm for Marketing Strategy Decision Making (AMSDM) that employs AI services process online feedback from customers regarding products companies’ websites or other e-commerce social media platforms. For this research, 1200 texts containing customer were analyzed by Azure Text Analytics service, which identifies types domains, subdomains, keywords it refers understands emotional tone attitudes conveyed responses through sentiment analysis techniques. The model performance was underlined computing Accuracy, Precision, Recall, F1-Score metrics both short long phrases feedback. Furthermore, integrated into a C# script extract frequency occurrence keywords. After that, AMSDM its advantages detailed. eliminates necessity manual intervention conserves time resources. Moreover, real-time nature allows companies respond promptly changing market dynamics preferences.

Language: Английский

Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4390 - 4390

Published: April 16, 2025

Air quality (AQ) is one of the most important urban environment indicators for life. The paper proposes a software solution predicting and forecasting air index (AQI) in areas. study integrates pollutant factors (CO, NO2, SO2, PM2.5), meteorological parameters (temperature, humidity, wind speed), traffic data to determine quality. For this purpose, 19 predictive models were developed compared: 12 machine learning algorithms, 7 deep learning, 1 model based on structural component analysis. Random Forest Regression model, customized within study, achieved best results, with an R2 score 99.59%, MAE 0.22%, MAPE 0.68%, OP (Overall Precision) 95.61%. It was subsequently validated unseen recorded mean deviation 0.58%. short-term AQI (5 days), AQIF 71.62%, 0.4%, 0.9%. proposed integrated into web application IoT infrastructure real-time alert mechanisms. Future directions include expanding dataset optimizing hyperparameters increase accuracy, as well integrating PM10 O3 factors, along degree industrialization demographic level.

Language: Английский