Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.

Employee satisfaction is a crucial factor affecting organizational performance, productivity, and overall workplace efficiency. This study investigates employment satisfaction within the Greek technology sector through sentiment analysis, focusing on employees’ responses through the Employee Experience-Satisfaction (EmEx-Sa) questionnaire. The study employs natural language processing (NLP) and, in particular, the lexicon-based sentiment analysis methodology to analyze data from 208 employees across the entirety of Greece, obtained from open and semi-open questions, multiple-choice alternatives, and demographic questions. The objective is to utilize data from sources such as the Michigan Organizational Assessment Questionnaire (MOAQ), Minnesota Satisfaction Questionnaire (MSQ) and Job Descriptive Index (JDI) to determine the primary elements that impact job satisfaction and, by applying principles of organizational ergonomics, gain insight into the attitudes and emotions of employees. Results reveal that the working environment (total sentiment score: 21.50) is the primary driver of positive sentiment, while salary (total sentiment score: −18.72) is the main source of dissatisfaction. Sentiment regarding superiors is more balanced, leaning slightly positive (total sentiment score: 0.02), but the analysis indicates opinions lack significant polarization. The findings delineate critical factors influencing job satisfaction, encompassing the work environment, leadership quality, salary, and opportunities for professional advancement. The research underscores the significance of internal marketing tactics in fostering engagement, trust, and transparency between management and employees and provides actionable suggestions for boosting working conditions, fostering employee well-being, and improving organizational performance, underscoring the strategic imperative of prioritizing job satisfaction.

This study explores the application of machine learning techniques in predicting safety attitudes among aviation maintenance personnel. Personality traits and demographic information are used for this purpose, with data obtained from an online dataset. The Random Forest machine learning algorithm was utilised to identify the relationships and to enable predictions. The obtained results indicated that extraversion had the most positive influence, followed closely by openness. On the other hand, neuroticism had the most negative impact. Total years of experience and experience in the current role are, on the other hand, the most influential demographic information. Combining personality traits with demographic information can improve safety attitude pre dictions. Nevertheless, definitive causal inferences cannot be established, as further analysis is required to verify the suitability of the Random Forest algorithm relative to other machine learning algorithms.