Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. According to the World Health Organization (WHO), primary risk factors include elevated blood pressure, glucose, blood lipids, and obesity. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities, often relying on predictive models trained on large datasets, which require intensive training. An intelligent system for CVDs patients could greatly assist in making informed decisions by effectively analyzing health parameters. Complex Event Processing (CEP) has emerged as a valuable method for solving real-time challenges by aggregating patterns of interest and their causes and effects on end users. In this work, we propose a fuzzy rule-based system for monitoring clinical data to provide real-time decision support. We designed fuzzy rules based on clinical and WHO standards to ensure accurate predictions. Our integrated approach uses Apache Kafka and Spark for data streaming, and the Siddhi CEP engine for event processing. Additionally, we pass numerous cardiovascular disease-related parameters through CEP engines to ensure fast and reliable prediction decisions. To validate the effectiveness of our approach, we simulated real-time, unseen data to predict cardiovascular disease. Using synthetic data (1000 samples), we categorized it into "Very Low Risk, Low Risk, Medium Risk, High Risk, and Very High Risk." Validation results showed that 20% of samples were categorized as very low risk, 15-45% as low risk, 35-65% as medium risk, 55-85% as high risk, and 75% as very high risk.

Forests are crucial for ecological balance, but wildfires, a major cause of forest loss, pose significant risks. Fire weather indices, which assess wildfire risk and predict resource demands, are vital. With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are increasingly used to gather climatic data such as wind speed, temperature, and humidity. However, processing these data streams to determine fire weather indices presents challenges, underscoring the growing importance of effective forest fire detection. This paper discusses using Apache Spark for early forest fire detection, enhancing fire risk prediction with meteorological and geographical data. Building on our previous development of Semantic Sensor Network (SSN) ontologies and Semantic Web Rules Language (SWRL) for managing forest fires in Monesterial Natural Park, we expanded SWRL to improve a Decision Support System (DSS) using a Large Language Models (LLMs) and Spark framework. We implemented real-time alerts with Spark streaming, tailored to various fire scenarios, and validated our approach using ontology metrics, query-based evaluations, LLMs score precision, F1 score, and recall measures.