In the era of social networking, understanding the insights of the market is very important for profitable investments. Sentiment Analysis helps us figure out these aspects. It is valuable for understanding how people's emotions affect stock market-based decisions. This study enables a direction of AI, based on sentiment analysis, particularly, Emotionally Intelligent (EI) Sentiment Analysis of stock tweets to help us make smart investment choices. The research aims further about the impact of how emotions participate equally in financial markets by collecting a bunch of stock-related tweets from social media. Using an applied algorithm, these tweets are analyzed to find out what emotions people have and their opinions find gains or losses in financial decisions. The study further aims to find a connection between the emotions in tweets and what happens to stock prices later on. By seeing patterns, dedicated machine learning models help us to decide where to invest. This is important for investors, people who study stocks, and the people who watch over the markets.

Cardiovascular diseases are the heart-related illnesses. If not treated, that often turns into serious and complex conditions that often result in the death of an individual. It is one of the top causes of death globally. The heart pumps blood throughout the body. This research focuses on predictive analysis and the determination of the severity of heart disease using machine learning algorithms. The datasets are selected then feature selection is the major step of preprocessing, which involves mechanisms to use impactful columns of the data set for experiments. Feature selection techniques include technical methods like the Pearson Correlation Coefficient, Relief, Minimum Redundancy Maximum Relevance, and Least Absolute Shrinkage and Selection Operator. Any of them generate results based on high relevancy and information gain. Choosing relevant features,due to the existence of labeled data, the classification technique is ideal. The supervised learning algorithms are applicable for this prospective. There also exists a proposed ensemble method algorithm. This approach results in an accuracy of 94.74%. The research is significant since it is helpful to determine early heart disease detection, which is an important aspect of early disease detection, which may save lives by early diagnosis in the future.

This research presents a web-based interactive system designed to facilitate engaging learning experiences for children facing challenges in cognitive, sensory, visual-perceptual, colour recognition, and hand-eye coordination, particularly in the context of occupational therapy. The system offers a variety of interactive tasks and games aimed at enhancing developmental skills. Distinct from existing applications, it incorporates voice assistance to improve comprehension and emphasizes cognitive and visual skill development. Key modules include activities such as alphabet tracing, toy identification, fruit recognition, and number learning. While primarily intended for children, the application also serves as a resource for parents and guardians to support their child's progress. By providing an engaging platform, this system aims to foster cognitive, perceptual, and visual skills through interactive game play. The findings underscore the potential of such interactive tools to create immersive learning environments that encourage active participation and sustained interest, ultimately contributing to improved educational outcomes. This research offers valuable insights for educators and developers seeking to leverage technology for enhanced cognitive development in children.