Personalized medicine recommendation systems are increasing in popularity to predict diseases and provide customized health advice on diet, workout plans and medication. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. Through the use of machine learning algorithms like Decision Tree, Random Forest, K-Means Clustering, and Hierarchical Clustering, these systems analyze patient inputs such as lifestyle (...) data, symptoms, and health metrics for accurate predictions of diseases and holistic recommendations on health. This inclusive process ensures each person receives tailored support to enhance the entire management of their health. The system's ability to suggest accurate diets, proper workouts, and appropriate drugs depending on the condition of the user significantly enhances its contribution toward more healthy lifestyles. The variety of algorithms increases accuracy and reliability as well because each model contributes uniquely in analyzing various aspects of patient data. This study presents a framework that demonstrates the system's efficacy in providing personalized disease predictions and health recommendations, which can benefit the development of preventive care and improve treatment outcomes. (shrink)

The proposed sensor device for dragline operator is designed to enhance environmental safety by detecting and alerting operators to climate change impacts and potential hazards. Utilizing advanced sensor technology, this device continuously monitors weather conditions, such as heavy rain, strong winds, and extreme temperatures. When a hazard is detected, the device provides real-time alerts through an intuitive interface, enabling operators to take immediate action to mitigate risks. This proactive approach ensures operators are well-informed about their surroundings, significantly reducing the risk (...) of accidents and promoting a safer working environment. Additionally, the sensor device seamlessly integrates with existing dragline systems, ensuring compatibility and ease of adoption. By enhancing situational awareness and facilitating timely responses to environmental threats, this sensor device represents a critical advancement in the safety and efficiency of dragline operations in the mining and construction indus. (shrink)

This project aims to develop an automated price comparison tool that addresses the inefficiencies of the current manual system used to compare prices between the Government e-Marketplace (GeM), Amazon, and Flipkart. In the existing process, users must visit each platform individually to compare prices, a time-consuming and error-prone method that often leads to missed opportunities for better deals. This project proposes a solution by creating a user- friendly tool capable of aggregating and displaying real-time product pricing from multiple e-commerce platforms (...) in one interface. The tool will enable users—whether government procurement officials or individual buyers—to quickly identify the most costeffective option for their purchase by providing instant price comparisons. In addition to price, the system will consider other relevant factors like product availability, shipping times, and user reviews, ensuring a comprehensive evaluation. By integrating this information into a single platform, the project aims to streamline procurement processes, reduce manual effort, and foster more informed purchasing decisions. This solution holds significant potential for public sector efficiency, allowing government bodies to optimize their procurement strategies and reduce costs. In the private sector, it offers individual consumers and businesses a reliable method to identify the best deals across platforms, contributing to smarter, more economical purchasing. (shrink)