Recent research shows that humans respond to music and that music has a significant impact on brain activity. Every day, the average person listens to up to four hours of music. People usually listen to music that matches their mood and interests. This project focuses on developing an application that uses facial expressions to propose songs based on the user's mood. Nonverbal communication takes the form of a facial expression. The Emotion-based music player project is a revolutionary concept that allows users to automatically play songs based on their feelings. It recognises the user's facial expressions and plays music that matches their mood. Computer vision is an interdisciplinary tool that allows computers to analyse digital images or movies at a high level. The computer vision components of this system employ facial expressions to assess the user's emotion. When an emotion is recognised, the system offers a playlist for that emotion, saving the user time from having to manually select and play songs.

Heaviness has been related to stroke, depression, and cancer are some of the most serious dangers to human existence. Heart disease, stroke, obesity, and type II diabetes are all disorders that have an impact on our way of life. Using data mining and machine learning approaches to forecast disease based on patient treatment history and health data has been a battle for decades. Many studies have used data mining approaches to forecast specific diseases using pathology data or medical profiles. These methods attempted to predict disease recurrence. Based on historical data from a multi-label classification problem, the review will focus on chronic disease prediction using various techniques such as convolutional neural networks (CNN), heterogeneous convolutional neural networks (HCN), and recurrent neural networks (RNNs). The study examines the current state of the art approaches for action recognition and prediction, as well as the future possibilities of the research.

Recently, in the whole world, enormous electric energy is consumed by the street lights. These lights are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be controlled. Street lights in India consume approximately 20-40% of the electrical energy produced in the entirenation and the demand for electricity in recent years has increased day by day. In this paper, smart street light is introduced which is IoT based, it aims to automate the light system, also, LEDs are used to assures the low power consumption. The operation of this system is to maintain the intensity of streetlights to 40% of the maximum intensity if no vehicles passing through the road. Electricity theft problem is also address in this paper uses a sudden signal of power drop or phase drop to detect the exact pole at which electricity theft is happening or if that particular street light is faulty. Moreover, all these data are shared through IoT about and can be controlled and monitored by a mobile application