Published Paper Details:

Music is a fantastic way for people to express themselves as well as a good source of enjoyment for music fans and listeners. Furthermore, relaxing music is an effective technique for evoking strong emotions and sending a quiet message. With technological advancements, the number of artists, their music, and music listeners is growing, which brings up the issue of manually exploring and picking music. This study offers a system that uses facial expressions at real time of a user to assess the user's mood (Emotion detection Model), output of which is then combined with mapped music from the music dataset to create a user specific music playlist (music recommendation model).Convolutional neural network is used to classify the users emotions in 7 different categories with an accuracy rate of 94 percent, thus satisfying the actual aim of the study.In the realm of personalized content recommendation systems, infusing humor adds a unique and entertaining dimension to user interactions. This paper introduces a Joke Reading Module, an innovative extension to a music recommendation system based on facial expressions using deep learning. The module aims to enhance user engagement by dy namically delivering jokes aligned with users' moods and humor preferences. Once the system has identified the user's mood and humor type, deliver a short joke or humorous comment along with the music recommendations .Ensure that the jokes are appropriate and consider cultural sensitivities. Develop or obtain a dataset of jokes categorized by humor types or styles (e.g., puns, wordplay, sarcasm).Train a natural language processing (NLP) model to understand the structure and context of jokes. Create an association between facial expressions and humor preferences based on the user's previous interactions with jokes. Integrate the NLP model with the facial expression recognition system to understand the user's mood and sense of humor. Classify users into different humor categories based on their facial expressions.