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In this research, Fibre Reinforced Polymer artificial composites are fabricated as a high-strength and lightweight. The artificial fibre of e-glass fibre and fly ash powder are used as a particulate reinforcement for our application. The mechanical strength of these fibre composites is improved by analysis and testing with different compositions of resin, catalyst and accelerator presented in this fibre. The composite samples were fabricated at varying particulate weight content (%) and at room temperature. Finally, by this project we can analyse the tensile strength and impact strength of e-glass fiber and fly ash are improved. These composites can help us to achieve a better combination of properties. The characteristics of these composites are durable, low cost, low weight, high specific strength, non-abrasive, equitably good mechanical properties, Eco friendly and biodegradable.

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We cannot imagine our lives without music. Only commercially produced music is played for users. The selection of the main features is an enormously important issue for systems like facial expression recognition. The recommended strategy helps individuals in their musical listening by providing recommendations based on emotions, feelings, and sentiments. The seven facial emotion categories that have been considered are angry, disgusted, fear, pleased, sad, surprise, and neutral—are meant to be specifically allocated to each identified face. To classify the emotion, the object should be detected from an inputted image. The object can be recognized in the image using the Haar-Cascades technique. This algorithm can be defined in different stages: Calculating Haar Features, Creating Integral Images, BiLSTM, and Implementing Cascading Classifiers. A deep learning model called BiLSTM (Bidirectional Long Short-Term Memory) is used to categorize human emotion. Based on the predicted emotion the music is mapped and the playlist is recommended to the user. The k-means clustering algorithm is used to map the music to the expected emotion, as compared to the existing models the deep learning model BiLSTM will give the best performance and 86.5% accuracy