Abstract
Technological advances integrating emotional maturity with established IoT systems are being examined with the emergence of the fourth industrial revolution. In this article, researchers propose an emotion-based music recommendation and classification framework (EMRCF) categorizing songs with high precision following individuals' interpersonal team with memory and emotional songs. In specific, when adding new tunes to an IoT app fortune, methods must be developed that immediately categorize the characters based on people's emotions . That's one of the essential questions for project management. The empathic framework is used to research to identify emotional information. Musical characteristics can be derived from discussions in a micro-enterprise with the task force. Correlation analysis and supporting neural network is used to perform dynamic designation. The innovative prediction accuracy proposed recognizes most of the emotional responses triggered by music audience members and effectively categorizes songs. Furthermore, a comparison study is made with proposed algorithms such as decision trees, deep cognitive system and neighbor-closest, and relevance vector machines. The EMRCF reaches the prediction accuracy of 96.12% and the precision rate of 96.69%, which is not achieved by existing approaches.