This paper conducts an intricate analysis of musical emotions and trends using Spotify music data, encompassing audio features and valence scores extracted through the Spotipi API. Employing regression modeling, temporal analysis, mood transitions, and genre investigation, the study uncovers patterns within music-emotion relationships. Regression models linear, support vector, random forest, and ridge, are employed to predict valence scores. Temporal analysis reveals shifts in valence distribution over time, while mood transition exploration illuminates emotional dynamics within playlists. The research contributes to nuanced insights into music's emotional fabric, enhancing comprehension of the interplay between music and emotions through years.
Financial institution are facing thoughtful challenges with loud rising number of credit card fraud occurrences across the globe. Although fraud has been a leading problem since the beginning of time, its operations in this present-day consent with various classes of people due to the availability of internet. Consequently, the matter of security is primary motivation […]
The explosive increase in the adoption of Information Technology in electronic commerce and other facets of human life has resulted in corresponding increase in concerns for security and privacy of parties involved. The very frequent and frightening rate at which data or users’ privacy breaches occur today implies that carrying out online transactions can pose […]
This paper investigated the impact of COVID-19 pandemic on information technology, telecommunication and social technologies use in information sharing and dissemination as well as in teaching and learning. The pandemic changed the world-view and general operations in technology and secular activities. The outbreak of the pandemic brought to the fore the use of virtual devices […]
