to download project abstract of plagiarism detection

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At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.


Firstly, In the ever-evolving landscape of digital music, the need for advanced recommendation systems has become paramount. Introduces innovative music recommendation with robust plagiarism detection beyond traditional algorithms.


Online music platforms offer abundant choices, emphasizing crucial personalized recommendations. Our proposed system not only focuses on enhancing music discovery but also addresses the growing concern of plagiarism within the music industry.


Our approach integrates collaborative filtering techniques for personalized music recommendations based on user preferences. Simultaneously, we employ a sophisticated plagiarism detection algorithm that analyzes music content to identify similarities and potential instances of intellectual property infringement.


The recommendation system employs an active learning model, continuously adapting to both user behavior and preferences. By utilizing a Haar cascade-based algorithm, the system performs efficient and rapid face detection in user profile pictures, further enhancing the accuracy of personalization.

To ensure the integrity of the recommended content, the system employs both advanced audio fingerprinting and spectrogram analysis. This multi-faceted approach allows for the identification of music pieces with high similarity, contributing to the prevention of unauthorized duplication and distribution.

Results and Evaluation:

Preliminary testing of the system demonstrates promising results in terms of recommendation accuracy and plagiarism detection efficiency. User feedback and engagement metrics further validate the system’s effectiveness in delivering personalized recommendations while maintaining content originality.


In Conclusion, The proposed Music Recommendation System represents a significant advancement in the realm of digital music platforms. Ongoing refinement and scalability considerations position this system as a valuable tool in the ever-expanding landscape of digital music consumption.

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