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@article{177982,
        author = {Parth Kalekar and Poonam Pathak and Aryan Patil and Bhumik Narkhede},
        title = {SECURE AND AI BASED STUDENT CONTRIVANCE SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3270-3275},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177982},
        abstract = {In the current academic world, blending Artificial Intelligence (AI) with cyber security is essential for making smart decisions and keeping data safe. This paper introduces the "Secure and AI-Based Student Contrivance System," a thorough academic support platform that uses machine learning and secure communication to boost student performance, transparency, and data privacy. The system is built around three key components: (1) an AI-driven prediction module that examines important academic metrics to anticipate student outcomes; (2) a dynamic policy-making and voting module that allows stakeholders to work together on educational strategies like grading systems and performance criteria; and (3) a secure, cloud-based document storage and expert guidance system that features key-based access control and encrypted communications. This solution not only gives students valuable insights into their performance and mentorship but also guarantees the confidentiality of academic data. Experimental results and user feedback show that the system is effective in enhancing engagement, academic planning, and data security.},
        keywords = {AI-Based Student Performance Prediction, Machine Learning in Education, Secure Communication in Academia, Cloud-Based Secure Document Storage, Academic Policy Voting System, Student-Expert Interaction Platform.},
        month = {May},
        }