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@article{203304,
        author = {Ms. Meghna Dohare and Sonali Guhe and Om Patle and Aastha Zade},
        title = {SkillArchive : Skill Decay & Knowledge Retention Tracker},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12255-12260},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203304},
        abstract = {SkillArchive is an online platform for predicting long-term skill retention and decay rates following initial learning. It pushes the frontiers in three broad areas: (1) a skill retention tracking model that incorporates mandatory last-used timestamps and scheduled confidence logging to model skill retention patterns over time, (2) a light-weight, interpretable AI component that analyzes skill retention gaps and confidence patterns to predict decay intervals and label skills as stable, decaying, or improving, and (3) an automated, privacy-friendly notification component that proactively sends revision suggestions through scheduled emails without continuous monitoring. Unlike most platforms, which are acquisition or assessment-oriented, SkillArchive is uniquely proactive in skill retention with a secure MERN-stack infrastructure. Performance tests demonstrate API call times of less than 150 ms and sound automated prediction and notification performance even under concurrent usage.},
        keywords = {Skill Decay, Knowledge Retention, Confidence Tracking, Temporal Analysis, Explainable AI, Predictive Modeling, Skill Maintenance, Automated Notifications, MERN Stack, Professional Development.},
        month = {May},
        }