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September 23, 2025Deleted JournalOpen Access

An Optimized Machine Learning Model for Automating Academic Scheduling

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Authors

BGBrinda GaneshUniversity of Maryland, College ParkPGPatricia GeorgeAmerican University of AntiguaHMH P Muruli

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Implication

This model improves academic scheduling efficiency using a genetic algorithm, suggesting a way to reduce human errors.

Key Points

  • The optimized model automates academic scheduling, leading to more accurate timetables and fewer human errors.
  • Utilizing a genetic algorithm resulted in significant time savings in the timetable development process.
  • Traditional scheduling methods require extensive manual work, often leading to inefficiencies and errors.
  • The integration of machine learning simplifies the scheduling process for schools and colleges, enhancing overall productivity.

Cite This Study

Ganesh et al. (2025) studied this question.

synapsesocial.com/papers/68d4757f31b076d99fa6ce0ehttps://doi.org/10.47392/irjaeh.2025.0535
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Timetable Generator Using Genetic Algorithm and Constraint Satisfaction Problem2025
  2. 2Revolutionizing Scheduling: A Comprehensive Analysis of Automated Timetabling Solution2024 · 1 citations
  3. 3An Automated Timetable Generation System for Academic Expertise Matching2024
  4. 4Automated Time – Table Generator Using Genetic Algorithm2024
  5. 5Automated Timetable Generation System Using Constraint Satisfaction and Genetic Algorithm.2026