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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE)
Komal Kumar Napa
Saveetha Engineering College
Computer Science & IT Education Engineering

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Investigating reading habits and their impact on reading performance among Indian undergraduate students Komal Kumar Napa; Rajkumar Govindarajan; Sathya Subramanian; Senthil Murugan Janakiraman; Nageswari Devana; Billa Manindhar
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i3.38490

Abstract

This study investigates the reading habits, genre preferences, and reading behaviors of undergraduate students and examines how these factors influence their reading performance. A total of 342 responses were directly collected from students through a structured questionnaire. Descriptive statistics revealed strong inclinations toward analytical genres such as mystery/thriller, science fiction, and true crime, while newspaper reading frequency remained low. Hypothesis testing showed no significant differences in reading scores across gender or academic departments. A significant positive correlation emerged between daily reading duration and newspaper reading frequency. Most notably, students who preferred analytical genres demonstrated significantly higher reading scores (Cohen’s d=1.36). Regression analysis further confirmed genre preference as the strongest predictor of reading performance. These findings highlight the importance of genre engagement and daily reading routines in enhancing reading comprehension and literacy development. The study offers meaningful implications for educators, curriculum designers, and reading intervention programs.
Machine learning-based solar power prediction for major Indian metro cities Komal Kumar Napa; Rajkumar Govindarajan; J. Senthil Murugan; Billa Manindhar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1362-1370

Abstract

The growing reliance on renewable energy has intensified the need for accurate solar power forecasting to support efficient grid operation and energy planning. However, reliable prediction remains challenging due to the strong dependence of solar power output on dynamic meteorological conditions. This study proposes a data-driven machine learning (ML) framework for high-precision solar power prediction across several major Indian metro cities. Using hourly weather and power generation data for the year 2023, a random forest regressor was developed to model complex non linear relationships between environmental variables and solar energy output. The proposed model achieved exceptional predictive performance, with an R² score of 0.9999 and a mean absolute error (MAE) of 0.15 kW, significantly outperforming conventional regression approaches. Feature contribution analysis revealed solar radiation as the dominant factor influencing power generation, while cloud cover and elevated temperatures exhibited negative effects. The key contribution of this work lies in demonstrating the robustness and generalizability of ensemble learning for urban-scale solar forecasting under diverse climatic conditions. The findings provide actionable insights for policymakers, grid operators, and energy planners to optimize solar integration and resource management.
Co-Authors Billa Manindhar Billa Manindhar J. Senthil Murugan Nageswari Devana Rajkumar Govindarajan Rajkumar Govindarajan Sathya Subramanian Senthil Murugan Janakiraman