Article Per Year (5 Year)
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) Bulletin of Electrical Engineering and Informatics
Komal Kumar Napa
Saveetha Engineering College
Computer Science & IT Education Electrical & Electronics Engineering 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.
Feature-guided transformer approach for detecting distributed denial of service attacks Lokeshwaran Kanagaraj; Raguraman Purushothaman; Sathya Subramanian; Durga Devi Saravanan; Komal Kumar Napa; Cornelius Karunakaran; Billa Manindhar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3712-3721

Abstract

Distributed denial of service (DDoS) attacks continues to pose serious risks to modern networks, with their growing intensity making early detection both critical and challenging. Conventional machine learning (ML) models often struggle with the nonlinear and highly dynamic nature of attack traffic, which motivates the use of advanced architectures. In this study investigate a transformer-based classifier for DDoS detection on the CIC-DDoS2019 dataset. The workflow included preprocessing, feature scaling, and domain-guided feature selection. Logistic regression (LR) was employed as a baseline, achieving 92.1% accuracy and F1-score of 0.90, thereby revealing the limitations of linear models. The transformer, after hyperparameter tuning and 5-fold cross-validation, reached an average accuracy of 99.95% with precision, recall, and F1-scores all above 99.9%. The model demonstrated stable convergence and generalization across folds. These results highlight the strength of attention mechanisms in capturing feature dependencies, while also pointing to future directions such as real-time deployment, explainability, and resilience to zero-day attacks.
An optimized deep learning framework for brain tumor classification using magnetic resonance imaging Komal Kumar Napa; Rajkumar Govindarajan; Senthil Murugan Janakiraman; Jayanthi Arumugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3722-3731

Abstract

Accurate and interpretable classification of brain tumors in magnetic resonance imaging (MRI) scans plays a crucial role in early diagnosis and effective treatment planning. This study introduces a deep learning (DL) framework based on a customized YOLOv5m architecture integrated with a bidirectional feature pyramid network (BiFPN) for multi-class brain tumor classification. The integration of BiFPN enhances multi-scale feature fusion, improving detection across varied tumor types, while YOLOv5m ensures real-time inference capabilities. To mitigate class imbalance, a class-weighted cross-entropy loss is adopted. The model is evaluated on multiple performance metrics, achieving a test accuracy of 88.86%, precision of 88.70%, recall of 88.20%, and F1-score of 88.25%. It also reports a mean average precision (mAP@0.5) of 94.36%, with high class-wise average precision (AP) for glioma, meningioma, pituitary, and no-tumor categories. Computational time for training (12484.81 seconds) and testing (146.82 seconds) confirms the model’s feasibility for real-time clinical deployment. To support interpretability, gradient-weighted class activation mapping (Grad-CAM) is integrated for visualizing class-discriminative regions, helping clinicians understand the model’s predictions. A gradio-based user interface is also developed, enabling intuitive interaction with the system.
Enhanced air quality index classification: leveraging genetic algorithm and SMOTE for accurate assessments in Indian cities Komal Kumar Napa; Ayodeji Olalekan Salau; Angati Kalyan Kumar; Sepiribo Lucky Braide; Aitizaz Ali; Ting Tin Tin
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.9996

Abstract

The escalating air pollution levels in Indian metropolitan regions necessitate robust predictive systems for air quality assessment. This study presents an advanced air quality index (AQI) forecasting model leveraging the radial basis function (RBF) kernel-based extreme learning machine (ELM) optimized using a genetic algorithm (GA). The proposed model is evaluated on real-time AQI datasets from four major Indian cities: Vishakhapatnam, Delhi, Hyderabad, and Patna. Initial experiments without class balancing yielded prediction accuracies of 83.9%, 88.3%, 87.0%, and 86.9% respectively. To address the class imbalance and enhance predictive performance, the synthetic minority oversampling technique (SMOTE) was applied. Post-balancing, the model achieved significantly improved accuracies of 93.9%, 94.7%, 92.3%, and 96.2% across the respective cities. These results underscore the effectiveness of integrating SMOTE with RBF-ELM for AQI prediction and demonstrate the critical role of data balancing in improving model generalizability. The proposed approach offers a promising solution for urban air quality monitoring and can assist policymakers in formulating timely interventions to mitigate health risks associated with air pollution.
Co-Authors Aitizaz Ali Angati Kalyan Kumar Ayodeji Olalekan Salau Billa Manindhar Billa Manindhar Cornelius Karunakaran Durga Devi Saravanan J. Senthil Murugan Jayanthi Arumugam Lokeshwaran Kanagaraj Nageswari Devana Raguraman Purushothaman Rajkumar Govindarajan Rajkumar Govindarajan Rajkumar Govindarajan Sathya Subramanian Sathya Subramanian Senthil Murugan Janakiraman Senthil Murugan Janakiraman Sepiribo Lucky Braide Ting Tin Tin