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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Applied Data Sciences International Journal Research on Metaverse
Batumalay, Malathy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Harnessing Sentiment Analysis with VADER for Gaming Insights: Analyzing User Reviews of Call of Duty Mobile through Data Mining Batumalay, Malathy; S, Priya; Kumar, Vinoth
International Journal Research on Metaverse Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i2.27

Abstract

This study investigates the application of sentiment analysis to understand user feedback for Call of Duty Mobile, a highly popular mobile game, by analyzing 50,000 reviews sourced from the Google Play Store. The research aimed to extract actionable insights from user sentiments, which could guide future game development and improvement. To achieve this, the sentiment of each review was analyzed using VADER (Valence Aware Dictionary and sEntiment Reasoner), a robust tool for classifying sentiment in textual data. The study categorizes reviews into three sentiment groups—positive, negative, and neutral—to identify and analyze prevailing user emotions. The findings revealed that the majority of reviews were positive, with users primarily praising the gameplay, graphics, and overall mobile experience. These aspects were considered crucial in driving user satisfaction and contributed to a majority of the positive feedback. Conversely, negative reviews were often focused on issues such as network connectivity problems, long loading times, and performance errors, indicating areas where users experienced frustration. These results highlight the importance of technical performance and network stability as key factors influencing player satisfaction. The study also delved deeper into keyword analysis to uncover common themes in the reviews, such as in-app purchases and concerns related to technical performance, which were frequently mentioned by users in both positive and negative feedback. These insights provide developers with a clearer understanding of what players value most in the game and where improvements are necessary. The study concludes that sentiment analysis can serve as a powerful tool for understanding user feedback, offering developers a data-driven approach to enhance game features and address user concerns. Moving forward, future research could benefit from the application of additional machine learning models to refine sentiment classification accuracy, as well as the integration of cross-platform reviews to gain a more comprehensive understanding of player sentiment across different user groups and devices. Such approaches would provide a richer, more nuanced view of user experiences, enabling game developers to create even more engaging and satisfying gaming experiences.
Step size variability with high performance solar-wind grid integration using MPPT algorithm Dhandapani, Lakshmi; Sreenivasan, Pushpa; Batumalay, Malathy
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2655-2664

Abstract

This paper proposes a high-efficiency maximum power point tracking (MPPT) algorithm based on a variable step size control technique for standalone hybrid solar-wind energy systems. Unlike conventional approaches that utilize separate MPPT controllers for photovoltaic (PV) and wind systems, the proposed method integrates a single adaptive control strategy that simultaneously optimizes power extraction from both renewable sources. The algorithm dynamically adjusts the step size according to environmental variations, improving convergence speed and tracking accuracy. The system is modeled in MATLAB/Simulink, incorporating a 500 W solar PV system and a 560 W wind turbine, both interfaced through traditional boost converters. To validate the performance, simulations are conducted under varying solar irradiance levels (600 W/m², 800 W/m², and 1000 W/m²) and wind speeds (8 m/s, 10 m/s, and 12 m/s). Results indicate that the PV output power increases from 288.8 W to 513 W with rising irradiance, while the wind output improves from 301.4 W to 439.3 W with increasing wind speed. The combined hybrid system achieves total output powers of 557.35 W, 691.74 W, and 807.12 W across three operating intervals. These findings confirm that the proposed variable step size MPPT algorithm significantly enhances energy harvesting efficiency and system performance under dynamic environmental conditions.
Comparative Study of CNN-Based Architectures for Early Brain Tumor Diagnosis D, Lakshmi; C, Pragash; Batumalay, Malathy; R, Karthick Manoj
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.920

Abstract

This study presents a comprehensive comparative analysis of Convolutional Neural Network (CNN)-based deep learning architectures for early brain tumor detection and classification using multi-modal medical imaging. The primary objective is to evaluate and integrate advanced deep neural network models, including EfficientNet-B2, VGG16, U-Net, and a hybrid CNN-LSTM, to enhance diagnostic accuracy, precision, and robustness. The proposed framework involves five key stages: image acquisition from MRI, CT, PET, and ultrasound modalities; preprocessing through normalization, skull stripping, noise reduction, and registration; segmentation of tumor regions; feature extraction; and classification using optimized deep learning algorithms. Experimental evaluation demonstrates that the hybrid CNN-LSTM model achieved the highest overall performance, with an accuracy of 98.81%, precision of 98.90%, recall of 98.90%, and F1-score of 99%. The EfficientNet-B2 model followed closely with 98.73% accuracy, 98.73% precision, 99.13% recall, and 98.79% F1-score, confirming its strength in efficient feature utilization and computational scalability. In contrast, VGG16 and U-Net achieved accuracies of 93.27% and 88%, respectively, indicating limited adaptability to complex tumor morphologies. The findings reveal that CNN-based hybrid models outperform traditional architectures by effectively capturing both spatial and temporal dependencies in MRI data, leading to improved interpretability and clinical reliability. The novelty of this research lies in its methodological integration of convolutional and recurrent layers within a unified diagnostic framework, establishing a reproducible, high-performance model for early brain tumor detection. The study contributes to the advancement of intelligent medical imaging systems by demonstrating that hybrid deep learning architectures can significantly reduce diagnostic uncertainty and enable more precise, automated clinical decision support for early intervention.
Hybrid Ensemble Learning for Anomaly Detection in Metaverse Transactions Using Isolation Forest, Autoencoder, and XGBoost Prakash, S.; Mary, S. Aruna; Sudhagar, G.; Batumalay, Malathy
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.46

Abstract

The rapid expansion of metaverse platforms has increased the volume and complexity of digital transactions, creating a greater need for reliable anomaly detection systems. This study proposes a hybrid ensemble learning framework that integrates Isolation Forest, Autoencoder, and XGBoost using a meta learning approach to detect anomalous transactions in metaverse environments. The framework combines unsupervised and supervised learning to identify structural irregularities, behavioral deviations, and contextual patterns associated with high-risk activities. Using a transaction dataset containing behavioral, contextual, and numerical features, the hybrid model was evaluated against its individual components. The results show that the proposed framework achieves superior accuracy, precision, recall, and ROC AUC values compared to standalone models. The analysis of feature importance indicates that quantitative variables, including transaction amount, session duration, and risk score, provide the strongest predictive contribution, while contextual and behavioral factors improve model interpretability and generalization. Principal Component Analysis further visualizes the separation between normal and anomalous clusters, confirming that the hybrid ensemble effectively captures latent relationships within high-dimensional transaction data. Overall, the findings demonstrate that the proposed approach provides a robust and scalable solution for detecting irregular patterns in metaverse-based blockchain transactions. This model also offers practical implications for real-time financial risk assessment and digital security management in decentralized virtual economies.
Co-Authors B., Kalaiselvi Balajia, R S Lakshmi C, Pragash D, Lakshmi Devadas, Amar Dev Thirukulam Dewi, Deshinta Arrova Dhandapani, Lakshmi Duraimutharasan, N. Habib, Shabana Joseph, Abin John K., Sivachandar K., Suresh Kumar Kukreja, Harsh Kurniawan, Tri Basuki Lakshmi Balaji, R S Mary, S. Aruna Muhammad Islam, Muhammad Nagrath, Preeti Prakash, S. R, Karthick Manoj R., Karthick Manoj R., Nishanth S, Priya S., Aasha Nandhini S., Dhivya S., Lakshmi S., Prakash S., Priya Saini, Akash Saluja, Dhruv Sreenivasan, Pushpa Sudhagar, G. T., Ananth kumar Tegwal, Devanshi Thinakaran, Rajermani Thiruvenkataswamy, C S Vinoth Kumar Yingthawornsuk, Thaweesak