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All Journal Bulletin of Electrical Engineering and Informatics
Lakshminarayana Munirathnam
M.S. Ramaiah Institute of Technology
Electrical & Electronics Engineering

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Hybrid machine learning model towards neuroimaging analysis for detection and grading brain tumors Lakshminarayana Munirathnam; Rashmi Shivaswamy; Suraksha Prakash; Reshma Shafi; Karanam Sunil Kumar
Bulletin of Electrical Engineering and Informatics 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/eei.v15i3.10988

Abstract

Neuroimaging analysis enables detailed observation of brain tumors, with growing adoption of advanced imaging techniques in clinical practice. Limitations of conventional approaches in supporting proactive decisionmaking and reliable grading are increasingly addressed through machine learning. However, earlier models often faced challenges of limited generalization and computational burden. To overcome these issues, this study introduces a hybrid convolution neural network–support vector machine (CNN–SVM) framework that combines ResNet-50 feature extraction with a feature weighting (FW) strategy and SVM-based classification for improved diagnostic precision. The system is further enhanced with a clinically guided grading scheme, mapping classification outputs into malignant, benign, and healthy categories for greater interpretability. The proposed model was evaluated on three benchmark neuroimaging datasets (Figshare, Kaggle magnetic resonance imaging (MRI), and BraTS-2019) and achieved up to 98.6% accuracy with high sensitivity and specificity, while retaining low computational cost and rapid inference, outperforming conventional CNN-only methods.
An efficient ANN-based global maximum power point searching approach for solar PV system under partial shading conditions Lakshminarayana Munirathnam; Yogeesh Amabalagere Chandrashekaraiah; Somu Pundalik Parande; Vani Bengaluru Purushothama; Ramya Mullur Varadhanayaka; Bellary Kursheed
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11597

Abstract

For solar photovoltaic (PV) systems operating in partial shading conditions (PSCs), this research suggests a simplified maximum power point tracking (MPPT) controller based on artificial neural networks (ANNs). By adding multiple local peaks (LPs) to the power-voltage (P-V) curve, PSCs make it impossible for traditional techniques to follow the global maximum power point (GMPP), which results in substantial energy loss and decreased system reliability. In order to overcome this, a DC–DC boost converter's (BC's) duty cycle is adaptively controlled by the suggested ANN-based controller for precise GMPP tracking. Three shading scenarios are used to validate the approach on a 3×2 PV array. According to simulation results, tracking efficiency is higher than 99.4% and overall system efficiency is higher than 99.3%. The ANN greatly reduces oscillations around the GMPP by achieving quick convergence (
Co-Authors Bellary Kursheed Karanam Sunil Kumar Ramya Mullur Varadhanayaka Rashmi Shivaswamy Reshma Shafi Somu Pundalik Parande Suraksha Prakash Vani Bengaluru Purushothama Yogeesh Amabalagere Chandrashekaraiah