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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE)
Balaramakrishna, K. V.
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Design and implementation of solar-grid based charging station for electric vehicle with fault detection method using R-Pi and IoT processor Vaigundamoorthi, M.; Karthick, S.; Chandrika, V. S.; Chithra, D.; Balaramakrishna, K. V.; Khandan, K. Lakshmi; Maguluri, Lakshmana Phaneendra; Chandrasekar, S.; Janarthanan, M.
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i4.pp794-802

Abstract

In this research describes the electrical vehicle (EV) charging station using PV panel with fault detection methods. The PV modules will failure for some time, because of some external factors and internal factors. In direct fault condition the monitor and analyze the external factors such as the life span, high intensity and breakage of the PV panels using Raspberry Pi (R-Pi) processor with internet of things (IoT) system. In power demand/day on the PV panel will be evaluated and analyzed through R-Pi processor and IoT. The efficiency and the range values of the PV panels will be monitored and analyzed through IoT. Proposed work explains, how the fault detection techniques have been improved and adopted in using R-Pi processor through IoT platform. The proposed dataset pre-processing system is incorporated with IoT module. The grid fault clearing time will be compared with the actual values through R-Pi processor. The PV panel faults are detected using thermal image processing, that image parameter values analysis through IoT based internal monitoring system.
Accurate stroke area classification using extreme gradient boosting with multi-feature extraction Praveen Kumar Rao, Kavikondala; Bondla, Maha Lakshmi; Srinivasa Rao, Bommaraju; Naveena, Ambidi; Balaramakrishna, K. V.; Goda, Srinivasarao
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.pp1390-1401

Abstract

Stroke, one of the most common neurological disorders leading to long-term disability and mortality, requires accurate detection of affected brain regions for timely treatment planning. However, conventional deep learning models face challenges in achieving precise segmentation and robust classification due to noisy inputs, weak feature representation, and poor generalization. To address these gaps, this study introduces a hybrid framework that integrates the ConvNeXt architecture for stroke region segmentation with XGBoost based classification, strengthened through three complementary feature extraction methods: local binary patterns (LBP), adaptive threshold directional binary gradient matrix (AT-DBGM), and wavelet packet transform (WPT). These methods capture textural, directional, and multi resolution features, which are concatenated into a stacked vector and classified using XGBoost. Preprocessing steps, including normalization and resizing, ensure improved input consistency. Experimental evaluations on benchmark stroke imaging datasets show that the proposed framework achieves 98.56% Dice similarity coefficient (DSC), 12.96 mm Hausdorff distance (HD), 99.12% accuracy, 98.69% sensitivity, 99.06% specificity, 98.98% precision, and 98.85% F1-score.
Co-Authors Bondla, Maha Lakshmi Chandrasekar, S. Chandrika, V. S. Chithra, D. Goda, Srinivasarao Janarthanan, M. Karthick, S. Khandan, K. Lakshmi Maguluri, Lakshmana Phaneendra Naveena, Ambidi Praveen Kumar Rao, Kavikondala Srinivasa Rao, Bommaraju Vaigundamoorthi, M.