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All Journal International Journal of Applied Power Engineering (IJAPE) BALI MEDICAL JOURNAL (BMJ) Bulletin of Electrical Engineering and Informatics Computer Science and Information Technologies
Soumya Mishra, Soumya
Senior Resident, Dept of Physiology, JIPMER, Puducherry.
Computer Science & IT Electrical & Electronics Engineering Engineering Medicine & Pharmacology

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CONGENITAL ANOMALIES AMONG NEWBORNS Devassy, Unnimon K.; M., Danasegaran; Sailesh, Kumar Sai; Mishra, Soumya; Reddy, Udaya Kumar; J., Antony N.
BALI MEDICAL JOURNAL Vol 4 No 1 (2015)
Publisher : BALI MEDICAL JOURNAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (253.38 KB)

Abstract

Background: The present study was undertaken to assess the incidence of congenital anomalies among newborns. The total anomalies were 280 and the highest incidence has been identified in the year 2011, and the incidences were high in the gastrointestinal system. It also reveals that the incidence is higher in low birth weight newborns, and the major incidence has been identified in full term newborns. Results: results of the study can be used to predict future incidence of anomalies and to increase public awareness about congenital anomalies to take preventive measures.
Load frequency control of interconnected power system using cuckoo search algorithm Mishra, Soumya; Kumar, Pujari Harish; Ramasamy, Rajarajan; Edayillam Nambiar, Renjini; Puvvada, Praveena
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6714

Abstract

This paper presents a new time-domain multi-objective function approach for solving load frequency control issue in an interconnected power system. The performance of interconnected power system in each area is validated for overshoot and settling time values of frequency change and tie-line power exchange. An objective function is created with the goal of enhancing proportional integral derivative (PID) controller settings by reducing overshoot and achieving faster time-domain settling times. The efficiency of the proposed time-domain multi-objective function is evaluated in a two-area thermal power plant using a nature-inspired cuckoo search optimization (CSA) technique. By comparing the time-domain simulation results of the test system with the existing integral error-based objective functions IAE, ISE, ITAE, and ITSE, the proposed objective function is validated. Further, a sensitivity analysis were carried out to analyze the robustness of the proposed multi-objective function under various uncertain conditions.
Advances in dermatological imaging: enhancing skin melanoma classification for improved patient outcomes Sahoo, Debadutta; Mishra, Soumya
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p111-120

Abstract

The study presents an enhanced AlexNet-based deep learning system for binary classification of melanoma skin cancer as either benign or malignant using two paired dermatoscopic and clinical image datasets. The study evaluates the resilience of the models across different image sets with common preprocessing and specific data augmentation, using a melanoma dataset containing 10,000 images and a benign versus malignant dataset with 3,600 images. The AlexNet refinement exceeded several standard machine learning (ML) classifiers and other deep architectures on the two datasets with practical training times, gaining 97.12% and 96.21% in balanced accuracy. The training proceeded with SGD as optimiser and cross-entropy as loss on 256×256 images. Benchmarking against support vector machine (SVM), k-nearest neighbour (KNN), and other convolutional neural networks (CNNs) designs shows that the selected architecture and hyperparameters achieved the highest performance on cost-effective computation for the routine melanoma triage. The report highlights the need for external validation, incorporation into dermatological workflows, and explainability to improve trust, diminish dataset bias, and support the safe clinical deployment in practice.
High impedance fault discrimination in microgrid power system using stacking ensemble approach Vinayagam, Arangarajan; Mohandas, Raman; Chindamani, Meyyappan; Sujatha, Bhadravathi Gavirangapa; Mishra, Soumya; Sundaramurthy, Arivoli
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp98-109

Abstract

High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
Co-Authors Antony N. J., Antony N. Chindamani, Meyyappan Danasegaran M., Danasegaran Edayillam Nambiar, Renjini Kumar Sai Sailesh, Kumar Sai Kumar, Pujari Harish Mohandas, Raman Puvvada, Praveena Ramasamy, Rajarajan Sahoo, Debadutta Sujatha, Bhadravathi Gavirangapa Sundaramurthy, Arivoli Udaya Kumar Reddy, Udaya Kumar Unnimon K. Devassy, Unnimon K. Vinayagam, Arangarajan