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103 results for “C. Subramani”
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Research ArticleOpen accessIn the author namesCOMPARISON OF OPTIMIZATION TECHNIQUE TO FIND THE OPTIMAL LOCATION OF FACTS CONTROLLERS FOR TRANSMISSION LINEJagadeesh Kumar Muthukrishnan, Subhransu Sekhar Dash, Harish Kiran Selvakumar et al.The aspect of this study is to compare the three different algorithms such as Genetic Algorithm (GA), Enhanced Genetic Algorithm (EGA) and Particle Swarm Optimization (PSO) to fine the optimal location of the FACTS device in the transmission line. The FACTS devices Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) are selected based on the steady stability … …American Journal of Applied Sciences, Vol. 11, Issue 2 · 26 Dec 2013 · 6,680 accessesView PDFCite
Research ArticleOpen accessIn the author namesWeb-Enabled Generalized Architectural Model for Online Power System AnalysisBoopathi C. Sengodan, Subhransu Sekhar Dash, Subramani Chinnamuthu et al.In this study, an effective Web-enabled generalized architectural model is proposed. The proposed model makes the power system analysis to be completely secured, distributed, platform-independent and language-independent. Although significant progress has been made for on-line power system analysis in distributed environment, still efficient distributed environment has not yet been exploited. The Web-enabled architectural models are emerging as basic methodologies to … …American Journal of Applied Sciences, Vol. 10, Issue 9 · 24 Aug 2013 · 6,721 accessesView PDFCite
Research ArticleOpen accessIn the author namesArtificial Neural Network Controller Based Distribution Static Compensator for Voltage Sag MitigationD. Rajasekaran, Subhransu Sekhar Dash, Arun Bhaskar Mayilvaganan et al.Switching of loads, capacitors, along with the proliferation of power electronics equipment, non-linear loads in industrial, commercial and domestic applications have lead to power quality issues in the distribution system. Power quality issues such as voltage sag, voltage swell and harmonics, which are certainly major concerning issues in the present era. These issues can lead to failure or malfunction of … …American Journal of Applied Sciences, Vol. 10, Issue 7 · 21 Jun 2013 · 6,958 accessesView PDFCite
Research ArticleOpen accessIn the author namesImplementation of Line Stability Index for Contingency Analysis and Screening in Power SystemsC. Subramani, Subhransu Sekhar Dash, Vivek Kumar, Harish KiranProblem statement: Estimating the margin in the loadability of the power system is essential in the real time voltage stability assessment. Voltage stability is currently one of the most important research areas in the field of electrical power system. In power system operation unpredictable events is termed as contingency and may be caused by line outage in the system which … …Journal of Computer Science, Vol. 8, Issue 4 · 11 Feb 2012 · 7,136 accessesView PDFCite
Research ArticleOpen accessIn the author namesProbabilistic Learning Models for Secure E-Commerce Transaction AnalysisAnanth Maria Pushpa, S. SubramaniaanThe tremendous expansion of e-commerce requires well-developed security mechanisms of transactions coupled with cryptography systems. This paper explores how probabilistic learning algorithms can be used to promote fraud and transaction validation in e-commerce systems that are cryptographically secured. A study compared different machine learning models to analyze retail data, including Naïve Bayes, Bayes Network, and Logistic learning. The results showed … …Journal of Computer Science, Vol. 22, Issue 11 · 20 Oct 2026 · 527 accessesView PDFCite
Research ArticleOpen accessIn the author namesExplainability Aware Heterogeneous Temporal Graph Transformer for Automated Genetic Disorder Detection from Genomic DataPuvaneswari Subramaniapillai, Indirani Govindasamy, Revathy GurumurthyGenetic disorders affect millions worldwide, posing major challenges for accurate diagnosis and effective treatments. New developments in genomic technologies, especially Next-Generation Sequencing (NGS), have enhanced the classification of genetic modification and improved diagnostic abilities. Moreover, the Machine Learning (ML) and Deep Learning (DL) methods emerged as efficient tools for evaluating the intricate genomic data and determining the patterns, which can … …Journal of Computer Science, Vol. 22, Issue 11 · 14 Oct 2026 · 581 accessesView PDFCite
Research ArticleOpen accessIn the author namesExplainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM VisualizationD. Vetrithangam, Pramod Kumar, Shilpasree S. et al.Kidney tumor classification from CT imaging remains challenging due to CT-specific intensity variations, tumor heterogeneity, and limited clinically meaningful interpretability in existing deep learning models. To address these limitations, we propose an Explainable MedAttn-ResNet50 framework that integrates radiology-driven preprocessing, multi-scale feature extraction, and medical attention mechanisms within a unified architecture. The preprocessing pipeline applies radiology-inspired intensity normalization, contrast enhancement, and … …Journal of Computer Science, Vol. 22, Issue 8 · 13 Aug 2026 · 1,127 accessesView PDFCite
Research ArticleOpen accessIn the author namesEffects of Vitamin E on Stress Resorption, Lipid Profile and Reproductive Performance of Sokoto Red GoatsKehinde Matthias OKUKPE, Ling Shing Wong, Geetha Subramaniam et al.Vitamin E (α-tocopherol) plays a critical role in animal nutrition as an antioxidant, contributing to anti-inflammatory responses, immune function, and gene expression regulation. This study aimed to determine the optimal dietary level of vitamin E to alleviate the effects of environmental stress in Sokoto Red Goats transported from their native dry savannah belt to the humid derived savanna belt of … …OnLine Journal of Biological Sciences, Vol. 26, Issue 1 · 20 Mar 2026 · 1,513 accessesView PDFCite
Research ArticleOpen accessIn the author namesExploratory Data Analysis of Applications of Encryption for Netflix by ML ModelsAnanth Maria Pushpa, Subramanian ShanmugamThis study examines the performance of various machine learning algorithms in analyzing Netflix's dataset, with a focus on encryption methods used for data protection. We evaluated several models including Ada BoostM1, IBk, Random Forest, and Decision Stump using multiple performance metrics. This work analysis revealed that the Ada BoostM1 model achieved the highest accuracy at 97.14%, outperforming other algorithms. It … …Journal of Computer Science, Vol. 21, Issue 9 · 24 Nov 2025 · 1,930 accessesView PDFCite