Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Journal of Applied Data Sciences
S., Prakash
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
Recognizing Fake Documents by Instance-Based ML Algorithm Tuning with Neighborhood Size S., Prakash; B., Kalaiselvi; K., Sivachandar; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.654

Abstract

The primary objective of this research paper is to classify spam SMS messages for scamming threats as soon as they are received on a device. The study focuses on evaluating the performance of K-Nearest Neighbors (KNN) classifiers with different neighborhood sizes to determine the most effective machine learning technique for improving accuracy and predictions in SMS spam detection. SMS is a short text messages service that permits mobile phone users to exchange messages.  In today’s world, people are so much tending towards mobile phones and it has become easy to spread spam content through them. One can easily access any person’s details through these social networking websites. No information which is shared and stored in the device is not secure. Numerous anti-spam systems have been developed. In this paper, we compare the classification results against spam SMS data to estimate the effectiveness of the KNN classifiers at different k levels and the comparisons shown. An effective method of classifying spam SMS, based on the metrics like F-measures, Precision, and recall score is recognized from the experiment results. The best performance was achieved with K = 4, where the classifier provided a high accuracy of 94.78% and strong results across all key performance metrics. The research highlights that feature selection plays a crucial role in improving classification efficiency by eliminating irrelevant or redundant features. Although KNN is a simple and effective approach, its scalability and real-time processing limitations suggest that future work should explore deep learning, ensemble models, or heuristic-based optimization for further improvements and support process innovation.
Power Quality Assessment in Grid-Connected Solar PV Systems Using Deep Learning Techniques S., Dhivya; S., Prakash; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.655

Abstract

To address challenges in stability, power quality, and computational demands while supporting sustainable energy goals in grid-connected solar PV systems, this research introduces a novel deep learning approach: Adaptive Graph-Aware Reinforced Autoencoder with Attention-Based Neural Architecture Search (AGRAAN). AGRAAN simplifies and accelerates the development of neural networks by automatically identifying optimal architectures through Neural Architecture Search (NAS), enabling efficient learning from limited data using Few-Shot Learning, and enhancing performance through attention mechanisms for time-series forecasting. This integrated approach reduces manual tuning and adapts effectively to various tasks. High levels of solar PV integration in power grids introduce variability due to weather conditions and limited forecasting, often resulting in high operational costs. To address this, the AGRAAN model enhances real-time solar variability prediction, improving adaptability, cost-efficiency, and grid stability. NAS supports architectural optimization, Few-Shot Learning improves adaptability with minimal data, and attention mechanisms enhance forecasting accuracy. Additionally, high PV penetration causes voltage fluctuations and harmonic distortions in diverse grid environments. To mitigate these effects, a complementary system named Graph-Aware Reinforced Autoencoder Control System (GRAACS) is proposed. GRAACS detects and manages power quality issues using Autoencoders for anomaly detection, Graph Convolutional Networks (GCNs) for spatial prediction, and Reinforcement Learning for adaptive real-time control. The combined AGRAAN and GRAACS models significantly enhance performance, achieving a high efficiency score of 0.98, an F1-Score of 0.97, and a low Mean Absolute Error (MAE) of 0.11. These results demonstrate the effectiveness of the proposed AI-driven framework in optimizing solar PV grid integration for energy efficiency.
Modelling and Investigation of Solar Photovoltaic-Based Converter Configurations with Data Science Approach S., Prakash; S., Lakshmi; S., Priya; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.715

Abstract

Renewable energy sources, such as solar photovoltaic (PV) systems, typically produce low-voltage outputs, necessitating the use of high-gain direct current (DC) converters for efficient energy conversion. This study proposes a high-gain DC-DC converter for PV applications, designed with two MOSFET switches, two inductors, and two capacitors, offering a compact and efficient configuration. The converter achieves a high voltage gain of 6.8 and maintains a conversion efficiency of 97.7%, making it suitable for high-power applications. A data science-driven approach was employed to analyze the converter’s performance, integrating conventional simulation with machine learning techniques. Simulation results, conducted using MATLAB, confirmed the converter's superior performance, achieving an input ripple of 0.05% and an output ripple of 0.01%. Machine learning models, including Linear Regression, Decision Tree, Ridge Regression, and Support Vector Machine (SVM), provided deeper insights into the converter's behavior. Linear Regression accurately predicted output voltage, Ridge Regression minimized overfitting, and the Decision Tree model identified Duty Ratio and Input Voltage as the most critical factors affecting efficiency. SVM effectively classified operating conditions into high, moderate, and low efficiency. The Zero-Voltage Switching (ZVS) technique minimized switching losses, enhancing overall efficiency. This study demonstrates that integrating data science techniques with conventional analysis enhances the understanding and optimization of high-gain converters. The proposed converter provides a scalable and efficient solution for PV applications, offering insights for further optimization as part of process innovation.
Co-Authors B., Kalaiselvi Batumalay, Malathy K., Sivachandar S., Dhivya S., Lakshmi S., Priya