Research & Publications

(To be online) This work uses ARIMA models to forecast crop yield in the Telangana region, providing essential data-driven insights for agricultural planning and resource management.

We apply Empirical Mode Decomposition to separate the intrinsic features and trends of German economic time series (GDP, compensation, employment rate) to identify business cycle fluctuations.

(To be online) Introduces an adaptive variant of the Ant Colony Optimization (ACO) algorithm to achieve robust and versatile clustering for dynamic image segmentation tasks.

This paper presents a Conditional Variational Autoencoder (CVAE) framework to generate realistic and physically-informed synthetic seismic ground motion data, improving earthquake hazard modeling.

We employ a Conditional Generative Adversarial Network (CGAN) to accurately model and predict the acceleration response spectra, critical for structural safety analysis in earthquake engineering.

Developed SLICE, a novel image processing methodology combining clustering and Earth Mover’s distance, for highly accurate and robust classification of brain tumours in medical imagery.

A detailed review exploring the synergy between photoacoustic imaging techniques and functional nanoparticles for enhanced biomedical diagnostics and therapeutic applications.

This review synthesizes the current state of nanotechnology in creating high-performance near-infrared photodetectors, focusing on material science and performance metrics.

Demonstrates a Machine Learning approach using Bootstrap Aggregation to optimize the design parameters of low-scale dipole antennas, reducing design time and complexity.

Presents a robust voting-based ensemble classification system designed to enhance the accuracy and reliability of automated malaria parasite detection from microscopic images.

A quality engineering study analyzing the Critical-to-Quality (CTQ) factors for Uninterruptable Power Supply (UPS) systems to improve reliability and performance standards.

Evaluates a novel network embedding scheme for integrating and analyzing multivariate data in complex healthcare applications, facilitating better diagnostic models.

Developed a sensor fusion system integrating acoustic and visual data for autonomous rover navigation, particularly effective in challenging and non-uniform environments.

Applies statistical and computational methods to analyze the form and growth characteristics of Eucalyptus regnans trees, relevant for forestry management and ecology.

Focuses on using Image Processing techniques combined with Clustering Algorithms to accurately quantify forest cover and monitor changes in remote sensing data.

Utilizes various Machine Learning models for the accurate and timely assessment of meteorological drought conditions across different regions in India.