VNRVJIET IT Department Showcases Dual Innovations at IITEX 2025

Hyderabad, June 27, 2025 – The Department of Information Technology at VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET) unveiled two groundbreaking technological innovations at the Industrial Innovation Technology Expo (IITEX) 2025, held from June 27–29 at HITEX Exhibition Center, Hyderabad. The prestigious event is organized by the Federation of Telangana Chambers of Commerce and Industry (FTCCI).

1. EWS-DDoSNet: A Smart Cybersecurity Early Warning System

Developed by undergraduate students Ashritha, Krishna Sai, Siddharth L, and Ph.D. scholar Raghupathi under the guidance of Dr. V. Radhakrishna and Dr. A. Sreenivasa Rao, EWS-DDoSNet is an advanced cybersecurity solution designed for early detection and prevention of Distributed Denial of Service (DDoS) attacks.

Unlike traditional reactive systems, EWS-DDoSNet employs mathematical modeling—including differential equations—and statistical metrics like variance, skewness, and kurtosis to proactively identify suspicious network behavior. Upon detection, the system activates real-time defensive measures, ensuring minimal service disruption.

One of its key advantages is its resource efficiency, making it suitable for use on legacy systems commonly found in small businesses, public sectors, and critical infrastructure.

“Our goal was to create a solution that’s both powerful and practical for real-world deployment,” said the student team.

2. ArogyaVignanX: AI-Powered Chest Disease Diagnosis for Rural Clinics

Also launched at IITEX 2025 is ArogyaVignanX, an AI-driven diagnostic tool developed by students Tanuja, Lalitha Bhavani, Vishal, and Shashank, mentored by Dr. V. Radhakrishna.

ArogyaVignanX analyzes chest X-rays to detect a wide range of respiratory conditions, including COVID-19, pneumonia, tuberculosis, COPD, asthma, lung cancer, pleural effusion, and interstitial lung disease. The system boasts up to 96.7% accuracy for common conditions, utilizing advanced signal processing methods such as Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) along with mathematical feature extraction like eigenvalues.

Optimized for low-power devices, the tool uses lightweight AI models—quantized Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM)—making it ideal for rural clinics and mobile health units.

“Access to timely and accurate diagnosis should be universal. With ArogyaVignanX, we hope to empower underserved communities with life-saving technology,” the team noted.

Both innovations underscore VNRVJIET’s commitment to applied research and societal impact, aligning with the national goals of digital transformation and inclusive healthcare.