N V S L SRAVYA EEDE

Technology driven Computer Science graduate pursuing MSc in Digital Forensics & Information Security.

Developed practical solutions through software development, AI&ML and cybersecurity, leveraging a strong foundation in programming and analytical problem solving.

Committed to contributing innovative perspectives in software engineering and cybersecurity while delivering positive customer experiences.

• IBM certification "ML0101EN: Machine Learning with Python: A Practical Introduction" on July 11, 2024.
• NPTEL certification "Software Testing" on November 21, 2023.
• Salesforce certification on November 28, 2023.
• Certification on MERN Stack by Smart Bridge.
• Certified in Database Management System on August 20, 2024, Infosys Springboard.
• Malware Forensics – GUVI | HCL.
• 100 Days of Python Expert – GUVI | HCL.
• Machine Learning Techniques – GUVI | HCL.
• JavaScript – GUVI | HCL.
• Machine Learning for Forensics and Security – GUVI | HCL.
• Information Security – GUVI | HCL.
• Getting Started with Cisco Packet Tracer – Cisco Networking Academy.

• Integrated static and dynamic malware analysis to detect suspicious activities, calculate risk scores, and generate forensic reports.
• Technologies: Python, Streamlit, Sysinternals Procmon, JSON.

• Developed an IoT security system for detecting unauthorized rack movement and tampering with real-time alerts.
• Technologies: ESP8266, SW-420, Blynk IoT, Embedded C.

• Built a Telugu text classification model using TF-IDF and Logistic Regression to identify positive and negative sentiments.
• Technologies: Python, NLP, TF-IDF, Scikit-learn.

• Developed a Decision Tree-based classification model for tumor characterization using data preprocessing and exploratory analysis.
• Technologies: Python, Machine Learning, Decision Tree.

• Developed a full-stack bus booking website featuring seat selection, gender-based adjacent seat allocation, and online payments.
• Technologies: MongoDB, Express.js, Angular.js, Node.js.

• Analyzed call records, timestamps, frequency, and contact patterns to identify suspicious communication activities.
• Technologies: Digital Forensics, CDR Analysis, Excel, Data Analysis.

• Major Project, Title: CNN Transfer Learning Techniques to Detect Plant Diseases, Supervisor Name: Dr. T. Nagamani, Professor, Department CSE., Description: This project focuses on leveraging Convolutional Neural Networks (CNNs) and transfer learning techniques to accurately detect and classify plant diseases from images. By utilizing pre-trained deep learning models such as VGG16, ResNetV2, Inception V3, Dense Net-201, Xception, the project aims to fine-tune these architectures on a dataset of plant images exhibiting various diseases. The goal is to create a robust and efficient system that can assist farmers and agricultural experts in early disease detection, enabling timely intervention and reducing crop losses. The project involves data preprocessing, model training, evaluation, and deployment, with an emphasis on achieving high accuracy and generalizability across diverse plant species and environmental conditions., This work investigates the application of sophisticated deep learning models for automated image classification-based plant disease identification. VGG 16 had an accuracy of 95.1%, Dense Net-201 had an accuracy of 94.4%, MobileNet had an accuracy of 97.5%, Inception ResNetV2 had an accuracy of 97%, Inception V3 had an accuracy of 97.2%., Xception performed the mall with an astounding accuracy of 99%. This astounding precision demonstrates how deep learning might revolutionize plant disease diagnosis
• CRAAS : Developing a centralized cybersecurity monitoring and threat assessment system that integrates network traffic analysis, system log monitoring, and SIEM-based event correlation to detect, classify, and prioritize security threats. Leverages AlienVault OSSIM and MITRE ATT&CK to support proactive threat detection, incident analysis, and risk assessment.

Hobbies : Drawing, Cooking, Dancing

• I hereby declare that to the best of my knowledge the information above furnished is correct and I take the complete responsibility if any information is found to be incorrect.