Meghana A C A C

70 · Very Good

About

Results-driven IT professional with a strong foundation in Java and web technologies, complemented by hands-on experience in full-stack development (MERN stack) and enterprise IT application support. Possesses a solid understanding of the software development lifecycle and a commitment to delivering scalable technical solutions. Completed IBM z/OS Mainframe course and NPTEL Java certification, demonstrating continuous learning and adaptability.

Skills & Expertise (29)

JavaScript Intermediate

6.5/10

Years Exp

Python Intermediate

6.5/10

Years Exp

React.js Intermediate

6.5/10

Years Exp

Adaptability Intermediate

6.0/10

Years Exp

Continuous Learning Intermediate

6.0/10

Years Exp

verbal and written communication Intermediate

6.0/10

Years Exp

Problem Solving Intermediate

6.0/10

Years Exp

Analytical Thinking Intermediate

6.0/10

Years Exp

VS Code Intermediate

6.0/10

Years Exp

GitHub Intermediate

6.0/10

Years Exp

Git Intermediate

6.0/10

Years Exp

RESTful APIs Intermediate

6.0/10

Years Exp

Express.js Intermediate

6.0/10

Years Exp

Node.js Intermediate

6.0/10

Years Exp

MERN Intermediate

6.0/10

Years Exp

Java Intermediate

5.2/10

Years Exp

S3 Intermediate

5.0/10

Years Exp

HTML Intermediate

5.0/10

Years Exp

MySql Intermediate

5.0/10

Years Exp

MongoDb Intermediate

5.0/10

Years Exp

Software development fundamentals Intermediate

5.0/10

Years Exp

EC2 Intermediate

5.0/10

Years Exp

AWS Fundamentals Intermediate

5.0/10

Years Exp

CSS3 Intermediate

5.0/10

Years Exp

Postman Intermediate

5.0/10

Years Exp

SDLC Intermediate

5.0/10

Years Exp

IAM Intermediate

4.5/10

Years Exp

Networking Fundamentals Intermediate

4.5/10

Years Exp

Oracle Intermediate

4.5/10

Years Exp

Work Experience

Internship

Deep Learning-Based Guava Fruit Disease Detection and Classification

Present - Present

Built an end-to-end deep learning-based image classification system using Convolutional Neural Networks and transfer learning models, including VGG16, ResNet50, MobileNetV2, and EfficientNetB0. Applied strong problem analysis and solution design to identify optimal model architectures for accurate disease classification. Optimized model training workflows through automated data preprocessing, model tuning, and validation processes. Evaluated system performance using confusion matrices, ROC curves, and comparative performance metrics to ensure accuracy, reliability, and scalability. Conducted comparative analysis of multiple models to support data-driven decision making and system optimization. Documented system architecture, model performance results, and testing outcomes to enable effective knowledge transfer and cross-team collaboration.