NANCY G

Bachelor of Engineering student specializing in Computer Science and Engineering with strong skills in programming, web development and artificial intelligence. Experienced through internships and academic projects, including web development with JavaScript and AI model development. Proven ability to work collaboratively in teams, deliver innovative solutions, and adapt quickly to new technologies. Highly motivated to contribute technical expertise and creativity to dynamic technology-driven environments.

SAP-Basis

• Certified Udemy SAP-Basis

UI/UX

• Certified GreatLearning UI/UX for Beginners

Java

• Certified Simplilearn Java (Basic)
• Certified HackerRank Java (Basic)
• Certified SkillRack Java

C

• Certified SkillRack C

SQL

• Certified SkillRack SQL

• Quick Learner
• Self-motivated Leadership

Handwritten Digit and Character Recognition
Duration: January 2, 2024 – January 5, 2024

• Developed a machine learning-based application to accurately recognize and classify handwritten digits and characters.
• Implemented Convolutional Neural Networks (CNN) for robust image recognition and classification.
• Performed data preprocessing and model training using extensive datasets for improved accuracy.
• Delivered a user-friendly interface for seamless and automated handwriting recognition.

Kanban Board Web Application
Duration: January 6, 2024 – January 8, 2024

• Designed and developed a web-based Kanban board to visually manage team tasks and workflow.
• Integrated features such as task creation, drag-and-drop functionality, and progress tracking.
• Enhanced team productivity by organizing tasks into columns representing different stages of completion.
• Focused on intuitive UI/UX for improved user engagement and efficiency.

Respiratory Disease Classification Using Lung Sounds with CNN-LSTM Model
Duration: January 27, 2025 – March 27, 2025

• Developed a deep learning-based system to classify respiratory diseases by analyzing lung sound recordings.
• Utilized a hybrid CNN-LSTM model to effectively extract both spatial and temporal features from audio spectrograms.
• Preprocessed lung sound datasets through denoising, segmentation, and feature extraction techniques.
• Achieved accurate classification of respiratory conditions, aiding in early and non-invasive diagnosis.
• Delivered a functional prototype capable of real-time disease detection based on lung auscultation data.