🌍 GPT-Based Translation System
🚀 Transforming Multilingual Communication with AI & AWS
📌 Project Overview
This project is a custom GPT-based translation system that enables users to upload documents or input text, select target languages, and receive translations in SDLXLIFF format—all within a sleek, conversational interface. The system seamlessly integrates OpenAI’s GPT-4 with AWS infrastructure, ensuring scalability, security, and efficiency in handling multilingual translations.
🎯 Key Features
✅ Conversational UI: Users interact with an AI-powered chat to upload files, input text, and get real-time status updates.
✅ Document & Text Translation: Supports .docx, .pdf, .txt, and .xlsx files (up to 50MB). Users can also paste up to 10,000 words of text.
✅ Smart Language Detection: The system automatically detects the source language while users specify the target language(s).
✅ GPT-4 Powered Translation: Ensures context-aware translations while preserving formatting and structure.
✅ SDLXLIFF Generation: Outputs translations in a structured format optimized for professional post-editing.
✅ AWS-Powered Backend:
- AWS S3 for secure file storage and lifecycle management.
- AWS Lambda for preprocessing, translation, and SDLXLIFF generation.
- CloudFront & Custom Domain for public access with HTTPS security.
🛠 Tech Stack
- Frontend: React (Hosted on AWS S3 + CloudFront)
- Backend: AWS Lambda (Python) + OpenAI GPT-4
- Storage: AWS S3 with automatic file cleanup (24h lifecycle policy)
- Security: HTTPS, S3 Encryption, and Access Policies
- Monitoring: AWS CloudWatch for logs & performance tracking
📈 Results & Impact
✔️ Fully functional, publicly accessible translation system.
✔️ End-to-end AWS integration for scalability & cost efficiency.
✔️ Highly optimized real-time feedback system for seamless user experience.
✔️ Supports enterprise-grade translations with structured outputs for localization workflows.
📂 Source Code: (Available upon request)
💡 Why This Project Matters?
This project showcases my expertise in Machine Learning, Cloud Computing (AWS), and AI-driven NLP. It bridges the gap between AI and real-world multilingual applications, demonstrating how LLMs can streamline translation workflows while ensuring enterprise compatibility.
If you’re interested in AI-powered translation, cloud-based ML deployments, or scalable NLP solutions, let’s connect! 🚀
🚀 Project: SA-CNN – Smart Signature Verification System
🖋️ Partially Adaptive Optimization Driven Spatially Attentive Convolutional Neural Network for Handwritten Signature Verification
🔍 Project Overview
🔹 Objective: Develop a fast and accurate signature verification system to combat forgery in banking, official documents, and secure transactions.
🔹 Industry/Domain: Smart Security Systems 🛡️ | Biometric Authentication 🔑
🛠️ Key Technologies Used
💻 Programming Language: Python 🐍
📚 Libraries/Frameworks: TensorFlow 🔗 | Keras 🤖
🧠 Techniques: CNN 🏗️ | Spatial Attention Mechanism 👀 | Custom Adam Optimizer ⚙️
⚡ Challenges Faced & Solutions
❌ Challenge:
⚠️ Traditional signature verification models were too complex, reducing processing speed.
✅ Solution:
🚀 Designed SA-CNN, a model that balances high accuracy with fast verification, making real-world applications seamless!
🎯 Project Details
📌 Problem Statement:
🔍 Ensuring accurate verification of handwritten signatures to prevent unauthorized transactions.
💡 Solution:
🛠️ Introduced SA-CNN, integrating:
✔️ Spatial Attention Mechanism 👁️ for better feature extraction
✔️ Adaptive Optimization 🔄 for efficient training & learning
🏆 Results:
✅ Achieved 99.95% accuracy 📊 on benchmark datasets (CEDAR)
✅ Outperformed standard models with fewer iterations ⚡
📈 Performance Metrics:
🔹 Accuracy: 99.95% ✅
🔹 Equal Error Rate (EER) 📉
🔹 Mean-Squared Error (MSE) 📊
🔹 AUC-ROC Curve 📈
🔥 Skills Demonstrated
🧠 Technical Skills:
💡 AI | 🤖 Machine Learning | ✍️ Signature Verification | 🏗️ CNN | 📊 Data Processing
🎯 Soft Skills:
🛠️ Problem-solving | 🔬 Research | 💡 Innovation
👀 Project: CF-CNN – AI for Ophthalmic Diagnosis
🧠 Channel Focused Convolutional Neural Network for Interpretable Eye Disease Detection
🔍 Project Overview
🔹 Objective: Automate eye disease diagnosis using Optical Coherence Tomography (OCT) images with high accuracy & interpretability.
🔹 Industry/Domain: 🏥 Medical Imaging | 👁️ Ophthalmology
🛠️ Key Technologies Used
💻 Programming Language: Python 🐍
📚 Libraries/Frameworks: TensorFlow 🔗 | Keras 🤖
🧠 Techniques: CNN 🏗️ | Dynamic Convolution 🔄 | Spatial Attention 👀 | Explainable AI (XAI) 💡
⚡ Challenges Faced & Solutions
❌ Challenge:
⚠️ Existing models were either too complex or not interpretable.
✅ Solution:
🚀 Developed CF-CNN, a model that balances high accuracy with explainability, making AI-driven diagnoses more transparent!
🎯 Project Details
📌 Problem Statement:
🔍 Accurately classify OCT images into:
✅ Normal | 🩸 Choroidal Neovascularization (CNV) | 🍬 Diabetic Macular Edema (DME) | ⚪ Drusen
💡 Solution:
🛠️ Introduced CF-CNN, integrating:
✔️ Dynamic Convolution 🌀 for adaptive learning
✔️ Spatial Attention 👁️ to focus on critical regions
✔️ Explainable AI (XAI) 💡 to highlight key features for diagnosis
🏆 Results:
✅ Achieved 96.68% accuracy 📊 with clear AI decision explanations!
📈 Performance Metrics:
🔹 Accuracy: 96.68% ✅
🔹 Dice Coefficient 🎯
🔹 Mean Absolute Error (MAE) 📉
🔥 Skills Demonstrated
🧠 Technical Skills:
🏥 Medical Image Analysis | 🤖 AI | 🏗️ CNN | 📊 Explainable AI (XAI)
🎯 Soft Skills:
💡 Innovation | 🔍 Analytical Thinking | 🗣️ Communication
👀 Project: DA-CNN – AI-Powered Retinal Disease Classification
🩺 Explainable AI-driven Dual Attentive CNN for Retinal Disease Diagnosis
🔍 Project Overview
🔹 Objective: AI-based retinal disease classification in OCT images with high accuracy and interpretability.
🔹 Industry/Domain: 👁️ Ophthalmology | 🏥 Medical Imaging
🛠️ Key Technologies Used
💻 Programming Language: Python 🐍
📚 Libraries/Frameworks: TensorFlow 🔗 | Keras 🤖
🧠 Techniques: CNN 🏗️ | Dual Attention Mechanism 🔍 | Explainable AI (XAI) 💡
⚡ Challenges Faced & Solutions
❌ Challenge:
⚠️ Ensuring a balance between accuracy, speed, and interpretability in medical diagnostics.
✅ Solution:
🚀 Developed DA-CNN, a model with dual attention and separable dynamic convolution, ensuring clear and reliable AI-driven insights for doctors.
🎯 Project Details
📌 Problem Statement:
🔍 Classify retinal diseases into:
✅ Normal | 🩸 Diabetic Macular Edema (DME) | ⚪ Drusen | 🩹 CNV
💡 Solution:
🛠️ Introduced DA-CNN, integrating:
✔️ Dual-Attention Mechanism 👀 for improved feature extraction
✔️ Separable Dynamic Convolution 🔄 for efficient learning
✔️ Explainable AI (XAI) 💡 to provide transparent decision-making
🏆 Results:
✅ Achieved an outstanding accuracy of 97.4% 📊, outperforming other models!
📈 Performance Metrics:
🔹 Accuracy: 97.4% ✅
🔹 MAE (Mean Absolute Error) 📉
🔹 Dice Coefficient 🎯
🔹 AUC-ROC Curve 📈
🔹 Sensitivity & Specificity 🏆
🔥 Skills Demonstrated
🧠 Technical Skills:
🔬 Medical Imaging | 🏗️ Machine Learning | 👀 Retinal Disease Classification | 📊 Explainable AI (XAI)
🎯 Soft Skills:
🛠️ Problem-solving | 🔬 Research | 🗣️ Presentation Skills
Project 4: Satellite Image Classification using Explainable AI-based CNN for Remote Sensing
Project Overview:
- Objective: Automate the classification of satellite images for remote sensing applications like urban planning and environmental monitoring.
- Industry/Domain: Remote Sensing, Environmental Science, Urban Planning
Key Technologies Used:
- Programming Languages: Python
- Libraries/Frameworks: TensorFlow, Keras
- Techniques: CNN, Dynamic Convolution, Dual Attention Mechanism, Explainable AI (XAI)
Challenges Faced:
- Existing models lacked simplicity, scalability, and explainability for satellite image classification.
- Needed a system that was fast, accurate, and interpretable.
Project Details:
- Problem Statement: Classify satellite images into categories like green area, desert, clouds, and water for environmental analysis.
- Solution: Developed a CNN model using separable dynamic convolution and channel-split dual attention for enhanced feature extraction, and utilized XAI techniques to explain classification decisions.
- Results: The model achieved a high accuracy of 97.69%, outperforming existing models in classification speed and interpretability.
- Metrics: Accuracy: 97.69%, Precision, Recall, F1-Score
Skills Demonstrated:
- Technical Skills: Satellite Image Classification, Remote Sensing, AI, CNN, XAI
- Soft Skills: Technical Research, Scalability, Communication
