Projects

1. Chatbot AI

A dynamic and interactive chatbot designed using advanced technologies like Groq and PyQT6 aimed to provide seamless user support and interaction. This innovative project focused on creating a responsive and efficient system that enhances user experience by addressing real-time queries, ensuring that users receive immediate assistance tailored to their needs. By leveraging machine learning algorithms and natural language processing capabilities, the chatbot continuously improves its responses and better understands context, allowing it to engage in meaningful conversations. Moreover, the integration of various communication channels not only broadens accessibility but also enables organizations to foster a more personalized connection with their audience. This comprehensive approach aims to increase user satisfaction and engagement, paving the way for more progressive interactions in the digital landscape.

• Technologies Used: Groq, PyQT6, Python
• Key Features: Interactive chat functionality, real-time query resolution, and smooth UI/UX integration.

2. Zeez AI

An AI-powered quizzing platform that generates customized quizzes based on user demographics and content types, including text, images, and audio. Quizifyr offers real-time chat support, personalized quizzes, and advanced media processing features.

• Technologies Used: JavaScript, OpenAI GPT-3 Turbo, AWS Amplify, Google Vision API, AWS Rekognition
• Key Features:
  • AI-driven quiz generation fine-tuned with GPT-3 Turbo for accurate MCQs.
  • Text-to-speech integration using AWS Amplify for accessibility.
  • Image-to-text processing powered by Google Vision API for visual content quizzes.
  • Real-time Chat System with enhanced text recognition using AWS Rekognition.
  • Secure authentication and intuitive UI/UX design built with AWS Amplify.

3. Text Recognition with OCR

Developed an Optical Character Recognition (OCR) system to improve text extraction accuracy for digital conversion of manual data. The project utilized deep learning techniques to overcome noise and distortions in low-quality images, ensuring precise text recognition.

• Technologies Used: Python, Deep Learning Libraries
• Key Features: Advanced image preprocessing, enhanced text extraction accuracy, and integration with various document types.

4. IPL Match Winner Prediction Using Machine Learning

This project involved developing a machine learning model using Random Forest Classifiers to predict IPL match outcomes based on historical data. The system used data visualization tools and algorithms to achieve insightful predictions.

• Technologies Used: Python, Pandas, NumPy, Matplotlib, Seaborn
• Key Features: Data preprocessing, ball-to-ball analysis, predictive modeling, and visual representation of data insights.

5. Template Generator

An admin tool designed to allow institutions to personalize their app’s interface to align with their branding and visual identity. The Template Generator uses MuleSoft 4 for API development and the Angular framework for the front end, enabling quick construction and maintenance of UI components.

• Technologies Used: Angular, MuleSoft 4, MySQL
• Key Features: Component-based design, API integration for seamless backend performance, and Agile development for efficient project management.

6. Regulation of Android’s Network Privacy Footprint

Developed a mobile application to provide anonymous browsing on Android devices, focusing on minimizing the network privacy footprint. This project involved developing the app using platform-native programming languages and cross-platform tools.

• Technologies Used: Java, React Native
• Key Features: Anonymous browsing features, mobile application development for Android, and integration with privacy protection protocols.

7. Heart Disease Prediction System Using AI

Developed an AI-powered prediction system that uses patient data to determine the likelihood of heart disease. The project involved building a predictive model using Machine Learning algorithms like Logistic Regression and Random Forest Classifiers. The system analyzed various health metrics such as blood pressure, cholesterol levels, and lifestyle habits to provide an accurate diagnosis.

• Technologies Used: Python, Scikit-learn, Pandas, NumPy
• Key Features:
  • Data collection and preprocessing from medical records.
  • Real-time risk assessment using AI algorithms.
  • Visualization of patient health metrics using Matplotlib.
  • Integration of an alert system for early intervention.

8. AI-Based Heart Health Monitoring App

An application designed to continuously monitor heart health using wearable devices. The app collects ECG data, heart rate variability, and other vital signs, which are then analyzed using deep learning models to detect irregularities like arrhythmias or abnormal heart rhythms. The app provides real-time alerts and personalized health recommendations.

• Technologies Used: TensorFlow, Keras, JavaScript (for frontend development), Python (for backend AI models)
• Key Features:
  • Integration with wearable devices (e.g., smartwatches) for data collection.
  • Deep learning models for real-time ECG analysis.
  • Secure cloud storage for patient data using AWS.
  • Personalized health recommendations based on AI insights.

9. AI-Powered Cardiac Rehabilitation Assistant

An AI-driven platform to support cardiac rehabilitation patients in following exercise and medication plans post-surgery. The platform uses Natural Language Processing (NLP) to interact with patients, providing daily exercise routines and tracking medication adherence. It also analyzes patient responses to adjust plans according to their recovery progress.

• Technologies Used: OpenAI’s GPT-3, AWS Lambda, Flask (for backend development), JavaScript (for frontend)
• Key Features:
  • Personalized rehabilitation plans tailored to individual patient needs.
  • NLP-based chatbot for patient interaction and progress tracking.
  • Integration with hospital databases for patient record updates.
  • Analytics dashboard for doctors to monitor patient progress remotely.

10. AI Diagnostic Tool for Heart Disease Detection Using Imaging

Built an AI-based diagnostic tool that analyzes medical imaging data (like echocardiograms and MRI scans) to detect heart disease. The system employs Convolutional Neural Networks (CNNs) to identify abnormalities in heart structure and function, providing a detailed report for doctors to review. The tool improves diagnostic accuracy and reduces the time needed for image analysis.

• Technologies Used: TensorFlow, Keras, OpenCV (for image processing), Python
• Key Features:
  • Image preprocessing and analysis using CNNs for high accuracy.
  • Automated detection of common heart conditions such as cardiomyopathy and valve defects.
  • Integration with cloud platforms for scalable image storage and processing.
  • AI-generated reports with visual annotations and diagnostic suggestions for cardiologists.