About Me

Chatbot AI

A dynamic chatbot solution designed using Groq, PyQT6, and other technologies, Chatbot Acroplan provides seamless interaction and support to users. The chatbot is capable of understanding and responding to queries efficiently, offering a smooth user experience.

Technologies Used: Groq, PyQT6, Python, and various automation tools.

Text Recognition with OCR

Developed an Optical Character Recognition (OCR) system aimed at improving 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.

Key Features: Advanced image preprocessing, enhanced text extraction accuracy, and integration with various document types.

Technologies Used: Python, Deep Learning Libraries

IPL Match Winner Prediction Using Machine Learning

Created a predictive model using random forest classifiers to analyze and predict match outcomes based on historical data and advanced algorithms.

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.

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.

Key Features include component-based design, API integration for seamless backend performance, and Agile development for efficient project management.

Technologies Used: Angular, MuleSoft 4, MySQL

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 creating the app using platform-native programming languages and cross-platform tools.

Key Features: Anonymous browsing features, mobile application development for Android, and integration with privacy protection protocols.

Technologies Used: Java, React Native

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.

Integration of an alert system for early intervention.

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.

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.

Personalized health recommendations based on AI insights.

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.

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, provide daily exercise routines, and track medication adherence. It also analyzes patient responses to adjust plans according to their recovery progress.

Analytics dashboard for doctors to monitor patient progress remotely.

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.