AIDE_Learning 🤘🏻

AI-Powered E-Learning Platform with Sign Language Support

📌 Project Overview

AIDE_Learning is an AI-based E-learning platform designed to provide secure access to educational content and support inclusive learning through text, voice, and sign language interaction. The platform integrates Natural Language Processing (NLP), Machine Learning, and Sign Tracking Technology to enhance accessibility for users, including people with hearing or speech impairments.

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🎯 Objectives

• Provide a secure user registration and login system for personalized learning.

• Develop a versatile AI chatbot that understands and responds via:

  • Text
  • Voice
  • Sign Language

• Implement data preprocessing and machine learning algorithms for accurate query classification and responses.

• Enable real-time sign language recognition using trained CNN models.

• Improve accessibility and engagement through multi-format learning content.

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🔐 System Features

1. User Authentication

• Secure user registration using:

  • Username
  • Password
  • Email
  • Phone number

• Ensures authorized access only.

• Personalized user experience after login.

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2. E-Learning Content

• Users can browse educational videos on various subjects.

• Videos are converted into:

  • 📄 Text format
  • 🔊 Audio format

• Users can choose their preferred learning mode.

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3. AI Chatbot (NLP-Based)

• Chatbot interacts with users and answers queries based on trained data.

• Supports:

  • Text input
  • Voice input
  • Sign language input

• Enhances accessibility for users with different needs.

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4. Sign Language Tracking 🤟

• Uses AI-powered Sign Tracking Technology.
• Tracks hand signs in real-time live streams.
• Converts signs into meaningful queries and provides answers instantly.
• Designed especially for people with hearing and speech impairments.

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🧠 Machine Learning & Data Processing

Data Preprocessing

• Handling missing values
• Encoding categorical data
• Text cleaning and NLP techniques (tokenization, stop-word removal, etc.)

Model Training

• Machine learning models trained on pre-processed datasets.

• Classification algorithm used:

  • Random Forest Classifier

• CNN-based model used for sign recognition:

  • cnn_housing.h5

⚠️ Note:

The dataset and trained models (cnn_housing.h5) are not included in this repository due to size and privacy constraints.

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📊 Performance Evaluation

The system performance is evaluated using Accuracy:

[ Accuracy = \frac{TP + TN}{TP + TN + FP + FN} ]

Where:

• TP – True Positive
• TN – True Negative
• FP – False Positive
• FN – False Negative

Accuracy measures how correctly the classifier predicts class labels for new data.

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🛠️ Technologies Used

• Python
• Machine Learning (Random Forest)
• Deep Learning (CNN)
• NLP (Natural Language Processing)
• Sign Language Recognition
• Speech-to-Text & Text-to-Speech
• Flask / Django (if applicable)
• OpenCV (for sign tracking)
• TensorFlow / Keras

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🚀 Future Enhancements

• Include multilingual support.
• Improve sign recognition accuracy.
• Add more learning resources.
• Deploy on cloud for scalability.
• Mobile application support.

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👥 Target Users

• Students
• Educators
• Hearing or speech-impaired learners
• Inclusive education platforms

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📁 Repository Note

Dataset and model training files are not uploaded to the repository. Please contact the author for access or use your own datasets for training.

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If you want, I can also:

• Shorten this for college submission
• Add screenshots section
• Create a project abstract
• Write a final year project report format

Just tell me 👍

Conference: IEEE (International Conference) Status: Published