In a world where mental health is becoming increasingly crucial, NeuroAI offers a technology-driven solution to help users understand and manage their emotions. By combining natural language processing, machine learning, and real-time sentiment analysis, this platform provides personalized recommendations to enhance emotional well-being.
- 🎭 Mood Analysis: Predict users' emotional states from text inputs using advanced sentiment analysis.
- 🎵 Personalized Suggestions: Recommend exercises, music, or movies tailored to the user's mood.
- 📔 Daily Journaling: Log emotional states with a diary feature and track changes over time.
- 📊 Mood Trends: Visualize mood trends using dynamic charts.
- 🤖 AI Chatbot: Integrated with the Gemini API for conversational support.
Understanding emotions can be challenging. NeuroAI bridges the gap by offering:
- Accessibility: Available anytime, anywhere, as a first step toward mental wellness.
- Real-Time Support: Instant feedback and suggestions for mood improvement.
- Advanced Technology: Built on cutting-edge machine learning models and APIs to ensure robust performance.
Languages: Python, JavaScript, HTML, CSS
Frameworks: Flask, TensorFlow, Streamlit
APIs: Google Gemini, Gradio
Libraries: NumPy, Pandas, NLTK, Chart.js, Matplotlib
Tools: VS Code, Jupyter Notebook, Git/GitHub
- 🧹 Cleaning: Removing unnecessary elements like punctuation, URLs, and stopwords.
- ✂️ Tokenization: Converting text into structured sequences.
- 📂 Splitting: Dividing datasets into training and testing subsets.
- 🏗️ Model Architecture: Bidirectional LSTM with Conv1D layers for sentiment analysis.
- 🛡️ Regularization: Implementing dropout layers to prevent overfitting.
- 📊 Visualization: Mood trends displayed using Chart.js.
| Title | Author(s) | Year | Key Insights | Limitations |
|---|---|---|---|---|
| Mindset: An Android-Based Mental Wellbeing Support Mobile Application | Malaika Samuel, C.P. Shirley | 2023 | Android app with mood tracking, journaling, and mindfulness tools. | Limited long-term engagement; data security concerns. |
| Mobile Application for Mental Health Using Machine Learning | Mendis E.S., et al. | 2022 | ML-based detection of stress and anxiety. | Lacks personalization; privacy issues. |
| Mental Health Mobile Apps to Empower Psychotherapy | Federico Diano, et al. | 2022 | Focus on blending apps with psychotherapy. | Simplifies complex issues; lacks therapist interaction. |
- 🌐 Accessibility: Language barriers or locked features limit access.
- 🎭 Lack of Personalization: Static recommendations fail to adapt to mood changes.
- 🔒 Data Privacy: Challenges in safeguarding sensitive user data.
- 🌍 Add multilingual support for wider accessibility.
- 🤝 Improve emotion detection for complex and mixed feelings.
- ⏱️ Implement real-time adjustments to recommendations.
- Python 3.12.1
- Node.js for Chart.js integration
- TensorFlow, NumPy, Pandas, NLTK libraries
- Clone this repository:
git clone https://github.com/triumph10/NeuroAI.git
This project is licensed under the MIT License