💡[Feature]: Use of AI to generate designs and images for upcoming trends Part 2

[Atharv714]

Is there an existing issue for this?

• [X] I have searched the existing issues

Feature Description

Build an AI Algorithm that generate Designs and Images for upcoming trend.

Use Case

To use AI to generate Designs and images for upcoming trend, personlized for the user

Benefits

No response

Add ScreenShots

No response

Priority

High

Record

• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [ ] I want to work on this issue

[Ar7109]

@Atharv714 @TAHIR0110 I want to work on this issue, please assign this to me under GSSOC 2024

[TAHIR0110]

@Ar7109 how will you make the model?? could you kindly list down the approach you would follow to this project?

[Ar7109]

@TAHIR0110

Approach to Building the Sentiment Analysis Model for Mental Health Detection

Define Objective: Create a model that identifies and classifies mental health-related posts by their sentiment (e.g., positive, negative, neutral).

Data Collection: Collect data from social media using APIs. Focus on posts tagged with mental health-related keywords (e.g., #anxiety, #mentalhealth).

Data Preprocessing: Clean text data by removing noise (e.g., special characters), tokenizing, and applying normalization techniques such as lemmatization or stemming.

Data Annotation: Manually label a subset of data or use crowdsourcing to create a training set with annotations for mental health concerns and associated sentiments.

Feature Extraction: Transform text into numerical representations using methods like TF-IDF, Word2Vec, or advanced embeddings like those from BERT.

Model Selection: Begin with simpler models such as SVM for a baseline, and then explore more sophisticated deep learning models like BERT for capturing nuanced context.

Model Training: Split the data into training, validation, and test sets. Optimize hyperparameters and use cross-validation to ensure model robustness.

Model Evaluation: Assess the model using metrics such as F1-Score and confusion matrices to identify strengths and areas for improvement.

Deployment: Develop APIs to serve the model for real-time sentiment analysis. Ensure the system can scale to handle large volumes of data efficiently.

Monitoring and Maintenance: Regularly monitor the model’s performance, retrain with new data to maintain accuracy, and ensure ethical usage and compliance with data protection regulations.

Tools and Technologies

• Languages: Python
• Libraries: TensorFlow, PyTorch, scikit-learn
• Deployment: Flask for API development, Docker for containerization

[TAHIR0110]

@Ar7109 Hello! It seems there's been a misunderstanding regarding the task. The intended objective is to develop an AI model that generates fashion designs based on upcoming trends, not to focus on mental health sentiment analysis.

Future enhancement of this issue is to analyse fashion preferences among individuals experiencing depression to identify potential patterns or trends. By leveraging augmented reality (AR) or virtual reality (VR) technologies, we aim to create personalised clothing suggestions that align with their unique sense of style, ultimately promoting well-being and self-expression. If this aligns with your vision, we can proceed to explore these innovative possibilities further, but in this issue this is not intended just neglect the mental health part for this issue it would be better.

if you are still interested I could assign you this issue #180

[Ar7109]

@TAHIR0110 Sorry for the misunderstanding , I will do the task, assign me the issue under GSSOC 2024

[TAHIR0110]

@Ar7109 assigned!