Investigating current neural network-based methodologies for indoor positioning.

Introducing a neural network model for indoor positioning utilizing primarily camera imagery and outlining the procedure for video data acquisition.

Evaluating the accuracy of the proposed method within a selected facility, examining its performance under image modifications (e.g., blurring) and in the context of the positioning task (classification versus regression).

Exploring the potential integration of the proposed model with other neural networks or its incorporation into an existing indoor positioning system.

Retrieve images related to a particular theme, extract dominant colours and create coherent colour palettes. Using GAN neural networks, new images are generated by mixing elements from the collected images. The key feature is the exploration of emotional impact and mood through complex colour and sentiment analysis. The project aims to provide a deeper understanding of the emotional nuances conveyed by visual content. Overall, it combines web scraping, machine learning and emotional analysis to explore the deep emotional tapestry within images.