Enhancing Road Safety: Real-Time Detection of Driver Distraction through Convolutional Neural Networks

As we navigate our daily commutes, the threat posed by a distracted driver is at a large, resulting in a troubling rise in traffic accidents. Addressing this safety concern, our project harnesses the analytical power of Convolutional Neural Networks (CNNs), with a particular emphasis on the well-established models VGG16 and VGG19. These models are acclaimed for their precision in image recognition and are meticulously tested for their ability to detect nuances in driver behavior under varying environmental conditions. Through a comparative analysis against an array of CNN architectures, this study seeks to identify the most efficient model for real-time detection of driver distractions. The ultimate aim is to incorporate the findings into vehicle safety systems, significantly boosting their capability to prevent accidents triggered by inattention. This research not only enhances our understanding of automotive safety technologies but also marks a pivotal step towards creating vehicles that are intuitively aligned with driver behaviors, ensuring safer roads for all.

Graph Neural Network and NER-Based Text Summarization

With the abundance of data and information in todays time, it is nearly impossible for man, or, even machine, to go through all of the data line by line. What one usually does is to try to skim through the lines and retain the absolutely important information, that in a more formal term is called summarization. Text summarization is an important task that aims to compress lengthy documents or articles into shorter, coherent representations while preserving the core information and meaning. This project introduces an innovative approach to text summarization, leveraging the capabilities of Graph Neural Networks (GNNs) and Named Entity Recognition (NER) systems. GNNs, with their exceptional ability to capture and process the relational data inherent in textual information, are adept at understanding the complex structures within large documents. Meanwhile, NER systems contribute by identifying and emphasizing key entities, ensuring that the summarization process maintains a focus on the most critical aspects of the text. By integrating these two technologies, our method aims to enhances the efficiency of summarization and also tries to ensures a high degree relevance in the condensed content. This project, therefore, offers a promising direction for handling the ever increasing volume of textual data in an information-saturated world.