ViSense: An Integrated Multi-Modal AI Framework for Intelligent Video Understanding with Sequential Cognitive Visualization.

This paper presents a sophisticated AI-based and deep learning framework for comprehensive video file analysis, comprising nine separate intelligent agencies integrated with an innovative sequential rendering concept. The proposed system utilizes deep learning model architectures, including convolutional neural networks (CNNs) for visual processing, recurrent neural networks for temporal modeling, and transformer-based models for integrating different media. The key innovation lies in the application of a dual screen rendering strategy that separates detailed textual analysis from graphical visualizations, thereby enhancing user comprehension and depth of analysis. Experimental results demonstrate superior performance in video comprehension tasks, with an average accuracy improvement of 23.7% compared to the latest available methods. Experimental results on benchmark datasets demonstrate superior performance in video comprehension tasks, with an average accuracy improvement of up to 23.7% compared to state-of-the-art methods.