Sketch Recognition using Artificial Intelligence

This study developed a Random Forest Classifier to identify ragams in Carnatic music by analyzing key audio features such as Chroma STFT, spectral properties, and MFCC coefficients. Trained on a dataset of 1,112 audio clips across 32 ragams, the model achieved a high classification accuracy of 99.1%, outperforming other baseline models.

As the digital world continues to grow, content creators frequently have trouble building a community and producing videos that will interest their audience. Especially as these people look toward the internet for both recreational and monetary reasons, finding out techniques to build a community is important in today’s age. This paper analyzes the issues of video performance, revealing the patterns of what makes a video successful and viral. By training different models and testing different datasets, we were able to find the correlation between the potential chances of popularity and the video’s content. Using the most accurate model, the Random Forest model, content creators can see whether or not they are likely to do well based on patterns found in trending videos.

We made a CNN that learned to recognize and classify sketches from Google’s Quick Draw dataset and implemented a novel brightness feature to test if it increased the accuracy.