Speech emotion recognition: Develop a system that uses AI to recognize emotions from images or live video feeds. You can use machine learning models like CNNs to classify facial expressions.

The research project aims to assess and contrast the predictive accuracy of two distinct machine learning algorithms: the Decision Tree Classifier (DTC) and a Convolutional Neural Network (CNN) integrated with Innovative Geometric Transformation. This investigation focuses on evaluating the capability of these algorithms to predict the risk of falling among elderly individuals who are grappling with various health concerns. This research study uses a dataset with different poses adjusted as classes and trains two machine learning algorithms, a Decision Tree Classifier and a Convolutional Neural Network, with a new geometric transformation. A sample size calculation is performed with a G- power pretest of 80%, a threshold of 0.05%, and a CI of 95%. Each algorithm has a sample size of 20. The Convolutional Neural Network with Novel Geometric Transformation obtained 82.60% of accuracy whereas the Decision Tree Classifier which has got 68.85% of accuracy. The statistical significance value between CNN and DTC based on the sample t-test is p=0.000 (p<0.05) two-tailed, this shows that there is significance between two groups. Based on the experiments and results, the Convolutional Neural Network with Novel Geometric Transformation approach obtained better accuracy for predicting fall risk in older adults than the Decision Tree Classifier model. [ABSTRACT FROM AUTHOR]