Paper Title
Detection of Human Behavior Using AI-Enhanced Convolutional Neural Network (CNN) and Support Vector Machines (SVM)

Abstract
The detection of human conducts the use of AI-greater Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) represents an enormous development in the area of synthetic intelligence and computer imaginative and prescient. This method leverages the strengths of both CNNs and SVMs to as it should be pick out and classify human movements, emotions, and interactions in various environments. CNNs are applied for their high-quality capacity to extract spatial functions from visual facts, together with pics or video frames, enabling the version to seize tricky patterns and information in human conduct. SVM, however, is employed as a strong classifier that correctly separates and categorizes the extracted capabilities into awesome behavioral instructions. The integration of those two strategies complements the overall overall performance of the gadget, as CNNs manage the complicated feature extraction technique, while SVMs offer unique classification. This hybrid version is particularly powerful in actual-time applications, along with surveillance, healthcare, and human-laptop interaction, in which accurate and timely detection of human behavior is important. By schooling the CNN-SVM version on massive datasets of annotated behavioral records, the system learns to apprehend a wide range of actions, from easy gestures to complex activities, with high accuracy and reliability. Keywords - Human Behavior Detection, AI, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Machine Learning, Pattern Recognition, Computer Vision, Deep Learning.