Emotion Analysis Using Iterative Supervised Classification Algorithm for Crime Detection - M.Jayakandan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research paper discusses using Land Weber iterative supervised classification and Quantized Spiking Network for emotion analysis in crime detection. The proposed methodology is evaluated using a real-world data set. The proposed approach is promising in terms of accuracy and robustness. This work aims to develop a supervised classification and quantized spiking network for emotion analysis. We propose a method to extract features from the temporal dynamics of a spiking neural network (SNN) and use these features to train a support vector machine (SVM) classifier. We also quantize the SNN output to improve the classification accuracy. Our results show that the proposed method can achieve good classification performance on a publicly available dataset.