The optimized Neural Networking scheme in Time Series Analysis for Detecting Depression

This study offers the Optimized Neural Networking scheme in time series analysis for detecting depression. The proposed scheme uses Deep learning strategies, including Convolutional Neural Networks, to analyze feature representations from time collection facts. The temporal records were extracted via convolution to enable robust prediction of melancholy. A real-global dataset of electronic fitness records was hired to evaluate the proposed scheme's performance. Results show that the proposed scheme performed a drastically excessive accuracy in detecting depression compared to conventional strategies, including linear regression and guide Vector devices. The proposed scheme can extract thrilling functions from time collection data that could efficaciously expect despair. The findings show the potential of applying deep mastering techniques for detecting melancholy and offer assurance for using this scheme in practice.