Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

Abstract

In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods.

Guiding Role of the Five Pre-crisis Predictions for Crisis Response Decision-making in Educational Online Public Opinion Crises: an Approach that Incorporates Integrated Learning

“…However, due to the openness and interactivity of the Internet, as well as its fast information transmission speed and difficulty in copying and tampering, it is unable to directly set agendas for the public and guide public opinion. At the same time, the anonymization of the internet also allows netizens to express their opinions freely, but this can also lead to unclear identity of the main body of public opinion crisis events, difficulty in unifying opinions, and even conflicting situations [9][10].…”