AUTHOR=Kshirsagar Pravin R. , Manoharan Hariprasath , Selvarajan Shitharth , Alterazi Hassan A. , Singh Dilbag , Lee Heung-No 

TITLE=Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.893989

DOI=10.3389/fpubh.2022.893989

ISSN=2296-2565

ABSTRACT=In this generation, the majority of individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognise such occurrences, and no procedures for discriminating them have been created. Even some advanced technology do not support distinct classes of individuals since writing language skills vary greatly across numerous places, making central operations cumbersome. As a result, a primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotional analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analysed to test the usefulness of CNN for recognising abnormalities, and the extracted features provide an enhanced value of around 74 percent greater than conventional models.