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

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¹Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur, India.

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Summary

This summary is machine-generated.

This study introduces a novel Convolutional Neural Network (CNN) model to detect diseases early through writing and sketching analysis. This machine learning approach aims to identify health issues, including depression, improving diagnostic accuracy and averting mental health decline.

Keywords:

convolutional neural network (CNN)depression characteristics emotion recognition machine learning (ML)perception

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Area of Science:

Artificial Intelligence
Machine Learning
Medical Diagnostics

Background:

Many individuals globally face health issues, with depression often stemming from intellectual difficulties.
Current diagnostic methods struggle to identify early signs of disease and differentiate individuals, partly due to variations in language and writing skills.

Purpose of the Study:

To develop a unique model capable of detecting various human diseases to prevent widespread depression.
To enhance early disease detection by analyzing input in the form of writing and sketching.

Main Methods:

Utilized a Convolutional Neural Network (CNN) model, a machine learning technique, for feature extraction.
Processed writing and sketching inputs as images for emotion analysis and abnormality recognition.
Analyzed specific characteristics like reference line, tilt, length, edge, constraint, alignment, separation, and sectors.

Main Results:

The CNN model demonstrated effectiveness in recognizing abnormalities through image emotion analysis.
Extracted features using CNN showed an enhanced value approximately 74% higher than conventional models.
The approach allows for easier differentiation of ordinary reactions, leading to more accurate predictions.

Conclusions:

The proposed CNN model offers a promising tool for early disease detection, including mental health conditions like depression.
This method improves diagnostic accuracy by analyzing visual patterns in writing and sketching.
The findings suggest a significant advancement over traditional diagnostic models in identifying subtle abnormalities.