Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4
Augmented Reality in Education
Volume-2 | Issue-4
A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2
AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 3 | Issue - 2 | june 2021
Published
31 May, 2021
The use of social media and leaving a digital footprint has recently increased all around the world. It is being used as a platform for people to communicate their sentiments, emotions, and expectations with their data. The data available in social media are publicly viewable and accessible. Any social media network user's personality is predicted based on their posts and status in order to deliver a better accuracy. In this perspective, the proposed research article proposes novel machine learning methods for predicting the personality of humans based on their social media digital footprints. The proposed model may be reviewed for any job applicant during the times of COVID'19 through online enrolment for any organisation. Previously, the personality prediction methods are failed due to the differing perspectives of recruiters on job applicants. Also, this estimation is modernized and the prediction time is also reduced due to the implementation of the proposed hybrid approach on machine learning prediction. The artificial intelligence based calculation is used for predicting the personality of job applicants or any person. The proposed algorithm is organized with dynamic multi-context information and it also contains the account information of multiple platforms such as Facebook, Twitter, and YouTube. The collection of the various dataset from different social media sites constitute to the increase in the prediction rate of any machine learning algorithm. Therefore, the accuracy of personality prediction is higher than any other existing methods. Despite the fact that a person's logic varies from season to season, the proposed algorithm consistently outperforms other existing and traditional approaches in predicting a person's mentality.
KeywordsDeep learning Personality prediction
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