Intelligent personality analysis on indicators in IoT-MMBD-enabled environment

Abstract

Psychologists seek to measure personality to analyze the human behavior through a number of methods. As the personality of an individual affects all aspects of a person’s performances, even how he reacts to situations in his social life, academics, job, or personal life. The purpose of this research article is to enlighten the use of personality detection test in an individual’s personal, academics, career, or social life, and also provide possible methods to perform personality detection test. One of the possible solutions is to detect the personality, and the study is based on the individual’s sense of humor. Throughout the twentieth century, psychologists show an outgoing interest in study of individual’s sense of humor. Since individual differences in humor and their relation to psychological well-being can be used to detect the particular personality traits. Machine learning has been used for personality detection involves the development and initial validation of questionnaire, which assesses four dimensions relating to individual differences in the uses of humor. Which are Self-enhancing (humor used to enhance self), Affiliative (humor used to enhance the relationship with other), Aggressive (humor used to enhance the self at the expense of others), and Self-defeating (the humor used to enhance relationships at the expense of self). Machine learning is gaining importance, nowadays, as it enables computers to perform self-learning without being programmed for a specific task. Psychologists seek to measure personality through different methods. Nowadays, the human being is so much complex that it is difficult to estimate the personality of an individual manually. The purpose of this chapter is to enlighten the use of personality detection test in academics, job placement, group interaction, and self-reflection. This book chapter provides the use of multimedia and IOT to detect the personality and to analyze the different human behaviors. It also includes the concept of big data for the storage and processing the data, which will be generated while analyzing the personality through IOT. We have used one of the supervised learnings called regression. Algorithms like Linear Regression, Multiple Linear Regression, Decision Tree, and Random Forest are used here for building the model for personality detection test. Among the different algorithms used in the project, Linear Regression and Multiple Linear Regression are proved to be the best so they can be used to implement the prediction of personality of individuals. Decision tree regression model has achieved minimum accuracy as in comparison to others so it is not the model, which can be used for training our model for determining the personality traits of individuals.