COMPUTATIONAL MODEL FOR MENTAL FACTOR CLASSIFICATION
R.J. Ramteke, S.S. Gharde
ICTACT Journal on Soft Computing July 1, 2022 DOI: 10.21917/ijsc.2022.0383 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Computational modeling study Peer reviewed |
|---|---|
| Sample size | 445 |
| Population | Respondents to a three-question survey |
| Measures | Accuracy, Precision, Specificity, Recall, F1-Score |
| Key points | Proposes that artificial wisdom requires adding values to positive decisions and that mental factors underlying thoughts can be classified computationally. A convolutional neural network classified mental factors from survey responses with 86.92% accuracy on a small database and 93.02% on a large database. |
Abstract
Artificial Wisdom is advancement of Artificial Intelligence where wisdom should be recognized with the intelligence. Wisdom can be realized by adding values in the positive decisions. This is resulted in to the overall behavior of human being. Behavior can be demonstrated with the help of simulating thought process. Thoughts are generated in the mind along with the mental state. Mental state also known as mental factor is responsible for arousal of different types of thoughts. Ancient Indian cannon Abhidhamm claimed 52 mental factors. These are categorized in three classes such as Ethically Variable Factor, Unwholesome Factor and Beautiful Factor. Proposed computational model of consciousness demonstrates the classification of the mental states. Dataset consists of 445 samples collected from various respondents by asking three questions. Preprocessing is performed by using the techniques of Natural language processing and nonaxiomatic logic. Convolutional Neural Network Machine learning technique applied to classify the mental factors. Performance of the proposed system is measured by applying statistical measures such as Accuracy, Precision, Specificity, Recall and F1-Score. Accuracy for small and large database is obtained as 86.92% and 93.02% respectively.