Support Vector Machines Models for Human Decision-Making Understanding: A Different Perspective On Emotion Detection

Abstract: The high integration of artificial intelligence (AI) into our daily life has led to get much research on the technology's potential, particularly with its ability to understanding human emotions during complex decision-making processes. Our study mainly focuses on the potential of Support Vector Machine (SVM) models in facial emotion recognition (FER), an important aspect of human-computer interaction (HCI) and examines this potential through the Viable System Approach (VSA) perspectives.The importance of HCI is highlighted in this study, which recognizes the significant effects of AI and the Internet of Things (IoT) on several aspects of human beings. The understanding of emotions as an important difference between human and machine is an ongoing issue, underlining the necessity for AI to incorporate emotional intelligence. The main objective of the project is to fill the knowledge gap existing between AI and human surroundings, ethics, and social factors. To achieve this, it focuses on two main objectives:1.Theoretical Value: Exploring the relationship between human emotions and the processes involved in making decisions in different social circumstances through the lens of VSA.2.Practical/Experimental Value: Developing and testing various SVM models for the automatic recognition and classification of human emotions, with the aim of understanding which parameters most affect the classification accuracy.Such a multidisciplinary methodology allows to bring together different ideas from computer science, machine learning, marketing, psychology, sociology, and business economics, providing a comprehensive understanding of AI's role in complex systems, especially in emotional perception and decision-making.From an experimental point of view, we realized three different SVM models based on the most widely used kernel functions (linear, polynomial, and radial). Then, we used the "Japanese Female Facial Expression (JAFFE)" dataset to test the models on three different configurations of the initial data, to understand which parameters are most influential for the performance of the classifiers and to investigate the limitations and potential of SVMs for emotion recognition. The paper's originality lies in its multidisciplinary nature, integrating computer science with VSA, providing a fresh perspective on FER. This approach is not just about developing a framework for HCI but delves deeper into understanding the social dynamics underlying decision-making. In addition, our study exhibits a good experimental novelty, offering new insights into the impact of different parameters on SVM performances. In conclusion, our paper emphasizes the significance of emotional aspects in HCI and the potential of AI in understanding human emotions. By employing the VSA, it extends the discussion on AI’s capabilities in complex decision-making processes, highlighting the necessity for AI systems to resonate cognitively with human users in increasingly digital environments.

Keywords: Support Vector Machines, Viable Systems Approach, Emotion Detection, Human Computer Interaction

DOI: 10.54941/ahfe1005089

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