Emotional intelligence - creating a new roadmap for artificial intelligence

被引：1

作者：

Dey S. ^{[1
]}

Chakraborty C. ^{[2
]}

机构：

[1] Department of Computational Sciences, Brainware University, Kolkata, Barasat

[2] Department of Electronics and Communication Engineering, Birla Institute of Technology, Jharkhand, Mesra

来源：

International Journal of Engineering Systems Modelling and Simulation | 2021年 / 12卷 / 04期

关键词：

Affective computing; Backpropagation; Chatbot; Deep learning; Emotional artificial intelligence; Feature extraction; Neural network; Psychological sensor; Social robot;

D O I：

10.1504/IJESMS.2021.119871

中图分类号：

学科分类号：

摘要：

Artificial intelligence (AI) changing the world with the power of creating intelligent solutions capable of autonomous decision-making and self-diagnostic abilities. Recent research on AI tends towards the use of emotional intelligence in artificial systems. A clear understanding of human emotion or cognitive behaviour can help an artificial system to become more rational and unbiased while making any decision. If a machine can think or feel like a human, it can be converted into a better decision-making system. This paper has performed a thorough review of state-of-the-art technologies and researches going in this particular field and tries to find out the current roadmap as well as the future trends in this area. This paper presents several types of research that are going on in this area and future trends. Copyright © 2021 Inderscience Enterprises Ltd.

引用

页码：291 / 300

页数：9

共 42 条

[1]

Albornoz E.M., Milone D.H., Rufiner H.L., Spoken emotion recognition using hierarchical classifiers, Computer Speech and Language, 25, 3, pp. 556-570, (2018)

[2]

Amit B., Chinmay C., Anand K., Biswas D., Emerging trends in IoT and big data analytics for biomedical and health care technologies, Handbook of Data Science Approaches for Biomedical Engineering, 5, pp. 121-152, (2019)

[3]

Basu S., Chakraborty J., Bag A., Aftabuddin M., A review on emotion recognition using speech, International Conference on Inventive Communication and Computational Technologies, (2018)

[4]

Bhavan A., Et al., Conference on Communication and Computational Technologies, (2020)

[5]

Canamero D., Emotions and adaptation in autonomous agents: a design perspective, Cybernetics and Systems: International Journal, 32, pp. 507-529, (2001)

[6]

Chaudhari M., Govilkar S., Emotion extraction using rule based and SVM-KNN algorithm, International Journal of Computer Applications, 125, 11, pp. 32-34, (2015)

[7]

Chinmay C., Bharat G., Soumya K.G., Tele-wound technology network for assessment of chronic wounds, Int. Journal of Telemedicine and Clinical Practices, 1, 4, pp. 345-359, (2016)

[8]

Crowder A., Friess S., Artificial psychology: the psychology of AI, systematics, cybernetics and informatics, 11, 8, pp. 64-68, (2013)

[9]

Davletcharova A., Et al., Detection and analysis of emotion from speech signals, Procedia Computer Science, 58, 6, pp. 91-96, (2015)

[10]

Dhiman C., Viswakarma D.K., A review of state-of-the-art techniques for abnormal human activity recognition, Engineering Applications of Artificial Intelligence, 77, pp. 21-45, (2019)

← 1 2 3 4 5 →