Utilizing Fuzzy Algorithm for Understanding Emotional Intelligence on Individual Feedback

Main Article Content

Elham Abdulwahab Anaam
Su-Cheng Haw
Kok-Why Ng
Palanichamy Naveen
Rasha Thabit

Abstract

Although previous studies looked at how employees should seek assistance, the issue is the researchinvestigation into how behavioral intelligence affects employee satisfaction is limited. This study examines several significant usages and developments of fuzzy mental modelling. The primary objective of the current section is to provide an innovative technique for modelling an emotion-based acceleration of the compressor for individuals. Methodologies of experiential thinking postulate that our comprehension of facial emotional reactions depends significantly on facial behavior imitation and the reactions as opportunities. Considering the theoretical foundations of combined logical reasoning. In addition, the hypothesis of probability, it additionally is not effective to build a comprehensive hypothesis concerning impressions. Combining emotional intelligence with fuzzy logic as a combination, we were able to tackle issues with current techniques that neither artificial intelligence nor fuzzy mathematics alone could.

Article Details

How to Cite
Anaam, E. A., Haw, S.-C., Ng, K.-W., Naveen, P., & Thabit, R. (2023). Utilizing Fuzzy Algorithm for Understanding Emotional Intelligence on Individual Feedback. Journal of Informatics and Web Engineering, 2(2), 273–283. https://doi.org/10.33093/jiwe.2023.2.2.19
Section
Regular issue

References

B. Dudzik, M. Jansen, F. Burger, and F. Kaptein, "Context in Human Emotion Perception for Automatic Affect Detection: A Survey of Audiovisual Databases," 2019 8th Int. Conf. Affect. Comput. Intell. Interact., no. 639, pp. 206–212, 2019.

S. C. Haw, L. J. Chew, K. Ong, K. W. Ng, P. Naveen, and E. A. Anaam, “Content-based Recommender System with Descriptive Analytics,” J. Syst. Manag. Sci., vol. 12, no. 5, pp. 105–120, 2022, doi: 10.33168/JSMS.2022.0507.

A. Hinduja and M. Pandey, “An Intuitionistic Fuzzy AHP based Multi Criteria Recommender System for Life Insurance Products,” Int. J. Adv. Stud. Comput. Sci. Eng., vol. 7, no. 1, pp. 1–8, 2018.

X. Ma, E. Y. Yang, and P. Fung, “Exploring Perceived Emotional Intelligence of Personality-Driven Virtual Agents in Handling User Challenges,” pp. 1222–1233.

B. Choi, Humanoid Robots. InTech, 2009. doi: 10.5772/108

M. Kuehne, I. Siwy, T. Zaehle, H. Heinze, and J. Lobmaier, "Out of Focus: Facial Feedback Manipulation Modulates Automatic Processing of Unattended Emotional Faces," no. 1974, pp. 1–10, 2015, doi: 10.1162/jocn.

A. Z. Zakuan, S. Abdul-Rahman, and H. Jantan, “Towards academic successor selection modelling with Genetic Algorithm in multi-criteria problems,” Int. J. Eng. Technol., vol. 7, no. 4, pp. 130–133, 2018, doi: 10.14419/ijet.v7i4.33.23516.

I. Ta, A. Ayan, I. Eskin, and G. Kahrama, “The effect of the level of self-monitoring on work engagement and emotional exhaustion: A Research on Small and Medium Size Enterprises ( SMEs ),” vol. 150, pp. 1080–1089, 2014, doi: 10.1016/j.sbspro.2014.09.122.

Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation systems: Algorithms, challenges, metrics, and business opportunities,” Appl. Sci., vol. 10, no. 21, pp. 1–20, 2020, doi: 10.3390/app10217748.

D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00592-5.

P. Sethi and S. R. Sarangi, “Internet of Things: Architectures , Protocols , and Applications,” vol. 2017, 2017.

M. B. Mollah et al., “Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey,” vol. 8, no. 6, pp. 4157–4185, 2021.

O. N. Jensen, P. Mortensen, O. Vorm, and M. Mann, “Automation of Matrix-Assisted Laser Desorption / Ionization Mass Spectrometry Using Fuzzy Logic Feedback Control,” vol. 69, no. 9, pp. 1706–1714, 1997.

M. Bagheri, A. Akbari, and S. A. Mirbagheri, “SC,” Process Saf. Environ. Prot., 2019, doi: 10.1016/j.psep.2019.01.013.

J. Singh and A. Sharan, “A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach,” Neural Comput. Appl., 2016, doi: 10.1007/s00521-016-2207-x.

G. Albertus, M. Meiring, and H. C. Myburgh, “A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms,” pp. 30653–30682, 2015, doi: 10.3390/s151229822.

F. Ali et al., “Jou rna,” Futur. Gener. Comput. Syst., 2020, doi: 10.1016/j.future.2020.07.047.

J. Mart and A. Aldea, “Emotions in human and artificial intelligence,” vol. 21, pp. 323–341, 2005, doi: 10.1016/j.chb.2004.02.010.

M. G. Bitmi and A. Ergeneli, “Emotional Intelligence: Reassessing the construct validity,” vol. 150, pp. 1090–1094, 2014, doi: 10.1016/j.sbspro.2014.09.123.

I. Pastor, “Leadership and emotional intelligence: the effect on performance and attitude,” Procedia Econ. Financ., vol. 15, no. 14, pp. 985–992, 2014, doi: 10.1016/S2212-5671(14)00658-3.

F. C. J. Cabibihan et al., Social Robotics. 2022. doi: 10.1007/978-3-031-24667-8.

A. Zenebe and A. F. Norcio, “Representation , similarity measures and aggregation methods using fuzzy sets for content-based recommender systems,” vol. 160, pp. 76–94, 2009, doi: 10.1016/j.fss.2008.03.017.

Y. Lim, K.W. Ng, P. Naveen, S.C. Haw, “Emotion Recognition by Facial Expression and Voice: Review and Analysis”, Journal of Informatics and Web Engineering, Vol. 1 No. 2, pp. 45 – 54, 2022.