Abstract
The main purpose of this chapter is to propose an AI Interactive system. In today’s Fast Working world, hard work and time management holds a key feature. While in that time management one doesn’t get enough time to explore himself, his/her emotions and enough time to interact with Friends and Family. This busy schedule leads one apart from socialization, and it’s the key to mental wellbeing.
The term loneliness is often misunderstood. It is not an objective condition, but rather a subjective one. Loneliness is different for everyone and there are many factors that contribute to it, including the feelings of isolation, anxiety, mental health and depression.
Machine learning has been used for many purposes and it can also be used to help people with loneliness, anxiety, and depression. Machine learning is helping people who are lonely and struggling with anxiety and depression. The technology is being used to create virtual assistants that provide support and guidance. By using machine learning, these assistants can learn about the user's specific needs and provide targeted assistance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Miller, R.E., LaValle, L.: Writing for Wikipedia: Applying Disciplinary Knowledge to the Biggest Encyclopedia. ACRL (2022)
Smith, D., Leonis, T., Anandavalli, S.: Belonging and loneliness in cyberspace: impacts of social media on adolescents’ well-being. Aust. J. Psychol. 73(1), 12–23 (2021)
Nikolova, G.: Determination of the stress intensity factor of polymer composites with recycled materials. Eng. Sci. LIX, 24–33 (2022)
Weber, R.: The lonely society-philip slater: the pursuit of loneliness: american culture at the breaking point (Boston: Beacon Press, 1970. Pp. xiii, 154. $7.50). The Rev. Politics 33(3), 425–427 (1971)
Ménard, M., Richard, P., Hamdi, H., Daucé, B., Yamaguchi, T.: Emotion Recognition based on Heart Rate and Skin Conductance. In: PhyCS, pp. 26–32. (2015)
Das, S., Das, I., Nath Shaw, R., Ghosh, A.: Advance machine learning and artificial intelligence applications in service robot. In: Nath Shaw, R., Ghosh, A., Balas, V.E., Bianchini, M. (eds.) Artificial Intelligence for Future Generation Robotics, pp. 83–91. Elsevier (2021). https://doi.org/10.1016/B978-0-323-85498-6.00002-2
Fischer, C.: Incentives can’t buy me knowledge: the missing effects of appreciation and aligned performance appraisals on knowledge sharing of public employees. Rev. Public Pers. Adm. 42(2), 368–389 (2022)
Samanci, H., Thulin, M.: Act like a human, think like a bot: a study on the capabilities required to implement a social bot on a social media platform (2022)
Guillemette, A., et al.: Impact and appreciation of two methods aiming at reducing hazardous drug environmental contamination: the centralization of the priming of IV tubing in the pharmacy and use of a closed-system transfer device. J. Oncol. Pharm. Pract. 20(6), 426–432 (2014)
Haas, E.J.: The role of supervisory support on workers’ health and safety performance. Health Commun. 35(3), 364–374 (2020)
Robert, A., Suelves, J.M., Armayones, M., Ashley, S.: Internet use and suicidal behaviors: internet as a threat or opportunity? Telemedicine e-Health 21(4), 306–311 (2015)
Drum, D.J., Denmark, A.B.: Campus suicide prevention: bridging paradigms and forging partnerships. Harvard Rev. Psychiatry 20(4), 209–221 (2012)
Henry, A., Wright, K., Moran, A.: Online activism and redress for institutional child abuse: function and rhetoric in survivor advocacy group tweets. Int. Groups Adv. 11(4), 493–516 (2022). https://doi.org/10.1057/s41309-022-00165-0
Gründer, G.: How we live together. In: How Do We Want to Live?, pp. 129–137. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-662-64225-2_10
Wallace, S.G.: Kids Out of the House-Lonely But (Almost) Never Alone (2019)
Mascarenhas, S., et al.: A virtual agent toolkit for serious games developers. In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–7. IEEE (2018)
Norman, K.P., et al.: Natural language processing tools for assessing progress and outcome of two veteran populations: cohort study from a novel online intervention for posttraumatic growth. JMIR Formative Res. 4(9), e17424 (2020)
Söderberg, E.: An evaluation of the usage of affective computing in healthcare. In: USCCS 2022, pp. 69–78 (2022)
Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., Sooriyabandara, M.: HealthyOffice: Mood recognition at work using smartphones and wearable sensors. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–6. IEEE (2016)
Khurana, Y., Jindal, S., Gunwant, H., Gupta, D.: Mental health prognosis using machine learning. SSRN 4060009 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aggarwal, I., Sahana, S., Das, S., Das, I. (2023). AI Based Interactive System-HOMIE. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-25088-0_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25087-3
Online ISBN: 978-3-031-25088-0
eBook Packages: Computer ScienceComputer Science (R0)