Efficiency of Smart AI-Based Voice Apps and Virtual Services Operating With Chatbots

  • Nidal Al Said College of Mass Communication, Ajman University, Ajman, United Arab Emirates
  • Dmitry Gura Kuban State Technological University, Kuban State Agrarian University,
  • Dmitry Karlov AMTI (branch) of the KubSTU
Keywords: Computer and Information Technologies, Artificial Intelligence, Voice Assistant, Chatbot, Machine Learning, SDGs


The development of computer and information technologies contributed to technological advancement in artificial intelligence (AI) by introducing "smart" apps in modern smartphones and gadgets. The need to apply AI in smart apps is due to the excessive demand of users in solving their day-to-day tasks. Their effectiveness was assessed by analyzing the average statistics based on the nature of the information requested in seven blocks of questions. The study results showed that depending on the accuracy of the query formulated, the data processing to derive the results from smart apps can be very different. The analysis was based on four indicators: accuracy, conformity, non-specificity, and no-response. Another urgent issue is studying the operation of Siri and Google Assistant smart apps to assess the reliability compliance of data from requests and application development perspectives. The study objectives included: analyzing and studying AI and its different forms; collecting data on the everyday use of apps in modern smartphones and gadgets with voice support functions; investigating device compatibility with smart apps to analyze and evaluate usage efficiency; studying the dependency of smart apps usage in everyday life.

Author Biographies

Nidal Al Said, College of Mass Communication, Ajman University, Ajman, United Arab Emirates

Nidal Al Said is Ph.D., Assistant Professor of the Department of Mass Communication, Ajman University, Ajman, United Arab Emirates. Research interests: computer and information technologies, artificial intelligence, voice assistant, chatbot, machine learning.

Dmitry Gura, Kuban State Technological University, Kuban State Agrarian University,

Dmitry Gura is Ph.D. in Technology, Associate Professor of the Department of Cadastre and GeoEngineering, Kuban State Technological University, Krasnodar, Russian Federation; Department of Geodesy, Kuban State Agrarian University, Krasnodar, Russian Federation. Research interests: computer and information technologies, artificial intelligence, voice assistant, chatbot, machine learning.

Dmitry Karlov, AMTI (branch) of the KubSTU

Dmitry Karlov is candidate of technical Sciences, docent of the Department of In-plant Electrical Equipment and Automation, AMTI (branch) of the KubSTU, Armavir, Russian Federation.  Research interests: computer and information technologies, artificial intelligence, voice assistant, chatbot, machine learning.


AbuShawar, B., and Atwell, E. Alice chatbot: Trials and outputs. Computaci´on y Sistemas 19, 4 (2015), 625–632.

Adam, M., Wessel, M., and Benlian, A. Ai-based chatbots in customer service and their effects on user compliance. Electronic Markets 31, 2 (2021), 427–445.

Ali, S. S., and Choi, B. J. State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics 9, 6 (2020), 1030.

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228 (2018).

Canbek, N. G., and Mutlu, M. E. On the track of artificial intelligence: Learning with intelligent personal assistants. Journal of Human Sciences 13, 1 (2016), 592–601.

Costa, P. Conversing with personal digital assistants: on gender and artificial intelligence. Journal of Science and Technology of the Arts 10, 3 (2018), 59–72.

Cunneen, M., Mullins, M., and Murphy, F. Artificial intelligence assistants and risk: framing a connectivity risk narrative. Ai & Society 35, 3 (2020), 625–634.

Date, R. C., Jesudasen, S. J., Weng, C. Y., et al. Applications of deep learning and artificial intelligence in retina. International Ophthalmology Clinics 59, 1 (2019), 39–57.

Donepudi, P. K. Application of artificial intelligence in automation industry. Asian Journal of Applied Science and Engineering 7, 1 (2018), 7– 20.

Doshi, S. V., Pawar, S. B., Shelar, A. G., and Kulkarni, S. S. Artificial intelligence chatbot in android system using open source programo. International Journal of Advanced Research in Computer and Communication Engineering 6, 4 (2017).

Du Preez, S. J., Lall, M., and Sinha, S. An intelligent web-based voice chat bot. In IEEE EUROCON 2009 (2009), IEEE, pp. 386–391.

Feustel, I. Adaptive dialogue management for a script knowledge based conversational assistant. Open Access Repositorium der Universitat Ulm und Technischen Hochschule Ulm, http://dx.doi.org/10.18725/OPARU-14106.

Horowitz, M. C., Allen, G. C., Saravalle, E., Cho, A., Frederick, K., and Scharre, P. Artificial intelligence and international security. 2018. Center for a New American Security.

Islas-Cota, E., Gutierrez-Garcia, J. O., Acosta, C. O., and Rodrıguez, L.-F. A systematic review of intelligent assistants. Future Generation Computer Systems 128 (2022), 45–62.

Kaplan, A., and Haenlein, M. Siri, siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62, 1 (2019), 15–25.

Lopez, G., Quesada, L., and Guerrero, L. A. Alexa vs. siri vs. cortana vs. google assistant: a comparison of speech-based natural user interfaces. In International conference on applied human factors and ergonomics (2017), Springer, pp. 241–250.

McGovern, S. L., Pandey, V., Gill, S., Aldrich, T., Myers, C., Desai, C., Gera, M., and Balasubramanian, V. The new age: artificial intelligence for human resource opportunities and functions. Ernst & Young LLP. (2018). Available: https://hrlens.org/wp-content/uploads/2019/11/EY-the-new-age-artificial-intelligence-for-human-resource-opportunities-and-functions.pdf.

Muslih, M., Supardi, D., Multipi, E., Nyaman, Y. M., Rismawan, A., et al. Developing smart workspace based iot with artificial intelligence using telegram chatbot. In 2018 International Conference on Computing, Engineering, and Design (ICCED) (2018), IEEE, pp. 230–234.

Nawaz, N., and Gomes, A. M. Artificial intelligence chatbots are new recruiters. IJACSA) International Journal of Advanced Computer Science and Applications 10, 9 (2019).

Park, C. W., and Seo, D. R. Sentiment analysis of twitter corpus related to artificial intelligence assistants. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA) (2018), IEEE, pp. 495–498.

Rahman, A., Al Mamun, A., and Islam, A. Programming challenges of chatbot: Current and future prospective. In 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (2017), IEEE, pp. 75–78.

Roh, Y., Heo, G., and Whang, S. E. A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering 33, 4 (2019), 1328–1347.

Satu, M. S., Parvez, M. H., et al. Review of integrated applications with aiml based chatbot. In 2015 International Conference on Computer and Information Engineering (ICCIE) (2015), IEEE, pp. 87–90.

Shawar, B. A., and Atwell, E. A comparison between Alice and Elizabeth chatbot systems. 2002. University of Leeds, School of Computing research report 2002.19.

Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., and Johnson, R. The influence of technology on the future of human resource management. Human resource management review 25, 2 (2015), 216–231.

Terzopoulos, G., and Satratzemi, M. Voice assistants and artificial intelligence in education. In Proceedings of the 9th Balkan Conference on Informatics (2019), pp. 1–6.

Thomas, N. An e-business chatbot using aiml and lsa. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016), IEEE, pp. 2740–2742.

Verma, P. K., Verma, R., Prakash, A., Agrawal, A., Naik, K., Tripathi, R., Alsabaan, M., Khalifa, T., Abdelkader, T., and Abogharaf, A. Machine-to-machine (m2m) communications: A survey. Journal of Network and Computer Applications 66 (2016), 83–105.

Vinyals, O., and Le, Q. A neural conversational model. arXiv preprint arXiv:1506.05869 (2015).

Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., and Zhang, J. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE 107, 8 (2019), 1738–1762.

How to Cite
Said, N.A., Gura, D. and Karlov, D. 2022. Efficiency of Smart AI-Based Voice Apps and Virtual Services Operating With Chatbots. MENDEL. 28, 2 (Dec. 2022), 9-16. DOI:https://doi.org/10.13164/mendel.2022.2.009.
Research articles