ASSESSMENT OF THE IMPACT OF A DISRUPTOR ON THE COMMUNICATION ENVIRONMENT

Authors

DOI:

https://doi.org/10.32347/tit.2024.71.03.09

Keywords:

Сommunications, convolutional neural network, project-oriented organization, disruptor, machine learning

Abstract

Abstract. In today's world, where the volume of information is constantly growing, effective communication is a key element of success in object-oriented organizations, especially those that use virtual teams, so identifying communication problems is important. To improve the quality of the analysis of communication processes, it is proposed to use a neural network. The scheme of this network is presented in the document. The network was trained on the DisRating dataset, which was developed based on the evaluation of the organization's data, and demonstrated high classification efficiency, which was determined using a series of analytical graphs. The results were analyzed using graphs (Bias histogram, Kernel histogram, Change of losses Mean Absolute Error). Based on the initial data of the network with the help of GAP analysis, the quantitative impact on individuals in the field of communication was determined. A model for quantitative assessment of the disruptor impact on the communication environment is presented. The proposed approach achieved high accuracy in the tasks of identifying the areas of the disruptor influence and its evaluation.

References

REFERENCES

Bushuyev, S., Bushuiev, D., Bushuieva, V., Bushuyeva, N., & Tykchonovych, J. STRATEGIC PROJECT MANAGEMENT DEVELOPMENT UNDER INFLUENCE OF ARTIFICIAL INTELLIGENCE. ISВN 978-617-05-0455-5

O. Voitenko, B. Lysytsin and A. Timinsky, "Bi-Adaptive Management of Strategic Projects Development of high-tech companies through the improvement of competencies," 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine, 2020, pp. 180-184, doi: 10.1109/CSIT49958.2020.9321989.

Kontsevyi, Vladyslav & Voitenko, Oleksandr. (2023). Communications disruptor in project-oriented organisations. 1-4. 10.1109/CSIT61576.2023.10324097.

Popescu, Marius-Constantin & Balas, Valentina & Perescu-Popescu, Liliana & Mastorakis, Nikos. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems. 8.

Fionn Murtagh, Multilayer perceptrons for classification and regression, Neurocomputing, Volume 2, Issues 5–6, 1991, Pages 183-197, ISSN 0925-2312, https://doi.org/10.1016/0925-2312(91)90023-5.

Sakshi Indolia, Anil Kumar Goswami, S.P. Mishra, Pooja Asopa, Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach, Procedia Computer Science, Volume 132,2018, Pages 679-688, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.05.069

Purwono, Purwono & Ma'arif, Alfian & Rahmaniar, Wahyu & Imam, Haris & Fathurrahman, Haris Imam Karim & Frisky, Aufaclav & Haq, Qazi Mazhar Ul. (2023). Understanding of Convolutional Neural Network (CNN): A Review. 2. 739-748. 10.31763/ijrcs.v2i4.888.

Du, Ke-Lin & Swamy, M.N.s. (2014). Recurrent Neural Networks. 10.1007/978-1-4471-5571-3_11.

Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun, Graph neural networks: A review of methods and applications, AI Open, Volume 1, 2020, Pages 57-81, ISSN 2666-6510, https://doi.org/10.1016/j.aiopen.2021.01.001.

Abdenour Hacine-Gharbi, Philippe Ravier, Rachid Harba, Tayeb Mohamadi, Low bias histogram-based estimation of mutual information for feature selection, Pattern Recognition Letters, Volume 33, Issue 10, 2012, Pages 1302-1308, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2012.02.022

Rosenblatt, M. (1956). "Remarks on Some Nonparametric Estimates of a Density Function". The Annals of Mathematical Statistics. 27 (3): 832–837. doi:10.1214/aoms/1177728190.

Parzen, E. (1962). "On Estimation of a Probability Density Function and Mode". The Annals of Mathematical Statistics. 33 (3): 1065–1076. doi:10.1214/aoms/1177704472. JSTOR 2237880.

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2001). The Elements of Statistical Learning : Data Mining, Inference, and Prediction : with 200 full-color illustrations. New York: Springer. ISBN 0-387-95284-5. OCLC 46809224.

Xiaoying Zou, Xizhao Wang, Si Cen, Guoquan Dai, Chao Liu, Knowledge graph embedding with self adaptive double-limited loss,Knowledge-Based Systems, Volume 252, 2022, 109310, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2022.109310.

Weijie Wang1 and Yanmin Lu1, Published under licence by IOP Publishing Ltd, IOP Conference Series: Materials Science and Engineering, Volume 324, 2017 the 5th International Conference on Mechanical Engineering, Materials Science and Civil Engineering 15–16 December 2017, Kuala Lumpur, Malaysia, Citation Weijie Wang and Yanmin Lu 2018 IOP Conf. Ser.: Mater. Sci. Eng. 324 012049, DOI 10.1088/1757-899X/324/1/012049

Patrick Schneider, Fatos Xhafa, Chapter 3 - Anomaly detection: Concepts and methods, Editor(s): Patrick Schneider, Fatos Xhafa, Anomaly Detection and Complex Event Processing over IoT Data Streams, Academic Press, 2022, Pages 49-66, ISBN 9780128238189, https://doi.org/10.1016/B978-0-12-823818-9.00013-4.

Kim, Sora & Ji, Yingru. (2018). Gap Analysis. 1-6. 10.1002/9781119010722.iesc0079

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Published

2024-12-30

How to Cite

KONTSEVYI, V. (2024). ASSESSMENT OF THE IMPACT OF A DISRUPTOR ON THE COMMUNICATION ENVIRONMENT. Transfer of Innovative Technologies, 7(1), 69–76. https://doi.org/10.32347/tit.2024.71.03.09

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Section

Information Technology