DATA SCIENCE TEXNOLOGIYALARI ASOSIDA TELEKOMMUNIKATSIYA TARMOQLARINING SAMARADORLIGINI BAHOLASH
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https://doi.org/10.5281/zenodo.21253990;
Data Science, telekommunikatsiya tarmoqlari, Big Data, sun’iy intellekt, mashinali o‘qitish, tarmoq samaradorligi, QoS, KPI, bashoratli tahlil, optimallashtirish.Abstrak
Mazkur maqolada telekommunikatsiya tarmoqlarining samaradorligini baholashda Data Science texnologiyalaridan foydalanishning nazariy va amaliy jihatlari tadqiq etilgan. Tadqiqotda katta hajmdagi tarmoq ma’lumotlarini yig‘ish, qayta ishlash, tahlil qilish va bashoratlash jarayonlarida zamonaviy Data Science usullarining afzalliklari yoritilgan. Shuningdek, mashinali o‘qitish algoritmlari, statistik modellashtirish hamda sun’iy intellekt texnologiyalarining tarmoq yuklamasi, xizmat ko‘rsatish sifati (QoS), tarmoq ishonchliligi va resurslardan foydalanish samaradorligini baholashdagi o‘rni ilmiy jihatdan asoslab berilgan. Tadqiqot natijalari telekommunikatsiya operatorlari faoliyatida ma’lumotlarga asoslangan boshqaruv qarorlarini qabul qilish, tarmoq nosozliklarini oldindan prognozlash hamda infratuzilma resurslarini optimallashtirish imkoniyatlarini kengaytirishi ko‘rsatib berilgan.Iqtiboslar
Provost F., Fawcett T. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Sebastopol: O'Reilly Media, 2013. – 414 p.
Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques. 3rd Edition. Waltham: Morgan Kaufmann Publishers, 2012. – 703 p.
Azzouni A., Pujolle G. NeuroPlan: Deep Learning for Network Traffic Prediction. Proceedings of the IEEE International Conference on Communications (ICC). Paris, France, 2017. – P. 1–6.
Cisco Systems. Cisco Annual Internet Report (2018–2023). White Paper. San Jose: Cisco Systems Inc., 2020. – 41 p.
International Telecommunication Union (ITU-T). Recommendation E.800: Definitions of Terms Related to Quality of Service. Geneva: ITU, 2008. – 28 p.
Barabási A.-L. Network Science. Cambridge: Cambridge University Press, 2016. – 474 p.
Goodfellow I., Bengio Y., Courville A. Deep Learning. Cambridge (MA): MIT Press, 2016. – 775 p.
Bifet A., Gavaldà R. Learning from Time-Changing Data with Adaptive Windowing. Proceedings of the SIAM International Conference on Data Mining (SDM). Minneapolis, USA, 2007. – P. 443–448.
Kipf T. N., Welling M. Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations (ICLR). Toulon, France, 2017.
Zaharia M., Chowdhury M., Franklin M. J., Shenker S., Stoica I. Spark: Cluster Computing with Working Sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud). Boston, USA, 2010. – P. 1–7.
Russell S., Norvig P. Artificial Intelligence: A Modern Approach. 4th Edition. Hoboken: Pearson Education, 2021. – 1136 p.
Bishop C. M. Pattern Recognition and Machine Learning. New York: Springer, 2006. – 738 p.
Murphy K. P. Machine Learning: A Probabilistic Perspective. Cambridge (MA): MIT Press, 2012. – 1104 p.
Aggarwal C. C. Data Mining: The Textbook. Cham: Springer International Publishing, 2015. – 734 p.
Leskovec J., Rajaraman A., Ullman J. D. Mining of Massive Datasets. 3rd Edition. Cambridge: Cambridge University Press, 2020. – 602 p.
Tan P.-N., Steinbach M., Karpatne A., Kumar V. Introduction to Data Mining. 2nd Edition. New York: Pearson, 2019. – 839 p.
Kurose J. F., Ross K. W. Computer Networking: A Top-Down Approach. 8th Edition. New York: Pearson, 2021. – 864 p.
Stallings W. Data and Computer Communications. 11th Edition. New York: Pearson, 2020. – 888 p.
Andrews J. G., Buzzi S., Choi W., Hanly S. V., Lozano A., Soong A. C. K., Zhang J. C. What Will 5G Be? IEEE Journal on Selected Areas in Communications. Vol. 32, No. 6. 2014. – P. 1065–1082.
Cisco Systems. Cisco Visual Networking Index: Forecast and Trends. San Jose: Cisco Systems Inc., 2020.
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