International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 4, 8 Aug. 2025
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Determining the Number of Effective Distributions Based on Neural Network Ensemble
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Author(s)
Index Terms
Big Data, Internal Distribution Mechanism, Number Of Effective Distributions, Neural Network Ensemble
Abstract
Since big data streams contain hidden meanings, there is a permanent motivation to store and process them. However, storing and processing this data requires special methods and tools. Today, the most effective approach for this situation is distributed computing mechanisms. However, this approach is economically expensive, since it requires a lot of computing resources. Therefore, users who do not have economic capabilities strive to solve problems with large data streams on a single server. In this situation, a sharp drop in efficiency in terms of time is observed. However, even for a single computing machine, the use of an internal distribution mechanism can lead to efficiency in terms of time. In this case, efficiency depends on several indicators, the most important of which is determining the number of effective distributions. However, determining the number of effective distributions is a complex process. To solve this problem, this research paper considers the use of artificial intelligence algorithms. First of all, the research methodology is developed and processes that are in it are explained. In the next step, Random Forest, XGBoost, Support Vector Regression, and Multiple Linear Regression algorithms are tested to determine the number of effective distributions. In order to improve the accuracy of the study, a multilayer neural network is improved, that is, a neural network ensemble method is developed that combines the above machine learning algorithms. At the end of the study, the research results are presented and explained in detail.
Cite This Paper
Nazarov Fayzullo, Rashidov Akbar, Yarmatov Sherzodjon, "Determining the Number of Effective Distributions Based on Neural Network Ensemble", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.4, pp.69-77, 2025. DOI:10.5815/ijisa.2025.04.07
Reference
[1]Rawat R., Yadav R. Big Data: Big Data Analysis, Issues and Challenges and Technologies. IOP Conference Series: Materials Science and Engineering, 2021, DOI 10.1088/1757-899X/1022/1/012014
[2]Badshah, A., Daud, A., Alharbey, R. et al. Big data applications: overview, challenges and future. Artif Intell Rev 57, 290 (2024). https://doi.org/10.1007/s10462-024-10938-5
[3]A. Rashidov, D. Mardonov and A. Soliev, "Diagnosis of Diabetes Mellitus Based on Artificial Intelligence Algorithms," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 349-353, doi: 10.1109/SmartIndustryCon65166.2025.10986060
[4]Singh, Nitin. Emerging Trends in Technologies for Big Data. The International Technology Management Review. 5. 202. 10.2991/itmr.2015.5.4.4.
[5]D. Junaydullaev, S. Tursunov and A. Rashidov, "An Approach Based on Data Profiling at the Preparing a Dataset for Cleaning," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 578-583, doi: 10.1109/SmartIndustryCon65166.2025.10986179
[6]Gonzalez A. et. al. Profiling Hyperscale Big Data Processing. In Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA '23). Association for Computing Machinery, New York, NY, USA, Article 47, 1–16. https://doi.org/10.1145/3579371.3589082
[7]Janev V. et. al. Knowledge graphs and big data processing. Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI, 12072. DOI: 10.1007/978-3-030-53199-7
[8]Rashidov A., Akhatov A., Mardonov D.. The Distribution Algorithm of Data Flows Based on the BIRCH Clustering in the Internal Distribution Mechanism. 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 923-927, doi: 10.1109/SmartIndustryCon61328.2024.10516193.
[9]X. Sun, Y. He, D. Wu and J. Z. Huang. Survey of Distributed Computing Frameworks for Supporting Big Data Analysis. in Big Data Mining and Analytics, vol. 6, no. 2, pp. 154-169, June 2023, doi: 10.26599/BDMA.2022.9020014.
[10]Rashidov, A., Akhatov, A. R., & Nazarov, F. M. Real-Time Big Data Processing Based on a Distributed Computing Mechanism in a Single Server. In C. Ananth, N. Anbazhagan, & M. Goh (Eds.), Stochastic Processes and Their Applications in Artificial Intelligence (pp. 121-138). IGI Global. https://doi.org/10.4018/978-1-6684-7679-6.ch009
[11]Akhatov, A. R., & Nazarov, F. M., Rashidov A. Mechanisms of information reliability in big data and blockchain technologies . ICISCT 2021: Applications, Trends and Opportunities, 3-5.11.2021, doi: 10.1109/ICISCT52966.2021.9670052
[12]Mazumder, Sourav & Bhadoria, Robin & Deka, Ganesh. Distributed Computing in Big Data Analytics: Concepts, Technologies and Applications [J]. 10.1007/978-3-319-59834-5.
[13]Steen Maarten, Tanenbaum Andrew. A brief introduction to distributed systems. Computing. 98. 10.1007/s00607-016-0508-7.
[14]Akhatov A., Renavikar A., Rashidov A. Optimization of the database structure based on Machine Learning algorithms in case of increased data flow. Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing And Security (ICABCS 2023), Gr. N01 Da, Up, India, 24-25 February 2023
[15]Akhatov A., Renavikar A., Rashidov A., Nazarov F. Optimization of the number of databases in the Big Data processing [J]. Проблемы информатики, № 1(58) 2023, DOI: 10.24412/2073-0667-2023-1-33-47
[16]Rashidov, A., Akhatov, A., Nazarov, F. The Same Size Distribution of Data Based on Unsupervised Clustering Algorithms [J]. In: Hu, Z., Zhang, Q., He, M. (eds) Advances in Artificial Systems for Logistics Engineering III. ICAILE 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-031-36115-9_40
[17]J.L. Gustafson “Reevaluating Amdahl’s law” // Communications of ACM 31(5), 1988, 532-33.
[18] Makhmadiyarovich N.F. Sherzodjon Y. Development of algorithms for predictive evaluation of investment projects based on machine learning. Artificial Intelligence, Blockchain, Computing and Security Volume 2. eBook ISBN: 9781032684994, pages 681 – 685, January 2023.
[19]L. Y. Xuramov, S. Xafizova and M. Mustaffaqulov, "Calculating Singular Integrals with Cauchy Kernels in Digital Processing of Gastroenterological Medical Signals," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 97-103, doi: 10.1109/SmartIndustryCon65166.2025.10985974.
[20]S. Yarmatov, X. Bustanov and L. Safarova, "Optimization and Improvement of Reliability of Machine Learning Algorithms Based on Regularization Methods," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 394-398, doi: 10.1109/SmartIndustryCon65166.2025.10986242.
[21]L. Y. Xuramov, A. B. Baxromo and M. E. Sanayev, "Advanced Noise-Resistant Electrogastroenterological Classification Employing Convolutional Neural Networks and Hybrid Wavelet Transform Denoising," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 89-96, doi: 10.1109/SmartIndustryCon65166.2025.10986224.
[22]N. Fayzullo, S. Sariyev and Y. Sherzodjon, "Analyzing the Effectiveness of Ensemble Methods in Solving Multi-Class Classification Problems," 2025 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2025, pp. 788-793, doi: 10.1109/SmartIndustryCon65166.2025.10986248.
[23]Bolikulov F. et. al. Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms. Mathematics 2024, 12, 2553. https://doi.org/10.3390/math12162553
[24]Nazarov F. M., Yarmatov S. Optimization of Prediction Results Based on Ensemble Methods of Machine Learning. 2023 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2023, pp. 181-185, doi: 10.1109/SmartIndustryCon57312.2023.10110726.
[25]Ali, Zeravan & Abduljabbar, Ziyad & Tahir, Hanan & Sallow, Amira & Almufti, Saman. Exploring the Power of eXtreme Gradient Boosting Algorithm in Machine Learning: a Review [J]. Academic Journal of Nawroz University. 12. 320-334. 10.25007/ajnu.v12n2a1612.
[26]S. Rana, H. M. Rai, L.Khuramov and D. Mardonov,Shaping the Future with Quantum Computing: An Exploration of its Emerging Field and Revolutionary Potential, Procedia Computer Science, Volume 259, 2025, Pages 844-853, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2025.04.036.
[27]Nazarov F. M., Yarmatov S., Xamidov M. Machine Learning Price Prediction on Green Building Prices. 2024 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russian Federation, 2024, pp. 906-911, doi: 10.1109/SmartIndustryCon61328.2024.10515790.
[28]Hari Mohan Rai, Aditya Pal, Rashidov Akbar Ergash o’g’li, Bobokhonov Akhmadkhon Kholmirzokhon Ugli, Yarmatov Sherzojon Shokirovich. “Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection” Procedia Computer Science, Volume 259, 2025, Pages 140-149, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2025.03.315
[29]Mekhriddin Nurmamatov, Shokhrukh Sariyev and Izhar Uddin, “Methods of Using Artificial Intelligence Algorithms in Human Resource Management”, International Russian Smart Industry Conference (SmartIndustryCon), 2025, doi.org/10.1109/SmartIndustryCon65166.2025.10986087