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Application of machine learning based models in computer network data

   | 20. Sept. 2023

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Wang, Q. (2015). Computer Network Fault Diagnosis Based On Neural Network. International Journal of Future Generation Communication and Networking, 8(5), 39-50. Search in Google Scholar

Liang, J., Zhou, N., Yun, L. (2018). t/k-fault diagnosis algorithm of n-dimensional hypercube network based on the MM∗ model. Journal of Systems Engineering and Electronics. Search in Google Scholar

Dlab, C., Zc, C., Bq, C., et al. (2020). Signal frequency domain analysis and sensor fault diagnosis based on artificial intelligence. Computer Communications, 160, 71-80. Search in Google Scholar

Fu, W., Chien, C. F., & Tang, L. (2022). Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower industry 3.5 smart production and an empirical study. Journal of Intelligent Manufacturing, 33. Search in Google Scholar

Rani, S., Sivia, J. S. (2020). Design and development of virtual instrument for fault diagnosis in fractal antenna array. International journal of RF and microwave computer-aided engineering, 30(1), e22026.1-e22026.10. Search in Google Scholar

Yang, K., Chu, R., Zhang, R., et al. (2019). A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network. Sensors, 20(1), 162. Search in Google Scholar

Bhanu, P. V., Kulkarni, P. V., Soumya, J. (2019). Fault-Tolerant Network-on-Chip Design with Flexible Spare Core Placement. ACM Journal on Emerging Technologies in Computing Systems, 15(1), 1-23. Search in Google Scholar

Xi, J. (2020). Output feedback fault-tolerant control for a class of nonlinear systems via dynamic gain and neural network. Neural computing & applications, 32(10). Search in Google Scholar

Fanchiang, K. H., Huang, Y. C., Kuo, C. C. (2021). Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier. Electronics, 10(10), 1161. Search in Google Scholar

Benmessahel, I., Xie, K., Chellal, M. (2018). A new evolutionary neural networks based on intrusion detection systems using multiverse optimization. Applied Intelligence, 48(C), 2315-2327. Search in Google Scholar

Zhu, C., Tian, W., Yin, B., et al. (2020). Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms. Applied Energy, 268, 115025. Search in Google Scholar

Chong, J., Tjurin, P., Niemela, M., et al. (2021). Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms. Gait & posture, 89, 45-53. Search in Google Scholar

Li, X., Chen, P., Fan, K. (2020). Overview of Deep Convolutional Neural Network Approaches for Satellite Remote Sensing Ship Monitoring Technology. IOP Conference Series: Materials Science and, Engineering, 730(1), 012071 (10pp). Search in Google Scholar

Wei, D., Xue, K., Bruschi, R., et al. (2020). Guest Editorial Leveraging Machine Learning in SDN/NFV-Based Networks. IEEE Journal on Selected Areas in Communications, 38(2), 245-247. Search in Google Scholar

Wiemken, T. L., Rutschman, A. S. (2020). Methodology Minute: A Machine Learning Primer for Infection Prevention and Control. American Journal of Infection Control, 48(12). Search in Google Scholar

Hossain, S. S., Ayodele, B. V., Ali, S. S., Cheng, C. K., & Mustapa, S. I. (2022). Comparative analysis of support vector machine regression and gaussian process regression in modeling hydrogen production from waste effluent. Sustainability, 14. Search in Google Scholar

Al-Yaseen, W. L., Othman, Z. A., Nazri, M. (2017). Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Systems with Applications, 67, 296-303. Search in Google Scholar

Inoue, T., Yu, M., Ikami, T., et al. (2021). Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points. Physics of Fluids, 33(7), 077105. Search in Google Scholar

B. H. W. A., A. H. Z., A. Y. L. (2019). Using a posterior probability support vector machine model to assess soil quality in Taiyuan, China - ScienceDirect. Soil and Tillage Research, 185, 146-152. Search in Google Scholar

Yu, X., Wang, H. (2021). Support vector machine classification model for color fastness to ironing of vat dyes. Textile Research Journal, 91(15-16), 1889-1899. Search in Google Scholar

eISSN:
2444-8656
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik