Mastering Neural Network Prediction for Enhanced System Reliability
DOI:
https://doi.org/10.63995/RVZF7165Keywords:
Interpretability; Neural Networks; Predictive Maintenance; Reliability; Robustness; System Efficiency; Trustworthy ModelsAbstract
Mastering neural network prediction is crucial for enhancing system reliability across various fields, from healthcare to autonomous driving. Neural networks, with their ability to learn and generalize from vast datasets, offer unparalleled predictive capabilities. By accurately forecasting system behaviors and potential failures, neural networks can significantly improve reliability and efficiency. For instance, in predictive maintenance, neural networks can analyze sensor data to predict equipment failures before they occur, reducing downtime and maintenance costs. Similarly, in healthcare, predictive models can identify patients at risk of developing certain conditions, enabling early interventions and better outcomes. To achieve high reliability, it is essential to address challenges such as overfitting, interpretability, and robustness of neural network models. Techniques like cross-validation, regularization, and the use of explainable AI (XAI) tools are vital in building trustworthy models. Moreover, continuous monitoring and updating of neural networks with new data ensure their predictions remain accurate over time. Integrating neural network predictions into system design and operational strategies can lead to more resilient and adaptive systems. By leveraging the power of neural networks, organizations can anticipate and mitigate risks more effectively, leading to enhanced reliability and performance in critical applications. This advancement underscores the transformative potential of neural networks in fostering more reliable and efficient systems.
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References
Jianzhou Wang, Feng Liu, Yiliao Song, and Jing Zhao. “A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system”. In: r2 48 (2016), pp. 281–297.
Gerald Warnecke and R Kluge. “Control of tolerances in turning by predictive control with neural networks”. In: Journal of Intelligent Manufacturing 9 (1998), pp. 281–287.
Jason Deutsch and David He. “Using deep learning-based approach to predict remaining useful life of rotating components”. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems 48.1 (2017), pp. 11–20.
Kamran Javed. “A robust and reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering”. PhD thesis. Université de Franche-Comté, 2014.
Nesim Yilmaz, Tuncer Demir, Safak Kaplan, and Sevilin Demirci. “Demystifying Big Data Analytics in Cloud Computing”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 25–36.
Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, and Tarek Abdelzaher. “Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework”. In: Proceedings of the 15th ACM conference on embedded network sensor systems. 2017, pp. 1–14.
Thomas G Calderon and John J Cheh. “A roadmap for future neural networks research in auditing and risk assessment”. In: International Journal of Accounting Information Systems 3.4 (2002), pp. 203–236.
Yang Liu, Nam Dinh, Yohei Sato, and Bojan Niceno. “Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results”. In: Applied Thermal Engineering 144 (2018), pp. 305–320.
Jason Brownlee. Long short-term memory networks with python: develop sequence prediction models with deep learning. Machine Learning Mastery, 2017.
Friedrich Stephanie and Louisa Karl. “Incorporating Renewable Energy Systems for a New Era of Grid Stability”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 1.1 (2020), pp. 37–49.
Monica Lam. “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis”. In: Decision support systems 37.4 (2004), pp. 567–581.
Christine K Lee, Ira Hofer, Eilon Gabel, Pierre Baldi, and Maxime Cannesson. “Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality”. In: Anesthesiology 129.4 (2018), pp. 649–662.
Rashid Mijumbi, Sidhant Hasija, Steven Davy, Alan Davy, Brendan Jennings, and Raouf Boutaba. “Topology-aware prediction of virtual network function resource requirements”. In: IEEE Transactions on Network and Service Management 14.1 (2017), pp. 106–120.
ChoHee Kim, Donghyun Gwan, and Minho Sena Nam. “Beyond the Atmosphere: The Revolution in Hypersonic Flight”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 2.1 (2021), pp. 152–163.
Zhengliang Xiang, Yuequan Bao, Zhiyi Tang, and Hui Li. “Deep reinforcement learning-based sampling method for structural reliability assessment”. In: Reliability Engineering & System Safety 199 (2020), p. 106901.
Kenneth O Stanley, Jeff Clune, Joel Lehman, and Risto Miikkulainen. “Designing neural networks through neuroevolution”. In: Nature Machine Intelligence 1.1 (2019), pp. 24–35.
Abbas Sheikhtaheri, Farahnaz Sadoughi, and Zahra Hashemi Dehaghi. “Developing and using expert systems and neural networks in medicine: a review on benefits and challenges”. In: Journal of medical systems 38 (2014), pp. 1–6.
Stylianos Ioannis Mimilakis, Konstantinos Drossos, Tuomas Virtanen, and Gerald Schuller. “Deep neural networks for dynamic range compression in mastering applications”. In: Audio Engineering Society Convention 140. Audio Engineering Society. 2016.
Long Wang, Zijun Zhang, Huan Long, Jia Xu, and Ruihua Liu. “Wind turbine gearbox failure identification with deep neural networks”. In: IEEE Transactions on Industrial Informatics 13.3 (2016), pp. 1360–1368.
Manuel André, João Margarida, Heitor Garcia, and Augusto Dante. “Complexities of Blockchain Technology and Distributed Ledger Technologies: A Detailed Inspection”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 2.1 (2021), pp. 164–177.
Hao Peng and Xiaoli Bai. “Artificial neural network–based machine learning approach to improve orbit prediction accuracy”. In: Journal of Spacecraft and Rockets 55.5 (2018), pp. 1248–1260.
Simon Haykin. “Neural networks expand SP’s horizons”. In: IEEE Signal Processing Magazine 13.2 (1996), pp. 24–49.
Wei Jiang and Hans D Schotten. “Neural network-based fading channel prediction: A comprehensive overview”. In: IEEE Access 7 (2019), pp. 118112–118124.
Adrichem De Jong, Mark Jansen, Jeffrey Van Dijk, and Johannes Meyer. “Analysis of Innovative Practices in Advanced Materials and Structural Engineering”. In: Fusion of Multidisciplinary Research, An International Journal (FMR) 2.1 (2021), pp. 178–188.
Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada, Abubakar Malah Umar, Okafor Uchenwa Linus, Humaira Arshad, Abdullahi Aminu Kazaure, Usman Gana, and Muhammad Ubale Kiru. “Comprehensive review of artificial neural network applications to pattern recognition”. In: IEEE access 7 (2019), pp. 158820–158846.
Alberto Bosio, Paolo Bernardi, Annachiara Ruospo, and Ernesto Sanchez. “A reliability analysis of a deep neural network”. In: 2019 IEEE Latin American Test Symposium (LATS). IEEE. 2019, pp. 1–6.
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