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Heat-loss cycle prediction in steelmaking plants through artificial neural network.

, , and . J. Oper. Res. Soc., 73 (2): 326-337 (2022)

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Development of Intelligent Robotic Process Automation: A Utility Case Study in Brazil., , , , , , , , and . IEEE Access, (2021)Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks., , , , , and . Int. J. Comput. Intell. Appl., 19 (4): 2050028:1-2050028:14 (2020)Cost-Sensitive Learning based on Performance Metric for Imbalanced Data., , , and . Neural Process. Lett., 54 (4): 3097-3114 (2022)Heat-loss cycle prediction in steelmaking plants through artificial neural network., , and . J. Oper. Res. Soc., 73 (2): 326-337 (2022)Multi-objective neural network model selection with a graph-based large margin approach., , , , and . Inf. Sci., (2022)Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition., , , , , and . IEEE Access, (2022)Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning., , , , , , , , , and 1 other author(s). ICPS, page 250-256. IEEE, (2020)Fault Detection in Continuous Industrial Chemical Processes: A New Approach Using the Hidden Markov Modeling. Case Study: A Boiler from a Brazilian Cellulose Pulp Mill., and . IDEAL, volume 7435 of Lecture Notes in Computer Science, page 743-752. Springer, (2012)Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function., , , and . Neural Process. Lett., 50 (2): 1937-1949 (2019)Intent Identification in Unattended Customer Queries Using an Unsupervised Approach., , , , , and . J. Inf. Knowl. Manag., 20 (3): 2150037:1-2150037:26 (2021)