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Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data.

, and . SSCI, page 516-521. IEEE, (2015)

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Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data., and . SSCI, page 516-521. IEEE, (2015)Distributed Learning of Deep Sparse Neural Networks for High-dimensional Classification., , , and . IEEE BigData, page 1587-1592. IEEE, (2018)Forward Gradients for Data-Driven CFD Wall Modeling., , , and . CoRR, (2023)Direct Error Driven Learning for Classification in Applications Generating Big-Data., , and . Development and Analysis of Deep Learning Architectures, volume 867 of Studies in Computational Intelligence, Springer, (2020)Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging., , , , , , , and . AI4S, page 1-6. IEEE, (2022)Self-supervised Learning for Anomaly Detection in Computational Workflows., , , , , , and . CoRR, (2023)Flow-Bench: A Dataset for Computational Workflow Anomaly Detection., , , , , , and . CoRR, (2023)A Multi-step Nonlinear Dimension-reduction Approach with Applications to Bigdata., , and . INNS Conference on Big Data, volume 144 of Procedia Computer Science, page 81-88. Elsevier, (2018)AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification., , , , , and . ICPR, page 1908-1914. IEEE, (2022)SF-SFD: Stochastic Optimization of Fourier Coefficients to Generate Space-Filling Designs., , and . WSC, page 3636-3646. IEEE, (2023)