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Forecasting multivariate time-series data using LSTM and mini-batches
(Springer, 2020)
Multivariate time-series data forecasting is a challenging task due to nonlinear interdependencies in complex industrial systems. It is crucial to model these dependencies automatically using the ability of neural networks ...
MaLeFICE: Machine learning support for continuous performance improvement in computational engineering
(Wiley, 2022-04-25)
Computer aided engineering (CAE) practices improved drastically within the last decade due to ease of access to computing resources and open-source software. However, increasing complexity of hardware and software settings ...
Towards low cost and smart load testing as a service using containers
(Springer, 2021)
Providing end-users with high quality e-commerce, online communication, education services requires careful performance monitoring, tuning and prediction under heavy traffic loads. To address this issue, we propose and ...
Finecloud: Fine-grained cloud service advisory using machine learning
(IEEE, 2022)
Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as ...
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