Titlebook: Intelligent Computing; Proceedings of the 2 Kohei Arai,Supriya Kapoor,Rahul Bhatia Conference proceedings 2020 Springer Nature Switzerland

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MESRS: Models Ensemble Speech Recognition System,d for SR, based on our implementation for an ensemble of multiple deep learning (DL) models with different architectures. Contrary to standard SR systems, we ensemble the most commonly used DL architectures followed by dynamic weighted averages, in order to classify audio clips correctly. Models’ tr

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DeepConAD: Deep and Confidence Prediction for Unsupervised Anomaly Detection in Time Series,or vehicle, capture and exploit time-series data from such sensors for health monitoring tasks such as anomaly detection, fault detection, as well as prognostics. Anomalies or outliers are unexpected observations which deviate significantly from the expected observations and typically correspond to

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Reduced Order Modeling Assisted by Convolutional Neural Network for Thermal Problems with Nonparametions of the solutions to this problem, under nonparametrized variability of the geometry, and the convection and radiation boundary conditions, using physics-based reduced order models (ROM). Nonparametrized geometrical variability is a challenging task in model order reduction, which we propose to

Heart-Attack

Deep Convolutional Generative Adversarial Networks Applied to 2D Incompressible and Unsteady Fluid tional Fluid Dynamics (CFD) for engineering problems. We claim that these DCGANs could be used in order to represent in an efficient fashion high-dimensional realistic samples. Let us take the example of fluid flows’ unsteady velocity and pressure fields computation when subjected to random variatio

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Improving Gate Decision Making Rationality with Machine Learning,osts and sets free resources for successful ideas and projects. A large body of literature is available on the decision making in IPM. In this study, we analyzed within IPM the cancellation of ideas and projects by gatekeeping boards as well as the possibilities of applying machine learning. The hyp

DRAFT

Urban Mobility Swarms: A Scalable Implementation,alize swarm membership. Nodes then join or disconnect from others based on proximity, accommodating the dynamically changing topology of urban mobility networks. This paper provides a technical description of our system, including the protocol and algorithm to coordinate the swarming behavior that e

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