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SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning
Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically rely on large numbers of sequential simulations, such as optimization and uncertainty quantification.
Neural PDE Solvers for Irregular Domains
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.
NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains
One of the sources of the attacks on IoT ecosystems are botnets.
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks
In this paper, a Deep Learning enabled Long Short Term Memory (LSTM) Autoencoder and a 13-feature Deep Neural Network (DNN) models were developed which performed a lot better in terms of accuracy on UNSW-NB15 and Bot-IoT datsets.
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study
A Hyperspectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image.
