Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

Security graphs are becoming indispensable for understanding system access and network activity and empowering security teams to anticipate, detect and contain breaches.

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning!

With random network coding, network nodes between the source and receivers are able to not only relay and replicate data packets, but also code them using randomly generated coding coefficients. From ...

To address this challenge, this work presents a physics-informed graph convolutional recurrent network (PIGCRN) that incorporates both spatial and temporal information on chemical process networks and ...

Recently, network diversity based security metrics is attracting researcher's interests. Several efforts have been devoted into the network diversity modeling, for the purpose of evaluating network ...

Python implementation of Markov Networks for neural computing - updated for modern NumPy. - jtatman/MarkovNetworkNew ...