Poster
in
Affinity Workshop: Black in AI
Using Epidemic Multi-agent Synthetic Datasets for Predictions in Communication Networks: An LSTM Perspective
ChukwuNonso H Nwokoye · Chukwuemeka E Etodike · Queen Nkechi Chigbue
Keywords: [ Multi-Agent Systems ] [ machine learning ] [ Deep Learning ] [ artificial intelligence ]
The epidemic Vulnerable-Latent-Contagious-Recovery-Inoculation (VLCRV-I) was proposed. Thereafter, an equivalent multi agent model was developed in order to cater for malware spread in computer networks. Then, various LSTM types was used for prediction and metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error were used to evaluate model performance. The best prediction of vulnerable computers were obtained using Stacked LSTM of 512 Layers and the Relu Activation Function.