Applying Inhomogeneous Probabilistic Cellular Au-tomata Rules on Epidemic Model
Journal Title: International Journal of Advanced Research in Artificial Intelligence(IJARAI) - Year 2013, Vol 2, Issue 4
Abstract
This paper presents some of the results of our probabilis¬tic cellular automaton (PCA) based epidemic model. It is shown that PCA performs better than deterministic ones. We consider two possible ways of interaction that relies on a two-way split rules either horizontal or vertical interaction with 2 different probabilities causing more of the best possible choices for the behavior of the disease. Our results are a generalization of that Hawkins et al done.
Authors and Affiliations
Wesam M. Elsayed, Ahmed H. El-bassiouny, Elsayed F. Radwan
Method for Psychological Status Monitoring with Line of Sight Vector Changes (Human Eye Movements) Detected with Wearing Glass
Method for psychological status monitoring with line of sight vector changes (human eye movement) detected with wearing glass is proposed. Succored eye movement should be an indicator of humans’ psychological statu...
Neural Network Based Hausa Language Speech Recognition
Speech recognition is a key element of diverse applications in communication systems, medical transcription systems, security systems etc. However, there has been very little research in the domain of speech proces...
ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection
Application of Group Decision Support System (GDSS) can assist for delivering the decision of various opinions (preference) cancer detection based on the preferences of various expertise. In this paper we propose E...
An Empirical Comparison of Tree-Based Learning Algorithms: An Egyptian Rice Diseases Classification Case Study
Applications of learning algorithms in knowledge discovery are promising and relevant area of research. The classification algorithms of data mining have been successfully applied in the recent years to predict Egy...
A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks
This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the...