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
Wildlife Damage Estimation and Prediction Using Blog and Tweet Information
Wildlife damage estimation and prediction using blog and tweet information is conducted. Through a regressive analysis with the truth data about wildlife damage which is acquired by the federal and provincial gover...
The Need for a New Data Processing Interface for Digital Forensic Examination
Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. There is a need for law enforcement agencies, government and private organis...
Diagrammatic Language for Artificial Intelligence: Representation of Things that Flow
This paper utilizes a diagrammatic language for expressing certain philosophical notions, such as possible worlds, beliefs, and propositions. The focus is on a diagrammatic representation that depicts “things” to s...
Secure Copier Which Allows Reuse Copied Documents with Sorting Capability in Accordance with Document Types
Secure copy machine which allows reuse copied documents with sorting capability in accordance with the document types. Through experiments with a variety of document types, it is found that copied documents can be shared...
An Inference Mechanism Framework for Association Rule Mining
Available approaches for Association Rule Mining (ARM) generates a large number of association rules, these rules may be trivial and redundant and also such rules are difficult to manage and understand for the user...