Automatic Diagnosis With Efficient Medical Case Searching Based on Evolving Graphs

Efficient Centrality Monitoring for Time-Evolving Graphs

The goal of this work is to identify the nodes that have the smallest sum of distances to other nodes (the lowest closeness centrality nodes) in graphs that evolve over time. Previous approaches to this problem find the lowest centrality nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, ‘Is it possible to improve the...

Automatic diagnosis with genetic algorithms and case-based reasoning

Case Based Reasoning Technology for Medical Diagnosis

Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues a...

Graph Searching on Chordal Graphs

In the graph searching problem initially a graph with all edges contaminated is pre sented We would like to obtain a state of the graph in which all edges are simultaneously clear by a sequence of moves using searchers The objective is to achieve the desired state by using the least number of searchers Two variations of the graph searching problem are considered in this paper One is the edge se...

Fast-Mixed Searching on Graphs

We introduce the fast-mixed search model, which is a combination of the fast search model and the mixed search model. We establish relations between the fast-mixed search problem and other graph search problems. We also establish relations between the fast-mixed search problem and the inducedpath cover problem. We present linear-time algorithms for computing the fast-mixed search number and opt...