PoK

Author:Klaus Dieter Althoff, Ralph Bergmann, Stefan Wess, Michel Manago, Eric Auriol, Oleg. I. Larichev, Alexander Bolotov, Yurii I. Zhuravlev, Serge I. Gurov

Description:
We describe an approach for developing knowledge based medical decision support systems based on the rather new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca project which particularly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis, as it was available among the partners, and ‘scale-up’ to more general non-technical decision support tasks as typically given in medical domains. Inreca technology is used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases that are caused by psychotropes

Author:Praveen Pathak, Michael Gordon, Weiguo Fan

Description:
Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. This paper addresses the issue of improving retrieval performance (in terms of precision and recall) for retrieval from document collections.There are three important paradigms of research in the area of information retrieval (IR): Probabilistic IR, Knowledge-based IR, and, Artificial Intelligence based techniques like neural networks and symbolic learning. Very few researchers have tried to use evolutionary algorithms like genetic algorithms (GA’s). Previous attempts at using GA’s have concentrated on modifying document representations or modifying query representations. This work looks at the possibility of applying GA’s to dapt various matching functions. It is hoped that such an adaptation of the matching functions will lead to a better retrieval performance than that obtained by using a single matching function. An overall matching function is treated as a weighted combination of scores produced by individual matching functions. This overall score is used to rank and retrieve documents. Weights associated with individual functions are searched using Genetic Algorithm.

Author:Yan Zhai, Peng Ning, Purush Iyer, Douglas S. Reeves

Description:
This paper presents techniques to integrate and reason about complementary intrusion evidence such as alerts generated by intrusion detection systems (IDSs) and reports by system monitoring or vulnerability scanning tools. To facilitate the modeling of intrusion evidence, this paper classifies intrusion evidence into either event-based evidence or state-based evidence. Event-based evidence refers to observations (or detections) of intrusive actions (e.g., IDS alerts),while state-based evidence refers to observations of the effects of intrusions on system states. Based on the interdependency between event-based and state-based evidence, this paper develops techniques to automatically integrate complementary evidence into Bayesian networks, and reasonabout uncertain or unknown intrusion evidence based on verified evidence. The experimental results in this paper demonstrate the potential of the proposed techniques. In particular, additional observations by system monitoring or vulnerability scanning tools can potentially reduce the false alert rate and increase the confidence in alerts corresponding to successful attacks.

Author:Mohamed Marzouk, Osama Moselhi

Description:
This paper presents an application of simulation optimization in construction utilizing genetic algorithms. The paperfocuses on the use of genetic algorithms (GAs) as a tool for optimizing the total cost of earthmoving operations accounting for available equipment models to contractors and their corresponding quantities. The developed genetic algorithm has a powerful computational utility that increases its efficiency. The fitness of generated chromosomes is calculated utilizing a simulation engine dedicated for earthmoving operations which is dynamically linked to the developed genetic algorithm. The impact of the algorithm’s control parameters on its conversion is also examined. A numerical example is presented to illustrate the capabilities of the developed algorithm in selecting near-optimum fleet configurations.