International journal of engineering intelligent systems for electrical engineering and communications 

About: International journal of engineering intelligent systems for electrical engineering and communications is an academic journal. The journal publishes majorly in the area(s): Artificial neural network & Fuzzy logic. It has an ISSN identifier of 0969-1170. Over the lifetime, 152 publications have been published receiving 1235 citations.

The role of ontologies in data integration

Abstract: In this paper, we discuss the use of ontologies for data integration. We consider two different settings depending on the system architecture: central and peer-to-peer data integration. Within those settings, we discuss five different cases studies that illustrate the use of ontologies in metadata representation, in global conceptualization, in high-level querying, in declarative mediation, and in mapping support. Each case study is described in detail and accompanied by examples.

A fuzzy case-based system for weather prediction

Abstract: Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. We describe an investigation into the application of case-based reasoning in airport weather forecasting. Knowledge about temporal features that human forecasters use to construct analogous climatological scenarios is encoded in a fuzzy similarity measure. The fuzzy similarity measure is used to locate the k-nearest neighbours from the historical database. These nearest neighbours are in turn adapted to produce values for the forecast parameters. Five sets of experiments show inter alia that the proposed WIND-1 system produces highly accurate forecasts based on real climatological data, using a standard technique for assessing the accuracy of forecasts produced by human forecasters.

Reliable diagnosis of acute abdominal pain with conformal prediction

Abstract: Medical decision support is an area in which a lot of machine learning research has been conducted and several diagnostic and prognostic systems have been developed. The majority of these systems only produce bare predictions, without any indication of how reliable each of these predictions is. An indication of this kind however, is highly desirable especially in the medical field. In this paper we address this problem with the use of a recently developed technique, called conformal prediction, for accompanying the predictions of traditional machine learning algorithms with measures of their accuracy and reliability. We apply conformal prediction based on a Neural Network classifier to the problem of acute abdominal pain diagnosis and obtain predictions which have a high level of accuracy and are complemented with well-calibrated and practically useful confidence measures.