Finding banded patternsin large data set using segmentation

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Towards an agent-based framework for online after-sales services

The multi-agent paradigm for building intelligent systems has gradually been accepted by researchers and practitioners in the research field of artificial intelligence. There are also attempts of adapting agents and agent-based systems for creating industrial applications and providing e-services. In this paper, we present an attempt to use agents for constructing an online after-sale services system. The system is decomposed into four major cooperative agents, and in which each agent concentrates on particular aspects in the system and expresses intelligence by using various techniques. The proposed agent-based framework for the system is presented at both the micro-level and the macro-level according to the Gaia methodology. UML notations are also used to represent some software design models. As the result of this, agents are implemented into a framework for which exploits Case-Based Reasoning (CBR) technique to fulfil real life on-line services' diagnoses and tasks

Data mining for AMD screening: A classification based approach

This paper investigates the use of three alternative approaches to classifying retinal images. The novelty of these approaches is that they are not founded on individual lesion segmentation for feature generation, instead use encodings focused on the entire image. Three different mechanisms for encoding retinal image data were considered: (i) time series, (ii) tabular and (iii) tree based representations. For the evaluation two publically available, retinal fundus image data sets were used. The evaluation was conducted in the context of Age-related Macular Degeneration (AMD) screening and according to statistical significance tests. Excellent results were produced: Sensitivity, specificity and accuracy rates of 99% and over were recorded, while the tree based approach has the best performance with a sensitivity of 99.5%. Further evaluation indicated that the results were statistically significant. The excellent results indicated that these classification systems are ideally suited to large scale AMD screening processes