University of the Aegean

About: University of the Aegean is a education organization based out in Mytilene, Greece. It is known for research contribution in the topics: Population & Context (language use). The organization has 2818 authors who have published 8100 publications receiving 179275 citations. The organization is also known as: UAEG.

Region of Interest Coding Techniques for Medical Image Compression

Abstract: We have provided an overview of state-of-the-art ROI coding techniques applied to medical images. These techniques are classified according to the image type they apply to; thus the first class includes ROI coding schemes developed for two-dimensional (2-D) still medical images whereas the second class consists of ROI coding in the case of volumetric images. In the third class, a prototype ROI encoder for compression of angiogram video sequences is presented. ROI coding preserves image quality in diagnostically critical regions by performing advanced image compression, enabling better image examination and addressing issues regarding image handling and transmission in telemedicine systems. The mapping of the ROI from the spatial domain to the wavelet domain is dependent on the used wavelet filters and it is simplified for rectangular and circular regions. Therefore, ROI coding is considered quite important in distributed and networked electronic healthcare.

Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

Abstract: The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.

Bayesian Modelling of Outstanding Liabilities Incorporating Claim Count Uncertainty

Abstract: This paper deals with the prediction of the amount of outstanding automobile claims that an insurance company will pay in the near future We consider various competing models using Bayesian theory and Markov chain Monte Carlo methods Claim counts are used to add a further hierarchical stage in the model with log-normally distributed claim amounts and its corresponding state space version This way, we incorporate information from both the outstanding claim amounts and counts data resulting in new model formulations Implementation details and illustrations with real insurance data are provided

Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS

Abstract: The spatial properties of gaps have an important influence upon the regeneration dynamics and species composition of forests. However, such properties can be difficult to quantify over large spatial areas using field measurements. This research considers how we conceptualize and define forest canopy gaps from a remote sensing point of view and highlights the inadequacies of passive optical remotely sensed data for delineating gaps. The study employs the analytical functions of a geographical information system to extract gap spatial characteristics from imagery acquired by an active remote sensing device, an airborne light detection and ranging instrument (LiDAR). These techniques were applied to an area of semi-natural broadleaved deciduous forest, in order to map gap size, shape complexity, vegetation height diversity and gap connectivity. A vegetation cover map derived from imagery from an airborne multispectral scanner was used in combination with the LiDAR data to characterize the dominant vegetation...