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 & Tourism. The organization has 2818 authors who have published 8100 publications receiving 179275 citations. The organization is also known as: UAEG.

Radial Basis Function Neural Networks Classification for the Recognition of Idiopathic Pulmonary Fibrosis in Microscopic Images

Abstract: This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of biological microscopic images displaying lung tissue sections with idiopathic pulmonary fibrosis. For the development of the RBF classifiers, the fuzzy means clustering algorithm is utilized. This method is based on a fuzzy partition of the input space and requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied in lung sections acquired using a microscope and captured by a digital camera, at a magnification of 4times. Age-and sex-matched, 6-to 8-week-old mice (five for each time point and five as control) were used for the induction of pulmonary fibrosis (cf. bleomycin). Bleomycin administration initially induces lung inflammation that is followed by a progressive destruction of the normal lung architecture. The captured images correspond to 7,15, and 23 days after bleomycin or saline injection and bronchoalveolar lavage (BAL) has been performed to the mice sample. The images were analyzed and color features were extracted. A support vector machines (SVMs)-based classifier was also employed for the same problem. The resulting scores derived by visual assessment of the images by expert pathologists were compared with the RBF and SVM classification outcome. Overall, the RBF neural network had a slightly better performance than that of the SVM classifier, but both performed very well, matching to a great percentage the scoring of the experts. There are some erroneous predictions of the algorithm for the regions characterized as "ill" regions (i.e., some bronchia were wrongly classified as fibrotic areas); however, in general, the algorithm worked pretty fine in distinguishing pathologic from normal in most cases and for heterogeneous fibrotic foci, achieving high values in terms of specificity and sensitivity.

Evaluating Second Generation Open Government Data Infrastructures Using Value Models

Abstract: Recently, a second generation of advanced open government data (OGD) infrastructures has emerged, influenced by the principles of the Web 2.0 paradigm, and oriented towards the elimination of the clear distinction between providers and consumers of such data, and the support of data 'pro-sumers'. This paper presents and validates a methodology for evaluating these advanced second generation of ODG infrastructures, which is based on the estimation of value models of them from users' ratings. This value model includes assessments of the various types of value generated by such an infrastructure, and also of the relations among them as well. This enables a deeper understanding of the whole value generation mechanism and a rational definition of improvement priorities. The proposed methodology has been used for the evaluation of an advanced second generation ODG e-Infrastructure developed in the European project ENGAGE.

Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks

Abstract: We introduce for the first time the utilization of Long short-term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C-band or O-band transmission systems for single channel and multi-channel 16-QAM modulation format with polarization multiplexing. A detailed analysis regarding the effect of the number of hidden units and the length of the word of symbols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be very efficient as post processors of optical receivers which classify data that have undergone non-linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi-channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase, however LSTM can be less complex than Digital Back Propagation in long distances (>1000 km).

Customer satisfaction and loyalty in the eyes of new and repeat customers: Evidence from the transport sector

Abstract: Purpose – The purpose of this paper is to attempt to explore the relative importance of the physical and interactive elements of service on overall satisfaction, particularly when these elements are moderated by the point‐of‐view of repeat and new customers Evidence is drawn from the transport sector industryDesign/methodology/approach – The data for this study come from 388 ferry passengers Regression analysis was used to test the influence of each parameter and SEM employed to assess the moderating effects of repeat patronage on satisfactionFindings – The results suggest that the physical elements of the service are of greater importance in determining customer evaluations on overall satisfaction than interactive features of service The results also suggest that these effects are not just direct but also moderated by the repeat use of the service Finally, both elements are very good predictors of overall satisfactionResearch limitations/implications – As results are obtained from only one industr