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This paper analyzes the relationships among eight ancient scripts from between Greece and India. We used convolutional neural networks combined with support vector machines to give a numerical rating of the similarity between pairs of signs (one sign from each of two different scripts). Two scripts that had a one-to-one matching of their signs were determined to be related. The result of the analysis is the finding of the following three groups, which are listed in chronological order: (1) Sumerian pictograms, the Indus Valley script, and the proto-Elamite script; (2) Cretan hieroglyphs and Linear B; and (3) the Phoenician, Greek, and Brahmi alphabets. Based on their geographic locations and times of appearance, Group (1) may originate from Mesopotamia in the early Bronze Age, Group (2) may originate from Europe in the middle Bronze Age, and Group (3) may originate from the Sinai Peninsula in the late Bronze Age. [ABSTRACT FROM AUTHOR]

This paper looks into the Greek-Turkish arms race a decade after an earlier contribution to the issue that relied heavily on artificial neural networks. The time period between the two papers contributes to the reliability of the results derived, not just by increasing the number of observations, but also mainly by incorporating the progress made in the realm of artificial intelligence. The focus on the case of both countries unlike the paper mentioned above that dealt with just the Greek side provides ample room for comparative purposes regarding the determinants of defense expenditure on both sides. The results derived in terms of input significance estimation support the findings of the earlier research as indicated above, pointing to the leading role of the demographic preponderance of Turkey over Greece. The paper also points to the fact that 10 years later, Turkey continues to set the arms race rules against its rival by determining the defense expenditure of Greece, whereas the role of the latter in affecting the military spending of Turkey is non-existent. [ABSTRACT FROM AUTHOR]

This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology for the analysis, prediction and interpretation of urban growth. The proposed urban model takes into account the changes over time in population and building use patterns. A GIS is used for handling the spatial and temporal data, performing contingency analysis and mapping the results. Spatial entities with similar characteristics are grouped together in clusters by the use of a fuzzy c-means algorithm. Each cluster represents a specific level of urban growth and development. A two-layer feed-forward multilayer perceptron artificial neural network is then used to predict urban growth. The model, applied to the prefecture of Attica, Greece, delineates the current and future evolution trends of the Athens metropolitan area, which are illustrated by maps of the urban growth dynamics. The proposed methodology aims to assist planners and decision makers in gaining insight into the transition from rural to urban. [Copyright &y& Elsevier]

Abstract: Missing data are omnipresent in forestry research, and this poses problems in the analysis of primary data. Many statistical problems have been viewed as missing data problems. To cope with incomplete data, several methods are currently being used. They are all based on assumptions some of which might not be valid in a particular case. The choice mostly depends on the objective of the study. Considerable mensuration research is motivated by the need for yield projections that can support forest management decisions. This paper is focused on a new approach for filling gaps in diameter measurements on standing tree boles. Dealing with this problem, an attempt was made to examine the applicability of artificial neural network models for missing data estimation and to use the estimated values in the subsequent analysis. The procedure that should be followed in the development of such models is outlined. The results show good performance of the examined ANN models compared to regression treatments for missing data and ANN models demonstrate their adequacy and potential for filling gaps in diameter measurements on standing tree boles. The ANN models applied in this study are sufficiently general and have great potential to be applicable for estimating the missing values of many variables in environmental applications. [Copyright &y& Elsevier]

• The system aims to minimize the gas consumption of domestic boilers for heating. • Neural Networks and a Genetic Algorithm are used for the optimal boiler operation. • Target is to find the optimal control instructions to be sent to the boiler. • The model leads mostly to lower boiler modulation, namely lower water temperatures. • Significant gas consumption reduction attained with the operation of the system. This paper presents a novel approach that aims to minimize the natural gas consumption of domestic boilers for heating purposes, while not compromising the user's heating needs. The system utilizes gas consumption data, the conditions both inside and outside the house collected via interconnected commercial thermostats, and the heating needs of the users. The architecture of the presented system is divided in two main coordinated processes: (a) the first one consists of two Neural Networks with Convolutional and Long Short-Term Memory layers, used to predict the indoor temperature and the boiler's modulation (load percentage), whereas (b) the second process includes a Genetic Algorithm used to determine the optimal operation conditions of the boiler, by finding the boiler control instructions that meet the user's heating preferences concerning the target indoor temperature, while minimizing the total gas consumption. One main advantage of the solution is its ability to consider boilers as a black box, since it does not need to be aware of the internal mechanics. In this way, the proposed methodology can be applied to a wide range of domestic gas boilers with minimum adjustments. The overall methodology is applied to four domestic boilers in Greece spanning three cities, to capture different climatic conditions and evaluate the system performance in varying outdoor conditions. The attained results indicate that the proposed system can lead to significant gas consumption reduction through autonomously created optimal control instructions provided to the boiler. [ABSTRACT FROM AUTHOR]

Modeling the elasticity modulus of unbound granular pavement materials has attracted significant research interest because of its importance in pavement design particularly in PPP/BOT projects. These efforts have been hampered by three factors: (i) inability to capture the correlations between the asphalt and granular layers and the subgrade, (ii) inadequate modeling of the effects of external factors on the elasticity modulus of unbound materials, and (iii) widespread use of linear statistical relationships to model a complex and non-linear phenomenon. In this paper genetically optimized neural networks and falling weight deflectometer (FWD) back-analysis results from a newly constructed BOT project in Athens, Greece, are employed in order to evaluate pavement section design parameters. It is shown that parameter values adopted during design do not co-inside with those observed from the back-analysis studies. Further, the results indicate that the relative estimation error for the modulus of elasticity of the unbound material does not exceed 25%, while the correlation between actual and predicted values is 86%, both suggesting that the proposed approach models the physical phenomenon adequately, a finding with important practical implications particularly in PPP projects. [ABSTRACT FROM AUTHOR]