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This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island. On the other hand the evaluation of the potential torrential risk due to high volume of water flow in the winter season is also very important. Data (from 1965–1993) from 78 measuring stations located in the 70 distinct watersheds of Cyprus were used. This data volume was divided in the training subset comprising of 60 cases and in the testing subset containing 18 cases. The input parameters are the area of the watershed, the average annual and the average monthly rain-height, the altitude and the slope in the location of the measuring station. Consequently three structural and two dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one. [Copyright &y& Elsevier]

Abstract: Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model''s accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE=28.06%, RMSE=76.56, U-statistic=0.67 and R 2 =0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE=2.88%, RMSE=13.75, U-statistic=0.19 and R 2 =0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings. [Copyright &y& Elsevier]

Over the last few years, one major research direction of information retrieval includes user behaviour prediction. For that reason many models have been proposed aiming at the accurate evaluation of information retrieval systems and the best prediction of web search users׳ behaviour. In this paper we propose a new evaluation metric for information retrieval systems which employs two relevance factors; a relevance judgement grade and a popularity grade that represents users׳ vote for a document. We show that this new metric performs better than other evaluation metrics when expressing user behaviour. Moreover, in order to test the performance of our metric on different and scalable ranking algorithms, we develop a pairwise text similarity algorithm using cosine similarity, implemented on the MapReduce model, and then perform experiments on rankings generated by the algorithm. [ABSTRACT FROM AUTHOR]

• The paper compares and evaluates the capability of various ML methods to classify the seismic damage potential of R/C buildings adequately. • The paper proposes a holistic system that automates the selection and application of the most appropriate algorithmic hyperparameters. • A large training dataset of R/C buildings analyzed for 65 real earthquake records using nonlinear dynamic analyses. • The SVM- Gaussian Kernel algorithm produced the highest classification results. • An auto hyperparameter tuning method for the winner algorithm is proposed, so that the hyperparameters are automatically optimized utilizing BO. The research on the application of machine learning (ML) methods in the field of earthquake engineering shows a continuous and rapid progress in the last two decades. ML methods and models belonging to the category of supervised, unsupervised and semi-supervised learning are applied for the assessment of seismic vulnerability of structures and estimation of the expected level of seismic damage. These models lead to the classification of seismic damage into predefined classes through the extraction of patterns from data collected from various sources. However, the lack of detailed knowledge can affect their performance and ultimately reduce their reliability, as well as the generalizability that should characterize them. Towards this direction, the present paper attempts to compare and evaluate for the first time the ability of an extensive number of ML methods in the correct classification of R/C buildings at the first stage pre- and post-seismic inspection considering three seismic damage categories. A database consisting of 5850 training samples is used for this evaluation. This database is generated by solving 90 R/C buildings for 65 actual seismic excitations applying nonlinear time history analyses. For each one of the training samples the maximum interstory drift ratio is calculated as a damage index. In addition, a major contribution of this paper is the presentation and extensive documentation of the procedures required for the preprocessing of the data. Finally, an auto hyperparameter tuning method for the winning algorithm is proposed, so that the hyperparameters are automatically optimized utilizing Bayesian Optimization. The most significant conclusion extracted is that the studied ML algorithms extract very different classification results. In addition, the Support Vector Machine - Gaussian Kernel algorithm extracted the most accurate results of all the studied ML algorithms. [ABSTRACT FROM AUTHOR]

Music Information Research (MIR) requires access to real musical content in order to test the efficiency and effectiveness of its methods as well as to compare developed methodologies on common data. Existing datasets do not address the research direction of musical track popularity that has recently received considerate attention. Moreover, sources of musical popularity do not provide easily manageable data and no standardised dataset exists for musical popularity research. To address these issues the Track Popularity Dataset (TPD) was created in a previous work. TPD provided (a) different sources of popularity definition ranging from 2004 to 2014, (b) mapping between different track/ author/ album identification spaces allowing use of different popularity sources, (c) information on the remaining, non popular, tracks of an album with a popular track, (d) contextual similarity between tracks and (e) ready for MIR use extracted features for both popular and non-popular audio tracks. This paper extends the TPD by (a) adding more readily computed features, (b) proposing feature & similarity definitions on popularity trends, (c) formulating common data mining scenarios on tracks’ popularity and (d) presenting respective promising results. [ABSTRACT FROM AUTHOR]