Predicting Wood Thermal Conductivity Using Artificial Neural Networks

An artificial neural network model that estimates wood thermal conductivity under a wide range of conditions of moisture content, temperature and apparent density was developed and tested with literature-obtained experimental data. The optimal network was determined to consist of an input layer, three hidden layers, and one output layer following the feed forward network structure and more specifically the back-propagation algorithm. Each of the three hidden layers of the ANN consisted of eleven neurons. The Neuralworks software package was used for the determination of the network structure and architecture, and for the training and testing phase. The evaluation produced an R2 value equal to 0.9994 and a RMS Error equal to 0.0123, thus proving that the developed ANN model is a reliable approach with powerful predictive capacity towards the estimation of thermal conductivity and it can be used by researchers under a wide range of conditions

Employing a radial-basis function artificial neural network to classify western and transition European economies based on the emissions of air pollutants and on their income

Abstract. This paper aims in comparing countries with different energy strategies, and demonstrate the close connection between environment and economic growth in the ex-Eastern countries, during their transition to market economies. We have developed a radial-basis function neural network system, which is trained to classify countries based on their emissions of carbon, sulphur and nitrogen oxides, and on their Gross National Income. We used three countries representative of ex-Eastern economies (Russia, Poland and Hungary) and three countries representative of Western economies (United States, France and United Kingdom). Results showed that the linkage between environmental pollution and economic growth has been maintained in exEastern countries

Transform-based graph topology similarity metrics

Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and supply chains, social media, computational aesthetics, and transportation networks. In this paradigm, signal processing methodologies are applied to the adjacency matrix, seen as a two-dimensional signal. Fundamental operations of this type include graph sampling, the graph Laplace transform, and graph spectrum estimation. In this context, topology similarity metrics allow meaningful and efficient comparisons between pairs of graphs or along evolving graph sequences. In turn, such metrics can be the algorithmic cornerstone of graph clustering schemes. Major advantages of relying on existing signal processing kernels include parallelism, scalability, and numerical stability. This work presents a scheme for training a tensor stack network to estimate the topological correlation coefficient between two graph adjacency matrices compressed with the two-dimensional discrete cosine transform, augmenting thus the indirect decompression with knowledge stored in the network. The results from three benchmark graph sequences are encouraging in terms of mean square error and complexity especially for graph sequences. An additional key point is the independence of the proposed method from the underlying domain semantics. This is primarily achieved by focusing on higher-order structural graph patterns

Fuzzy graphs: Algebraic structure and syntactic recognition

© Springer Science+Business Media Dordrecht 2013. Directed fuzzy hypergraphs are introduced as a generalization of both crisp directed hypergraphs and directed fuzzy graphs. It is proved that the set of all directed fuzzy hypergraphs can be structured into a magmoid with operations graph composition and disjoint union. In this framework a notion of syntactic recognition inside magmoids is defined. The corresponding class is proved to be closed under boolean operations and inverse mor-phisms of magmoids. Moreover, the language of all strongly connected fuzzy graphs and the language that consists of all fuzzy graphs that have at least one directed path from the begin node to the end node through edges with membership grade 1 are recognizable. Additionally, a useful characterization of recognizability through left derivatives is also achieved

Calculation of Sediment Transport Capacity of Flowing Water in Rivers with Machine Learning

Sediment Transport and Morphodynamic

E-Learning in the work-places in the Rural Sector of northeastern Greece

Internet based applications and in particular e-learning ones have proved very successful when applied to training diverse groups in small and disparate communities. This paper discusses the potential of e-learning methods in training in the rural sector of northeastern Greece. A survey was carried out amongst Greek rural communities in the region of Eastern Macedonia and Thrace during the autumn of 2003. The results of the survey have been analyzed and discussed with two axes of focus in mind: Establishing which areas of learning would be the most immediately acceptable for use in an e-learning application of training within the farming industry and to ascertain the extent to which e-learning has already been adopted within the rural areas of northeastern Greec

An explanation-based approach for experiment reproducibility in recommender systems

© 2019, Springer-Verlag London Ltd., part of Springer Nature. The offline evaluation of recommender systems is typically based on accuracy metrics such as the Mean Absolute Error and the Root Mean Squared Error for error rating prediction and Precision and Recall for measuring the quality of the top-N recommendations. However, it is difficult to reproduce the results since there are various libraries that can be used for running experiments and also within the same library there are many different settings that if not taken into consideration when replicating the results might vary. In this paper, we show that within the use of the same library an explanation-based approach can be used to assist in the reproducibility of experiments. Our proposed approach has been experimentally evaluated using a wide range of recommendation algorithms ranging from collaborative filtering to complicated fuzzy recommendation approaches that can solve the filter bubble problem, a real dataset, and the results show that it is both practical and effective

Can e-Government Systems Bridge the Digital Divide?

Electronic Government systems are often seen as panacea in the remedy of all failings of governance. With a history span of almost two decades, e-government implementations have often reached dead ends and have regularly failed to deliver the promise that the governments that have initiated them have made to their citizens. Despite an abundance of development models and best case scenarios identified in literature, e-government services are continually failing to attract the citizens and to capture their trust and faith. The main reason quoted for such failures is the lack of innovation and inclusivity in the way a service is designed and delivered. The digital divide is the major risk of marginalizing sectors of society or even whole continents due to lack of access to web based services. In the developing world it is mainly the lack of, or poor infrastructure that maintains and often widens the divide, while in the developed world it is lack of skills and difficulty of accessing services that leads citizens to abandon their efforts in using services online. Whatever the reason that leads to non-access of services the effect is similar and those citizens that fall victim to it are increasingly consumed into the trap of the digital divide. Efforts and initiatives to address the divide have primarily focused on building the infrastructure and providing access to the web. However, the quality and accessibility of online services is quite often then reason why citizens distance themselves from web-based services and the internet in total. This paper attempts to explore the shortfall in criteria for evaluating a government’s efforts in planning, implementing and delivering services that address the operational requirements of efficient government, but equally cater for the needs of the citizens as end users of the service

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ