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Earthquakes are hazardous natural disasters, and they may cause severe damage and losses where they occur. In addition to their devastating effects, they may trigger following disasters like tsunamis and fires. Post-earthquake fires are known as the most dangerous secondary disasters and generally cause much more damage than the damage caused by the earthquake itself. The difficulty in determining and responding to ignition sources, the lack of equipment and workforce, and obstacles like collapsed buildings that block the ways to reach fires may cause catastrophic disasters after an earthquake. In recent years, Unmanned Aerial Vehicle technologies (UAVs) have shown promising performance in post-disaster response operations. Parallel to technological improvements, they have been used for many purposes, like fire-fighting, victim location detection, base station support, and material distribution in disaster areas. To manage a possible response and improve the performance of UAVs in post-earthquake fire areas, it is crucial to be prepared in advance. This study proposes an artificial neural network-based clustering approach for unmanned aerial vehicle use in post-earthquake fire areas. After conducting a detailed literature review covering post-earthquake fires, usage of UAVs in disasters, and some aspects of Self Organizing Maps, the methodology used for clustering the neighborhoods regarding their post-earthquake fire risk similarities is introduced. A real-life application is carried out to identify and cluster the regions and provide preliminary information to the decision-makers on possible interventions. Neighborhoods of Tuzla district, one of the riskiest districts in terms of post-earthquake fires in Istanbul, are clustered with Self-Organizing Maps (SOM). In a possible post-earthquake fire disaster, the Tuzla district can be divided into three areas, and UAVs can be organized more efficiently and quickly based on this cluster information. The results of this real-life application can guide decision-makers by showing which regions have similarities for UAV response in possible post-earthquake fires and where they can be intervened together. The authorities can benefit from the findings of this study while preparing disaster plans, intervention actions, and post-disaster humanitarian activities. [ABSTRACT FROM AUTHOR]

In the past years, high entropy alloys (HEAs) witnessed great interest because of their superior properties. Phase prediction using machine learning (ML) methods was one of the main research themes in HEAs in the past three years. Although various ML-based phase prediction works exhibited high accuracy, only a few studied the variables that drive the phase formation in HEAs. Those (the previously mentioned work) did that by incorporating domain knowledge in the feature engineering part of the ML framework. In this work, we tackle this problem from a different direction by predicting the phase of HEAs, based only on the concentration of the alloy constituent elements. Then, pruned tree models and linear correlation are used to develop simple primitive prediction rules that are used with self-organizing maps (SOMs) and constructed Euclidean spaces to formulate the problem of discovering the phase formation drivers as an optimization problem. In addition, genetic algorithm (GA) optimization results reveal that the phase formation is affected by the electron affinity, molar volume, and resistivity of the constituent elements. Moreover, one of the primitive prediction rules reveals that the FCC phase formation in the AlCoCrFeNiTiCu family of high entropy alloys can be predicted with 87% accuracy by only knowing the concentration of Al and Cu.

Self Organizing Maps (SOM) are unsupervised neural networks suited for visualisation purposes and clustering analysis. This study uses SOM to solve a software engineering problem: detecting the most important (key) classes from software projects. Key classes are meant to link the most valuable concepts of a software system and in general these are found in the solution documentation. UML models created in the design phase become deprecated in time and tend to be a source of confusion for large legacy software. Therefore, developers try to reconstruct class diagrams from the source code using reverse engineering. However, the resulting diagram is often very cluttered and difficult to understand. There is an interest for automatic tools for building concise class diagrams, but the machine learning possibilities are not fully explored at the moment. This paper proposes two possible algorithms to transform SOM in a classification algorithm to solve this task, which involves separating the important classes - that should be on the diagrams - from the others, less important ones. Moreover, SOM is a reliable visualization tool which able to provide an insight about the structure of the analysed projects. [ABSTRACT FROM AUTHOR]

The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33) B (25) C (24) and D (15). The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP), through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN) Self Organizing MAP (SOM) in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

This study focuses on the best possible way forward in utilizing inconclusive molecules of PubChem bioassays AID 1332, AID 434987 and AID 434955, which are related to beta-lactamase inhibitors of Mycobacterium tuberculosis (Mtb). The inadequacy in the experimental methods that were observed during the invitro screening resulted in an inconclusive dataset. This could be due to certain moieties present within the molecules. In order to reconsider such molecules, insilico methods can be suggested in place of invitro methods For instance, datamining and medicinal chemistry methods: have been adopted to prioritise the inconclusive dataset into active or inactive molecules. These include the Random Forest algorithm for dataminning, Lilly MedChem rules for virtually screening out the promiscuity, and Self Organizing Maps (SOM) for clustering the active molecules and enlisting them for repositioning through the use of artificial neural networks. These repositioned molecules could then be prioritized for downstream drug discovery analysis. [ABSTRACT FROM AUTHOR]

Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%. [ABSTRACT FROM AUTHOR]

In this paper a particular emphasis is given on developing a character recognition system using SciLab, a free and open source computing software and is most promising alternative to MatLab. In the proposed work a character recognition system to extract printed text from an image is developed using Kohenen self organizing maps (SOM) based retrieval system. SOM being an unsupervised method of training has a superior feature extracting property. Samples of same characters which are oriented at same angle but with different size, color and fonts are used. After calculation of certain topological and geometrical properties of a character it is classified and recognized. With self organizing map together with K means clustering algorithm using SciLab software, the system has achieved a remarkable accuracy of 99% to 100%, when tested for various text input images. [ABSTRACT FROM AUTHOR]

The main goal of this study is a comparison of two different methods of pattern recognition. The first, Principal Component Analysis (PCA), is a method frequently used in climatology. The second, Self Organizing Maps (SOM), is a relatively new and efficient method based on Artificial Neural Networks (ANN). In order to compare the two methodologies, two teleconnection patterns were chosen, the North Atlantic Oscillation (NAO), which mostly affects the climate of Western Europe, and the North Sea–Caspian Pattern (NCP), mainly affecting eastern Mediterranean and the Balkan Peninsula. The teleconnection patterns are studied for winter 500 hPa geopotential height anomalies over the broader Europe area. The secondary objective of the study is to evaluate the ECHAM5/MPI General Circulation Model (GCM) in representing the two teleconnection patterns for a reference (1971–2000) and a future (2071–2100) period. The evaluation of the reference period is done comparing the simulated data to the NCEP/NCAR reanalysis data. The future period is studied to examine whether the current dominant circulation patterns change or not during the last 30 years of the 21st century. According to the results, both PCA and SOM methodologies capture the main variability mode over the study area, represented by the NAO pattern, but SOM is capable of capturing even less pronounced patterns, such as the NCP. In the future simulations, the atmospheric circulation during winter seems to be more pronounced with stronger NAO and NCP teleconnection patterns. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity of wind, the accurate wind speed prediction plays a major role in renewable energy systems. To increase the accuracy of wind speed prediction, a hybrid computing model is proposed. The proposed model is tested on real time wind data. The objective is to predict accurate wind speed based on proposed hybrid computing model which integrates Self Organizing feature Maps and Multilayer Perceptron network. The key advantages include higher accuracy, precision and minimal error. The results are computed by the training and testing methodologies. The experimental result shows that as compared to the conventional neural network models, the proposed hybrid model performs better in terms of minimization of errors. [Copyright &y& Elsevier]

Abstract: Kohonen maps and Counterpropagation Neural Networks are two of the most popular learning strategies based on Artificial Neural Networks. Kohonen Maps (or Self Organizing Maps) are basically self-organizing systems which are capable to solve the unsupervised rather than the supervised problems, while Counterpropagation Artificial Neural Networks are very similar to Kohonen maps, but an output layer is added to the Kohonen layer in order to handle supervised modelling. Recently, the modifications of Counterpropagation Artificial Neural Networks allowed introducing new supervised neural network strategies, such as Supervised Kohonen Networks and XY-fused Networks. In this paper, the Kohonen and CP-ANN toolbox for MATLAB is described. This is a collection of modules for calculating Kohonen maps and derived methods for supervised classification, such as Counterpropagation Artificial Neural Networks, Supervised Kohonen Networks and XY-fused Networks. The toolbox comprises a graphical user interface (GUI), which allows the calculation in an easy-to-use graphical environment. It aims to be useful for both beginners and advanced users of MATLAB. The use of the toolbox is discussed here with an appropriate practical example. [Copyright &y& Elsevier]

Abstract: The regional dynamics of industrial fisheries within Large Marine Ecosystems (LMEs) boundaries were investigated by means of a historical-descriptive approach. Landings data from the Sea Around Us Project database were used to detect trends in total yields and variations in landings composition by functional groups over time. The temporal and spatial scales covered by this study allowed general issues to be addressed such as the detection of recurrent patterns and synchronies in fisheries landings. An unsupervised artificial neural network, Self Organizing Map (SOM), is used as a tool to analyze fisheries landings composition variation over five decades in 51 LMEs all over the world. From the historical analysis of “fishing behaviors” within LMEs a broad distinction between two main types of fisheries emerged: (1) small and medium pelagics fisheries, with stable compositions or cyclic behaviors, occurred in LMEs which share common productive features, despite different geographical locations and (2) demersal fisheries, which are more affected by economic drivers and tend to concentrate in LMEs in the Northern Hemisphere. Our analysis can be regarded as a first step towards the challenging scope of describing the relative influence of environmental and economic drivers on exploited ecosystems. [Copyright &y& Elsevier]

Abstract: Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity. [Copyright &y& Elsevier]

Abstract: In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the ‘novelty’ of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated. [Copyright &y& Elsevier]

Abstract: The state-of-the-art text clustering methods suffer from the huge size of documents with high-dimensional features. In this paper, we studied fast SOM clustering technology for Text Information. Our focus is on how to enhance the efficiency of text clustering system whereas high clustering qualities are also kept. To achieve this goal, we separate the system into two stages: offline and online. In order to make text clustering system more efficient, feature extraction and semantic quantization are done offline. Although neurons are represented as numerical vectors in high-dimension space, documents are represented as collections of some important keywords, which is different from many related works, thus the requirement for both time and space in the offline stage can be alleviated. Based on this scenario, fast clustering techniques for online stage are proposed including how to project documents onto output layers in SOM, fast similarity computation method and the scheme of Incremental clustering technology for real-time processing, We tested the system using different datasets, the practical performance demonstrate that our approach has been shown to be much superior in clustering efficiency whereas the clustering quality are comparable to traditional methods. [Copyright &y& Elsevier]

Malaria is one of the principal causes of sickness and death in Nigeria, and puts a huge socioeconomic burden on the country. According to the Federal Ministry of Health, more than 50% of Nigerians suffer at least one bout of malaria per year. Irrespective of incessant efforts by government, a high level of control has not been achieved. This study discussed the application of Self Organizing Maps (SOMs) for prioritization of malaria endemic zones in Nigeria. 35 Public Health Centers (PHCs) and 15 government-owned health centers were randomly selected. Five malariometric parameters were considered, as these reflected the intensity of malaria transmission in the country. SOMs, a type of Artificial Neural Network (ANN) was used in this study for prioritization of malaria endemic zones in Nigeria. It yielded nine clusters based on neighborhood distance, which reflected on zones based on status of intensity of malaria epidemiology. The results obtained reinforced SOM’s efficiency than other methods formally used. SOM made targeting control measures at high-risk areas possible and greatly increased the cost efficiency of malaria control programs. [ABSTRACT FROM AUTHOR]

Abstract: A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models. [Copyright &y& Elsevier]

Abstract: Glaucoma, a leading cause of blindness worldwide, is a progressive optic neuropathy with characteristic structural changes in the optic nerve head and concomitant visual field defects. Ocular hypertension (i.e. elevated intraocular pressure without glaucoma) is the most important risk factor to develop glaucoma. Even though a number of variables, including various optic disc and visual field parameters, have been used in order to identify early glaucomatous damage, there is a need for computer-based methods that can detect early glaucomatous progression so that treatment to prevent further progression can be initiated. This paper is focused on the description of a system based on image processing and classification techniques for the estimation of quantitative parameters to define vessel deformation and the classification of image data into two classes: patients with ocular hypertension who develop glaucomatous damage and patients with ocular hypertension who remain stable. The proposed system consists of the retinal image preprocessing module for vessel central axis segmentation, the automatic retinal image registration module based on a novel application of self organizing maps (SOMs) to define automatic point correspondence, the retinal vessel attributes calculation module to select the vessel shape attributes and the data classification module, using an artificial neural network classifier, to perform the necessary subject classification. Implementation of the system to optic disc data from 127 subjects obtained by a fundus camera at regular intervals provided a classification rate of 87.5%, underscoring the value of the proposed system to assist in the detection of early glaucomatous change. [Copyright &y& Elsevier]

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models. [ABSTRACT FROM AUTHOR]

Abstract: Bayesian-regularized genetic neural networks (BRGNNs) were used to model the binding affinity (IC50) for 128 non-peptide antagonists for the human luteinizing hormone-releasing hormone (LHRH) receptor using 2D spatial autocorrelation vectors. As a preliminary step, a linear dependence was established by multiple linear regression (MLR) approach, selecting the relevant descriptors by genetic algorithm (GA) feature selection. The linear model showed to fit the training set (N =102) with R 2 =0.746, meanwhile BRGNN exhibited a higher value of R 2 =0.871. Beyond the improvement of training set fitting, the BRGNN model overcame the linear one by being able to describe 85% of test set (N =26) variance in comparison with 73% the MLR model. Our non-linear QSAR model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals the electronegativities, masses and polarizabilities as the most influencing atomic properties in the structures of the heterocycles under analysis for having an appropriate LHRH antagonistic activity. Furthermore, the ability of the non-linear selected variables for differentiating the data was evidenced when total data set was well distributed in a Kohonen self-organizing map (SOM). [Copyright &y& Elsevier]

Abstract: The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in ‘good’ and ‘bad’ risk categories. Self organizing maps (SOMs) exist since decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. This is mainly due to the unsupervised character of the SOM''s learning process. In this paper, the potential of SOMs for credit scoring is investigated. First, the powerful visualization capabilities of SOMs for exploratory data analysis are discussed. Afterwards, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Two different methods of integration are proposed. The first technique consists of improving the predictive power of individual neurons of the SOM with the aid of supervised classifiers. The second integration method is similar to a stacking model in which the output of a supervised classifier is entered as an input variable for the SOM. Classification accuracy of both approaches is benchmarked with results reported previously. [Copyright &y& Elsevier]

Abstract: Understanding relationships in high-dimension datasets requires proper data visualization. Two examples of high-dimension data are major-element geochemical and hyperspectral data. Major-element geochemical data consists of eleven oxide measurements for each sample. Well-known correlations exist for these types of data, i.e., the negative relationship between SiO2 and MgO; other more subtle relationships are rarely apparent. Hyperspectral data is by definition high-dimension data consisting of upwards of 100+ discrete measurements of the electromagnetic spectrum for a material. Hyperspectral data are a significant challenge to interpret when evaluating information for heterogeneous materials such as rocks. Self-organizing maps (SOMs) provide insight into complex relationships in high-dimension datasets while preserving the inherent topological relations and simultaneously producing a statistical model of the dataset. Another benefit of SOMs is their generation of composite vectors which can be analyzed to extract the relative importance of each component during classification. The veracity of SOMs is demonstrated using two datasets from the Spanish peaks intrusive complex of south-central Colorado including major-element geochemical and hyperspectral measurements. [Copyright &y& Elsevier]

A hierarchical neural-network-based approach for circuit tuning at the post-fabrication stage is proposed. In this approach,measurements that characterize the behavior of the circuit under test are first selected. The best candidates of circuit parameters for tuning are also determined. A training set comprising the selected circuit measurements is then constructed. These measurements are calculated during simulations in which the circuit parameter values are uniformly distributed in a tolerance region around their nominal values. The training set is fed to a self organizing map neural network to cluster the measurements. The generated clusters are manipulated and classified via a hierarchical circuit tuning procedure. Based on this classification, tuning values for the tuning parameters are calculated. Situations in which the circuit cannot be tuned are also addressed. Experimental results indicate that the developed approach provides a robust and efficient technique for circuit tuning. [ABSTRACT FROM AUTHOR]

Processes of enrichment, concentration and retention are thought to be important for the successful recruitment of small pelagic fish in upwelling areas, but are difficult to measure. In this study, a novel approach is used to examine the role of spatio-temporal oceanographic variability on recruitment success of the Northern Benguela sardine Sardinops sagax. This approach applies a neural network pattern recognition technique, called a self-organising map (SOM), to a seven-year time series of satellite-derived sea level data. The Northern Benguela is characterised by quasi-perennial upwelling of cold, nutrient-rich water and is influenced by intrusions of warm, nutrient-poor Angola Current water from the north. In this paper, these processes are categorised in terms of their influence on recruitment success through the key ocean triad mechanisms of enrichment, concentration and retention. Moderate upwelling is seen as favourable for recruitment, whereas strong upwelling, weak upwelling and Angola Current intrusion appear detrimental to recruitment success. The SOM was used to identify characteristic patterns from sea level difference data and these were interpreted with the aid of sea surface temperature data. We found that the major oceanographic processes of upwelling and Angola Current intrusion dominated these patterns, allowing them to be partitioned into those representing recruitment favourable conditions and those representing adverse conditions for recruitment. A marginally significant relationship was found between the index of sardine recruitment and the frequency of recruitment favourable conditions (r2 = 0.61, p = 0.068, n = 6). Because larvae are vulnerable to environmental influences for a period of at least 50 days after spawning, the SOM was then used to identify windows of persistent favourable conditions lasting longer than 50 days, termed recruitment favourable periods (RFPs). The occurrence of RFPs was compared with back-calculated spawning dates for each cohort. Finally, a comparison of RFPs with the time of spawning and the index of recruitment showed that in years where there were 50 or more days of favourable conditions following spawning, good recruitment followed (Mann-Whitney U-test: p = 0.064, n = 6). These results show the value of the SOM technique for describing spatio-temporal variability in oceanographic processes. Variability in these processes appears to be an important factor influencing recruitment in the Northern Benguela sardine, although the available data time series is currently too short to be conclusive. Nonetheless, the analysis of satellite data, using a neural network pattern-recognition approach, provides a useful framework for investigating fisheries recruitment problems. [Copyright &y& Elsevier]

[Display omitted] The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diazepam release from fused deposition modeling (FDM) printed tablets. Diazepam printed tablets of various shapes were created by a computer-aided design (CAD) program and prepared by fused deposition modeling using previously prepared polyvinyl alcohol/diazepam filaments via hot-melt extrusion. The surface to volume ratio (SA/V) for each shape was calculated. Printing parameters were varied including infill density (20%, 70% and 100%) and infill pattern (line and zigzag). Influence of tablet SA/V ratio and printing parameters (infill density and infill pattern) on the release of diazepam from printed tablets were modeled using self-organizing maps (SOM) and multi-layer perceptron (MLP). SOM as an unsupervised neural network was used for visualizing interrelation among the data, whereas MLP was used for the prediction of drug release properties. MLP had three layers (with structure 2-3-5) and was trained using back propagation algorithm. Input parameters for the modeling were: infill density and SA/V ratio; while output parameters were percent of drug release in five time points. The data set for network training was divided into training, validation and test sets. The dissolution rate increased with higher SA/V ratio, lower infill density (less than 50%) and zigzag infill pattern. The established ANN model was tested; calculated f 2 factors for two tested formulations (70.24 and 77.44) showed similarity between experimentally observed and predicted drug release profiles. Trained MLP network was able to predict drug release behavior as a function of infill density and SA/Vol ratio, as established design space for formulated 3D printed diazepam tablets. [ABSTRACT FROM AUTHOR]