Encog: Library of Interchangeable Machine Learning Models for Java and C#

This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org

Un Sistema per la Valutazione di Proprietà Molecolari Basato su Reti Neurali per Grafi

Sviluppo di una libreria per il learning di modelli contestuali NN4G volta alla creazione di uno strumento di supporto alla cheminformatics per la valutazione di risultati predittivi in ambito tossicologic

Comparative study of connectionist simulators

This paper presents practical experiences and results we obtained while working with simulators for artificial neural network, i.e. a comparison of the simulators\u27 functionality and performance is described. The selected simulators are free of charge for research and education. The simulators in test were: (a) PlaNet, Version 5.6 from the University of Colorado at Boulder, USA, (b) Pygmalion, Version 2.0, from the Computer Science Department of the University College London, Great Britain, (c) the Rochester Connectionist Simulator (RCS), Version 4.2 from the University of Rochester, NY, USA and (d) the SNNS (Stuttgart Neural Net Simulator), Versions 1.3 and 2.0 from the University of Stuttgart, Germany. The functionality test focusses on special features concerning the establishment and training of connectionist networks as well as facilities of their application. By exemplarily evaluating the simulators\u27 performance, we attempted to establish one and the same type of back-propagation network for optical character recognition (OCR). A respective quality statement is made by comparing the number of cycles needed for training and the recognition rate of the individual simulators

An empirical investigation of neural networks, evolution strategies, and evolutionary trained neural networks and their application to some chemical engineering problems

Evolutionary algorithms and neural networks have been successfully used to solve difficult problems in various domains. Researchers and practitioners have applied them as single paradigms or in combination with each other. Here the utility of CI methods in Chemical Engineering is investigated. The performance of neural networks and evolutionary algorithms and combinations of them on real engineering problems is shown. An encoding of chemical compounds is proposed that allows the application of both paradigms and establishes a basis for comparisons. Solutions found by CI methods are presented that compare to the best physically motivated methods known so far and even outperform them in several ways. During the design process of chemical plants the knowledge how chemicals react with each other ist very important. For this reason there is a need for calculation methods which are able to predict thermodynamic properties. In this work, properties under consideration concern either pure components where the heat of vaporization has to be predicted or mixtures where the heat of mixing should be predicted

Delineamento de experimentos no treinamento de redes neurais artificiais para o problema de previsão de séries temporais não lineares

A time series is defined as a collection of observations of a variable over time, whose data order has a fundamental importance due to the dependence between these consecutive values. The analysis of these data, and the understanding of this correlation, is an important tool in understanding phenomena in various sciences, such as Economics, Engineering and Operations Management, where prices, demands and values are these variables. The modeling of this data sequence provides its use in order to, based on historical data, make predictions for future periods. This consecutive relationship can be considered complex and, not uncommonly, non-linear. The use of Artificial Neural Networks has proven increasingly effective in establishing pattern recognition, modeling and predicting future values. The statistical programs available on the market provide user-friendly tools and results demonstrated in several scientific available in publications, but the number of factors and levels that are available for use during the training of Artificial Neural Networks, which may indicate the need for hundreds of years to execute every possible combination. In this study, the statistical methodology of Design of Experiments (DOE) is applied in order to determine the best parameters of an Artificial Neural Network for the prediction of non-linear time series and, thus, significantly reduce the time needed to point out the choice of the best Artificial Neural Network capable of solving our prediction problem. Instead of using the most common technique for training an Artificial Neural Network, that is, the empirical method, DOE is proposed to be the best methodology. The main motivation for this dissertation was the prediction of non-linear seasonal time series - which is related to many real problems, such as short-term electrical load, daily prices and returns, water consumption, etc. A case study is presented. The objective was fulfilled when it was proved to reach error results, between prediction and real value, smaller for the Artificial Neural Network than the error reached with the model.Uma série temporal é definida como uma coleção de observações de uma variável ao longo do tempo, cuja ordem dos dados é de fundamental importância devido a dependência entre estes valores consecutivos. A análise destes dados, e o entendimento desta correlação, é um importante instrumento no entendimento de fenômenos em diversas ciências, como Economia, Engenharias e Gestão de Operações, onde preços, demandas e valores são estas variáveis. A modelagem desta sequência de dados proporciona a sua utilização no objetivo de, com base nos dados históricos, realizar previsões para períodos futuros. Esta relação consecutiva pode ser considerada complexa e, não incomum, não lineares. O uso de Redes Neurais Artificiais tem se provado cada vez mais eficaz em estabelecer reconhecimento de padrões, modelagem e a previsão de valores futuros. Os programas estatísticos disponíveis no mercado disponibilizam ferramentas de uso amigável e de resultados demonstrados em diversos científicos disponíveis em publicações, porém o número de fatores e níveis que são disponibilizados para utilização durante o treinamento das Redes Neurais Artificiais, o que pode nos apontar a necessidade de centenas de anos para executarmos todas as combinações possíveis. Neste estudo a metodologia estatística de Delineamento de Experimentos (Design of Experiments - DOE) é aplicada com o propósito de determinar os melhores parâmetros de uma Rede Neural Artificial para a previsão de séries temporais não lineares e, assim, reduzir significativamente o tempo necessário para se apontar a escolha da melhor Rede Neural Artificial capaz de resolver nosso problema de previsão. Ao invés de utilizarmos a técnica mais comum de treinamento de uma Rede Neural Artificial, ou seja, o método empírico, o DOE é proposto para ser a melhor metodologia. A principal motivação para esta dissertação foi a previsão de séries temporais sazonais não lineares - que está relacionada com muitos problemas reais, tais como carga elétrica de curto prazo, preços diários e retornos, consumo de água, etc. Um estudo de caso é apresentado. O objetivo foi cumprido quando se comprovou atingir resultados de erros, entre previsão e valor real, menores para a Rede Neural Artificial do que o erro alcançado com o modelo

Use of artificial neural networks for emergencies prediction at Hospital Universitari de Girona Dr. Josep Trueta

The motivation of the project is to model and predict the volume of arrivals at the emergency department (ED) of a general hospital. The process consists of complex linear and nonlinear patterns together. Those types of temporal series are tough to solve efficiently using Box-Jenkins methods (ARIMA models) due its high stochastic behaviour and nonlinearity. Once the time series analysis is discarded owing the bad results obtained, and in order to change the approach of the task, artificial neural networks (ANN) are chosen to solve the problem. This methodology offers a whole new perspective of study, enabling the use of algorithms in a high tight time constraint in order to predict intraday information such as the arrivals expected to occur in the afternoon using morning information. The objective is to program a plain applicative, able to extract the data needed (endogenous and exogenous variables), compute the ANN algorithm and finally show the relevant results in order to help improving the human and material resources management in the area of emergencies. As a fundamental part of the project, the best methodology to work with ANN algorithms is seek in order to settle an accurate approach for future studies

Performance Modelling, and Adaptive Control for Linked Sequential Systems

This thesis investigates the dynamics of linked sequential systems of machines in industrial laundries. Two aspects are considered: firstly the control of such systems and in particular the decision making point when a batch to be processed can be sent to one of many identical machines, and secondly the modelling of the whole system of linked machines. The decision making point in the control of these systems is frequently implemented in a sub-optimal manner, or a manner which becomes sub-optimal as conditions change. An adaptive system is preferable and an Evolutionary Artificial Neural Network approach (EANN) is proposed. The EANN is tested on simulations of real laundry systems and shown to be effective. Then it is applied to two abstract game playing problems in order to better understand its limitations. Limitations are found to include the fact that if learning does not appear to take place, it is not possible to determine if this is a failure of the Evolutionary approach or the Artificial Neural Network parameters. The dynamics and performance of Linked Sequential Systems in Industrial Laundries are not well understood or covered by theory in the literature. The theory of the performance of these systems is outlined, and an Agent Based Model (ABM) simulation presented. The ABM simulation is explained and then the simulation is compared to a real world system in an existing laundry. The performance of the existing system is measured and compared to the prediction of the ABM simulation. The ABM simulation is shown to offer a better understanding of the system than the previous static calculation. Finally the ABM is used in a design exercise to show how it could be used to specify a system more accurately than the static calculation at design stage

Landslide susceptibility mapping using machine learning: A literature survey

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.Web of Science1413art. no. 302