Encyclopedia of Artificial Intelligence最新文献

{"title":"Solar Radiation Forecasting Model","authors":"F. Hocaoglu, Ö. N. Gerek, M. Kurban","doi":"10.4018/978-1-59904-849-9.CH210","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH210","url":null,"abstract":"The prediction of hourly solar radiation data has important consequences in many solar applications (Markvart, Fragaki & Ross, 2006). Such data can be regarded as a time series and its prediction depends on accurate modeling of the stochastic process. The computation of the conditional expectation, which is in general non-linear, requires the knowledge of the high order distribution of the samples. Using a finite data, such distributions can only be estimated or fit into a pre-set stochastic model. Methods like Auto-Regressive (AR) prediction, Fourier Analysis (Dorvlo, 2000) Markov chains (Jain & Lungu, 2002) (Muselli, Poggi, Notton & Louche, 2001) and ARMA model (Mellit, Benghanem, Hadj Arab, & Guessoum, 2005) for designing the non-linear signal predictors are examples to this approach. The neural network (NN) approach also provides a good to the problem by utilizing the inherent adaptive nature (Elminir, Azzam, Younes, 2007). Since NNs can be trained to predict results from examples, they are able to deal with non-linear problems. Once the training is complete, the predictor can be set to a fixed value for further prediction at high speed. A number of researchers have worked on prediction of global solar radiation data (Kaplanis, 2006) (Bulut & Buyukalaca, 2007). In these works, the data is treated in its raw form as a 1-D time series, therefore the inter-day dependencies are not exploited. This article introduces a new and simple approach for hourly solar radiation forecasting. First, the data are rendered in a matrix to form a 2-D image-like model. As a first attempt to test the 2-D model efficiency, optimal linear image prediction filters (Gonzalez, 2002) are constructed. In order to take into account the adaptive nature for complex and non-stationary time series, NNs are also applied to the forecasting problem and results are discussed. BACKGROUND","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IA Algorithm Acceleration Using GPUs","authors":"A. Seoane, A. Villanueva","doi":"10.4018/978-1-59904-849-9.CH129","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH129","url":null,"abstract":"Graphics Processing Units (GPUs) have been evolving very fast, turning into high performance programmable processors. Though GPUs have been designed to compute graphics algorithms, their power and flexibility makes them a very attractive platform for generalpurpose computing. In the last years they have been used to accelerate calculations in physics, computer vision, artificial intelligence, database operations, etc. (Owens, 2007). In this paper an approach to general purpose computing with GPUs is made, followed by a description of artificial intelligence algorithms based on Artificial Neural Networks (ANN) and Evolutionary Computation (EC) accelerated using GPU.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129583310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical Modelling of Highly Inflective Languages","authors":"M. Maučec, Z. Kacic","doi":"10.4018/978-1-59904-849-9.CH215","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH215","url":null,"abstract":"A language model is a description of language. Although grammar has been the prevalent tool in modelling language for a long time, interest has recently shifted towards statistical modelling. This chapter refers to speech recognition experiments, although statistical language models are applicable over a wide-range of applications: machine translation, information retrieval, etc. Statistical modelling attempts to estimate the frequency of word sequences. If a sequence of words is s = w1w2...wk, the probability can be expressed as:","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132865960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Classifier for Atrial Fibrillation (ECG)","authors":"O. Valenzuela, I. Rojas, F. Rojas, A. Guillén, L. Herrera, F. Rojas, Maria del Mar Cepero","doi":"10.4018/978-1-59904-849-9.CH134","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH134","url":null,"abstract":"This chapter is focused on the analysis and classification of arrhythmias. An arrhythmia is any cardiac pace that is not the typical sinusoidal one due to alterations in the formation and/or transportation of the impulses. In pathological conditions, the depolarization process can be initiated outside the sinoatrial (SA) node and several kinds of extra-systolic or ectopic beatings can appear. Besides, electrical impulses can be blocked, accelerated, deviated by alternate trajectories and can change its origin from one heart beat to the other, thus originating several types of blockings and anomalous connections. In both situations, changes in the signal morphology or in the duration of its waves and intervals can be produced on the ECG, as well as a lack of one of the waves. This work is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely on the differentiation of the types of atrial fibrillations. First of all we will study the ECG, and the treatment of the ECG in order to work with it, with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers that obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of Atrial Fibrillation (AF) that they showed, was realised. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction mentioned before. A new method based on evolutionary algorithms will be used to realise a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126778298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cluster Analysis of Gene Expression Data","authors":"Alan Wee-Chung Liew, N. Law, Hong Yan","doi":"10.4018/978-1-59904-849-9.CH045","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH045","url":null,"abstract":"Important insights into gene function can be gained by gene expression analysis. For example, some genes are turned on (expressed) or turned off (repressed) when there is a change in external conditions or stimuli. The expression of one gene is often regulated by the expression of other genes. A detail analysis of gene expression information will provide an understanding about the inter-networking of different genes and their functional roles. DNA microarray technology allows massively parallel, high throughput genome-wide profiling of gene expression in a single hybridization experiment [Lockhart & Winzeler, 2000]. It has been widely used in numerous studies over a broad range of biological disciplines, such as cancer classification (Armstrong et al., 2002), identification of genes relevant to a certain diagnosis or therapy (Muro et al., 2003), investigation of the mechanism of drug action and cancer prognosis (Kim et al., 2000; Duggan et al., 1999). Due to the large number of genes involved in microarray experiment study and the complexity of biological networks, clustering is an important exploratory technique for gene expression data analysis. In this article, we present a succinct review of some of our work in cluster analysis of gene expression data.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126922585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Class Prediction in Test Sets with Shifted Distributions","authors":"Óscar Pérez, Manuel A. Sánchez-Montañés","doi":"10.4018/978-1-59904-849-9.CH044","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH044","url":null,"abstract":"Machine learning has provided powerful algorithms that automatically generate predictive models from experience. One specific technique is supervised learning, where the machine is trained to predict a desired output for each input pattern x. This chapter will focus on classification, that is, supervised learning when the output to predict is a class label. For instance predicting whether a patient in a hospital will develop cancer or not. In this example, the class label c is a variable having two possible values, “cancer” or “no cancer”, and the input pattern x is a vector containing patient data (e.g. age, gender, diet, smoking habits, etc.). In order to construct a proper predictive model, supervised learning methods require a set of examples xi together with their respective labels ci. This dataset is called the “training set”. The constructed model is then used to predict the labels of a set of new cases xj called the “test set”. In the cancer prediction example, this is the phase when the model is used to predict cancer in new patients. One common assumption in supervised learning algorithms is that the statistical structure of the training and test datasets are the same (Hastie, Tibshirani & Friedman, 2001). That is, the test set is assumed to have the same attribute distribution p(x) and same class distribution p(c|x) as the training set. However, this is not usually the case in real applications due to different reasons. For instance, in many problems the training dataset is obtained in a specific manner that differs from the way the test dataset will be generated later. Moreover, the nature of the problem may evolve in time. These phenomena cause pTr(x, c) ≠ pTest(x, c), which can degrade the performance of the model constructed in training. Here we present a new algorithm that allows to re-estimate a model constructed in training using the unlabelled test patterns. We show the convergence properties of the algorithm and illustrate its performance with an artificial problem. Finally we demonstrate its strengths in a heart disease diagnosis problem where the training set is taken from a different hospital than the test set.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121044747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI Walk from Pharmacokinetics to A Marketing","authors":"J. Martín-Guerrero, E. Soria-Olivas, P. Lisboa, Antonio J. Serrano","doi":"10.4018/978-1-59904-849-9.CH011","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH011","url":null,"abstract":"• Artificial Neural Networks (ANNs): Multilayer Perceptron (MLP), Finite Impulse Response (FIR) Neural Network, Elman Network, Self-Oganizing Maps (SOMs) and Adaptive Resonance Theory (ART). • Other clustering algorithms: K-Means, Expectation-Maximization (EM) algorithm, Fuzzy C-Means (FCM), Hierarchical Clustering Algorithms (HCA). • Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH). • Support Vector Regression (SVR). • Collaborative filtering techniques. • Reinforcement Learning (RL) methods.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"35 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120938359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study on E-Note-Taking","authors":"Shaista Rashid, D. Rigas","doi":"10.4018/978-1-59904-849-9.CH052","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH052","url":null,"abstract":"In all walks of life individuals are involved in a cumulative and incremental process of knowledge acquisition. This involves the accessing, processing and understanding of information which can be gained through many different forms. These include, deliberate means by picking up a book or passive by listening to someone. The content of knowledge is translated by individuals and often recorded by the skill of note-taking, which differs in method from one person to another. This article presents an investigation into the techniques to take notes including the most popular Cornell method. A comparative analysis with the Outlining and Mapping methods are carried out stating strengths and weaknesses of each in terms of simplicity, usefulness and effectiveness. The processes of developing such skills are not easy or straightforward and performance is much influenced by cognition. Therefore, such associations regarding cognitive conceptions involve the exploration into note-taking processes encoding and storage, attention and concentration, memory and other stimuli factors such as multimedia. The social changes within education from the traditional manner of study to electronic are being adapted by institutes. This change varies from computerising a sub-component of learning to simulating an entire lecture environment. This has enabled students to explore academia more conveniently however, is still arguable about its feasibility. The article discusses the underlying pedagogical principles, deriving instructions for the development of an e-learning environment. Furthermore, embarking on Tablet PC’s to replace the blackboard in combination with annotation applications is investigated. Semantic analysis into the paradigm shift in e-learning and knowledge management replacing classroom interaction presents its potential in the learning domain. The article concludes with ideas for the design and development of an electronic note-taking platform. BACKGROUND","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121660583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous ACO in a SVR Traffic Forecasting Model","authors":"Wei‐Chiang Hong","doi":"10.4018/978-1-59904-849-9.CH063","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH063","url":null,"abstract":"The effective capacity of inter-urban motorway networks is an essential component of traffic control and information systems, particularly during periods of daily peak flow. However, slightly inaccurate capacity predictions can lead to congestion that has huge social costs in terms of travel time, fuel costs and environment pollution. Therefore, accurate forecasting of the traffic flow during peak periods could possibly avoid or at least reduce congestion. Additionally, accurate traffic forecasting can prevent the traffic congestion as well as reduce travel time, fuel costs and pollution. However, the information of inter-urban traffic presents a challenging situation; thus, the traffic flow forecasting involves a rather complex nonlinear data pattern and unforeseen physical factors associated with road traffic situations. Artificial neural networks (ANNs) are attracting attention to forecast traffic flow due to their general nonlinear mapping capabilities of forecasting. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training errors. SVR has been used to deal with nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines SVR model with continuous ant colony optimization (SVRCACO), to forecast inter-urban traffic flow. A numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed model. The simulation results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time-series model. BACKGROUND","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary Approaches to Variable Selection","authors":"M. Gestal, J. Andrade","doi":"10.4018/978-1-59904-849-9.CH089","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH089","url":null,"abstract":"The importance of juice beverages in daily food habits makes juice authentication an important issue, for example, to avoid fraudulent practices. A successful classification model should address two important cornerstones of the quality control of juicebased beverages: to monitor the amount of juice and to monitor the amount (and nature) of other substances added to the beverages. Particularly, sugar addition is a common and simple adulteration, though difficult to characterize. Other adulteration methods, either alone or combined, include addition of water, pulp wash, cheaper juices, colorants, and other undeclared additives (intended to mimic the compositional profiles of pure juices) (Saavedra, García, & Barbas, 2000).","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131270100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}