{"title":"State of the Art in Writer's Off-Line Identification","authors":"C. Travieso-González, Carlos F. Romero","doi":"10.4018/978-1-59904-849-9.CH212","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH212","url":null,"abstract":"Today, advances in Computer Science and the proliferation of computers in modern society are an unquestionable fact. Nevertheless, the continuing importance of orthography and the hand-written document are also beyond doubt. The new technologies permit us to work with online information collecting, but there is still a large quantity of information in our society which requires using algorithms for samples off-line. Security in certain applications requires having biometric systems for their identification; in particular, banking checks, wills, postcards, invoices, medical prescriptions, etc, require the identity of the person who has written them to be verified. The only way to do this is with writer recognition techniques. Furthermore, many hand-written documents are vulnerable to possible forgeries, deformations or copies, and generally, to illicit misuse. Therefore, a high percentage of routine work is carried out by experts and professionals in this field, whose task is to certify and to judge the authenticity or falsehood of handwritten documents (for example: wills) in a judicial procedure. Therefore nowadays research on writer identification is an active field. At present, some software tools enable certain characteristics to be displayed and visualised by experts and professionals, but these experts need to devote a great deal of time to such investigations before they are able to draw up conclusions about a given body of writing. Therefore, these tools are not time-saving and nor do they provide a meticulous analysis of the writing. They have to work with graph paper and templates in order to obtain parameters (angles, dimensions of the line, directions, parallelisms, curvatures, alignments, etc.). Moreover, they have to use a magnifying glass and graph paper in order to measure angles and lines. This research aims to lighten this arduous task.","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":"116825489","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":"Robots in Education","authors":"M. A. Yousuf","doi":"10.4018/978-1-59904-849-9.CH203","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH203","url":null,"abstract":"When a child is absent from school for a prolonged period, it can deeply affect their academic performance and emotional wellbeing. For many, the affects can be felt far into adulthood. Studies have shown that overall salary earned is strongly correlated to educational attainment. For example, in the US, individuals who did not graduate from high school earned $181 less weekly than their peers who did. For sick children, prolonged absences may mean it is difficult to achieve adequate level of school leaving them disadvantaged for the remainder of their life.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"3 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":"131057407","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}
J. Gómez, Ricardo Colomo Palacios, Marcos Ruano Mayoral, Á. García-Crespo
{"title":"Microarray Information and Data Integration Using SAMIDI","authors":"J. Gómez, Ricardo Colomo Palacios, Marcos Ruano Mayoral, Á. García-Crespo","doi":"10.4018/978-1-59904-849-9.CH157","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH157","url":null,"abstract":"Technological advances in high-throughput techniques and efficient data gathering methods, coupled computational biology efforts, have resulted in a vast amount of life science data often available in distributed and heterogeneous repositories. These repositories contain information such as sequence and structure data, annotations for biological data, results of complex computations, genetic sequences and multiple bio-datasets. However, the heterogeneity of these data, have created a need for research in resource integration and platform independent processing of investigative queries, involving heterogeneous data sources. When processing huge amounts of data, information integration is one of the most critical issues, because it’s crucial to preserve the intrinsic semantics of all the merged data sources. This integration would allow the proper organization of data, fostering the analysis and access the information to accomplish critical tasks, such as the processing of micro-array data to study protein function and medical researches in making detailed studies of protein structures to facilitate drug design (Ignacimuthu, 2005). Furthermore, DNA micro-array research community urgently requires technology to allow up-to-date micro-array data information to be found, accessed and delivered in a secure framework (Sinnot, 2007). Several research disciplines, such as Bioinformatics, where information integration is critical, could benefit from harnessing the potential of a new approach: the Semantic Web (SW). The SW term was coined by Berners-Lee, Hendler and Lassila (2001) to describe the evolution of a Web that consisted of largely documents for humans to read towards a new paradigm that included data and information for computers to manipulate. The SW is about adding machine-understandable and machine-processable metadata to Web resource through its key-enabling technology: ontologies (Fensel, 2002). Ontologies are a formal explicit and shared specification of a conceptualization. The SW was conceived as a way to solve the need for data integration on the Web. This article expounds SAMIDI, a Semantics-based Architecture for Micro-array Information and Data Integration. The most remarkable innovation offered by SAMIDI is the use of semantics as a tool for leveraging different vocabularies and terminologies and foster integration. SAMIDI is composed of a methodology for the unification of heterogeneous data sources from the analysis of the requirements of the unified data set and a software architecture.","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":"134017784","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":"Navigation by Image-Based Visual Homing","authors":"Matthew Szenher","doi":"10.4018/978-1-59904-849-9.CH173","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH173","url":null,"abstract":"Almost all autonomous robots need to navigate. We define navigation as do Franz & Mallot (2000): “Navigation is the process of determining and maintaining a course or trajectory to a goal location” (p. 134). We allow that this definition may be more restrictive than some readers are used to it does not for example include problems like obstacle avoidance and position tracking but it suits our purposes here. Most algorithms published in the robotics literature localise in order to navigate (see e.g. Leonard & Durrant-Whyte (1991a)). That is, they determine their own location and the position of the goal in some suitable coordinate system. This approach is problematic for several reasons. Localisation requires a map of available landmarks (i.e. a list of landmark locations in some suitable coordinate system) and a description of those landmarks. In early work, the human operator provided the robot with a map of its environment. Researchers have recently, though, developed simultaneous localisation and mapping (SLAM) algorithms which allow robots to learn environmental maps while navigating (Leonard & Durrant-Whyte (1991b)). Of course, autonomous SLAM algorithms must choose which landmarks to map and sense these landmarks from a variety of different positions and orientations. Given a map, the robot has to associate sensed landmarks with those on the map. This data association problem is difficult in cluttered real-world environments and is an area of active research. We describe in this chapter an alternative approach to navigation called visual homing which makes no explicit attempt to localise and thus requires no landmark map. There are broadly two types of visual homing algorithms: feature-based and image-based. The featurebased algorithms, as the name implies, attempt to extract the same features from multiple images and use the change in the appearance of corresponding features to navigate. Feature correspondence is like data association a difficult, open problem in real-world environments. We argue that image-based homing algorithms, which provide navigation information based on whole-image comparisons, are more suitable for real-world environments in contemporary robotics.","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":"123930467","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}
M. Martínez-Zarzuela, F. Pernas, D. G. Ortega, J. F. Higuera, M. Antón-Rodríguez
{"title":"Stream Processing of a Neural Classifier I","authors":"M. Martínez-Zarzuela, F. Pernas, D. G. Ortega, J. F. Higuera, M. Antón-Rodríguez","doi":"10.4018/978-1-59904-849-9.CH218","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH218","url":null,"abstract":"INTRODUCTION An Artificial Neural Network (ANN) is a computational structure inspired by the study of biological neural processing. Although neurons are considered as very simple computation units, inside the nervous system, an incredible amount of widely interconnected neurons can process huge amounts of data working in a parallel fashion. There are many different types of ANNs, from relatively simple to very complex, just as there are many theories on how biological neural processing works. However, execution of ANNs is always a heavy computational task. Important kinds of ANNs are those devoted to pattern recognition such as Multi-Layer Perceptron (MLP), Self-Organizing Maps (SOM) or Adaptive Resonance Theory (ART) classifiers (Haykin, 2007). Traditional implementations of ANNs used by most of scientists have been developed in high level programming languages, so that they could be executed on common Personal Computers (PCs). The main drawback of these implementations is that though neural networks are intrinsically parallel systems, simulations are executed on a Central Processing Unit (CPU), a processor designed for the execution of sequential programs on a Single Instruction Single Data (SISD) basis. As a result, these heavy programs can take hours or even days to process large input data. For applications that require real-time processing, it is possible to develop small ad-hoc neural networks on specific hardware like Field Programmable Gate Arrays (FPGAs). However, FPGA-based realization of ANNs is somewhat expensive and involves extra design overheads (Zhu & Sutton, 2003). Using dedicated hardware to do machine learning was typically expensive; results could not be shared with other researchers and hardware became obsolete within a few years. This situation has changed recently with the popularization of Graphics Processing Units (GPUs) as low-cost and high-level programmable hardware platforms. GPUs are being increasingly used for speeding up computations in many research fields following a Stream Processing Model This article presents a GPU-based parallel implementation of a Fuzzy ART ANN, which can be used both for training and testing processes. Fuzzy ART is an unsupervised neural classifier capable of incremental learning, widely used in a universe of applications as medical sciences, economics and finance, engineering and computer science. CPU-based implementations of Fuzzy ART lack efficiency and cannot be used for testing purposes in real-time applications. The GPU implementation of Fuzzy ART presented in this article speeds up computations more than 30 times with respect to a CPU-based C/C++ development when executed on an NVIDIA 7800 GT GPU.","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":"117305657","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":"Association Rule Mining","authors":"Vasudha Bhatnagar, S. Kochhar","doi":"10.4018/978-1-59904-849-9.CH027","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH027","url":null,"abstract":"INTRODUCTION Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health care improvement etc.. Data mining techniques can be classified as either descriptive or predictive techniques. Descriptive techniques summarize / characterize general properties of data, while predictive techniques construct a model from the historical data and use it to predict some characteristics of the future data. Association rule mining, sequence analysis and clustering are key descriptive data mining techniques, while classification and regression are predictive techniques. The objective of this article is to introduce the problem of association rule mining and describe some approaches to solve the problem.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"17 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114125524","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}
Pedro Antonio Gutiérrez, C. Hervás‐Martínez, F. Martínez-Estudillo, Mariano Carbonero-Ruz
{"title":"Multilogistic Regression by Product Units","authors":"Pedro Antonio Gutiérrez, C. Hervás‐Martínez, F. Martínez-Estudillo, Mariano Carbonero-Ruz","doi":"10.4018/978-1-59904-849-9.CH166","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH166","url":null,"abstract":"Multi-class pattern recognition has a wide range of applications including handwritten digit recognition (Chiang, 1998), speech tagging and recognition (Athanaselis, Bakamidis, Dologlou, Cowie, Douglas-Cowie & Cox, 2005), bioinformatics (Mahony, Benos, Smith & Golden, 2006) and text categorization (Massey, 2003). This chapter presents a comprehensive and competitive study in multi-class neural learning which combines different elements, such as multilogistic regression, neural networks and evolutionary algorithms. The Logistic Regression model (LR) has been widely used in statistics for many years and has recently been the object of extensive study in the machine learning community. Although logistic regression is a simple and useful procedure, it poses problems when is applied to a real-problem of classification, where frequently we cannot make the stringent assumption of additive and purely linear effects of the covariates. A technique to overcome these difficulties is to augment/replace the input vector with new variables, basis functions, which are transformations of the input variables, and then to use linear models in this new space of derived input features. Methods like sigmoidal feed-forward neural networks (Bishop, 1995), generalized additive models (Hastie & Tibshirani, 1990), and PolyMARS (Kooperberg, Bose & Stone, 1997), which is a hybrid of Multivariate Adaptive Regression Splines (MARS) (Friedman, 1991) specifically designed to handle classification problems, can all be seen as different nonlinear basis function models. The major drawback of these approaches is stating the typology and the optimal number of the corresponding basis functions. Logistic regression models are usually fit by maximum likelihood, where the Newton-Raphson algorithm is the traditional way to estimate the maximum likelihood a-posteriori parameters. Typically, the algorithm converges, since the log-likelihood is concave. It is important to point out that the computation of the Newton-Raphson algorithm becomes prohibitive when the number of variables is large. Product Unit Neural Networks, PUNN, introduced by Durbin and Rumelhart (Durbin & Rumelhart, 1989), are an alternative to standard sigmoidal neural networks and are based on multiplicative nodes instead of additive ones.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"30 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":"114535427","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":"Commonsense Knowledge Representation I","authors":"P. Ein-Dor","doi":"10.4018/9781599048499.ch050","DOIUrl":"https://doi.org/10.4018/9781599048499.ch050","url":null,"abstract":"Significant advances in artificial intelligence, including machines that play master level chess, or make medical diagnoses, highlight an intriguing paradox. While systems can compete with highly qualified experts in many fields, there has been much less progress in constructing machines that exhibit simple commonsense, the kind expected of any normally intelligent child. As a result, commonsense has been identified as one of the most difficult and important problems in AI (Doyle, 1984; Waltz, 1982).","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"83 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":"114582781","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":"Multilayer Optimization Approach for Fuzzy Systems","authors":"I. Silva, R. Flauzino","doi":"10.4018/978-1-59904-849-9.CH164","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH164","url":null,"abstract":"The design of fuzzy inference systems comes along with several decisions taken by the designers since is necessary to determine, in a coherent way, the number of membership functions for the inputs and outputs, and also the specification of the fuzzy rules set of the system, besides defining the strategies of rules aggregation and defuzzification of output sets. The need to develop systematic procedures to assist the designers has been wide because the trial and error technique is the unique often available (Figueiredo & Gomide, 1997). In general terms, for applications involving system identification and fuzzy modeling, it is convenient to use energy functions that express the error between the desired results and those provided by the fuzzy system. An example is the use of the mean squared error or normalized mean squared error as energy functions. In the context of systems identification, besides the mean squared error, data regularization indicators can be added to the energy function in order to improve the system response in presence of noises (from training data) (Guillaume, 2001). In the absence of a tuning set, such as happens in parameters adjustment of a process controller, the energy function can be defined by functions that consider the desired requirements of a particular design (Wan, Hirasawa, Hu & Murata, 2001), i.e., maximum overshoot signal, setting time, rise time, undamped natural frequency, etc. From this point of view, this article presents a new methodology based on error backpropagation for the adjustment of fuzzy inference systems, which can be then designed as a three layers model. Each one of these layers represents the tasks performed by the fuzzy inference system such as fuzzification, fuzzy rules inference and defuzzification. The adjustment procedure proposed in this article is performed through the adaptation of its free parameters, from each one of these layers, in order to minimize the energy function previously specified. In principle, the adjustment can be made layer by layer separately. The operational differences associated with each layer, where the parameters adjustment of a layer does not influence the performance of other, allow single adjustment of each layer. Thus, the routine of fuzzy inference system tuning acquires a larger flexibility when compared to the training process used in artificial neural networks. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, such methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"7 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":"116247265","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}
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