Encyclopedia of Artificial Intelligence最新文献

{"title":"Neural Network-Based Visual Data Mining for Cancer Data","authors":"E. Romero, J. J. Valdés, A. Barton","doi":"10.4018/978-1-59904-849-9.CH176","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH176","url":null,"abstract":"","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"128 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":"121625776","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":"Ontologies and Processing Patterns for Microarrays","authors":"Mónica Miguélez Rico, José Antonio Seoane Fernández, J. Dorado","doi":"10.4018/978-1-59904-849-9.CH186","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH186","url":null,"abstract":"The researchers currently have a new tool for dealing with the solution of biomedical problems: the Microarrays. These devices support the study and the acquisition of information related to many genes at the same time by means of a unique experiment, providing multiple potential applications such as mutation detection of microorganism identification. Some of the problems that exist when working with this type of technologies are the high number of data and the complex technical nomenclature to be dealt with. These facts imply the need of using several standards and ontologies when performing this type of experiments.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"121 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":"126313972","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":"E-Learning in New Technologies","authors":"Nieves Pedreira, J. R. Salgueiro, M. Carballo","doi":"10.4018/978-1-59904-849-9.CH081","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH081","url":null,"abstract":"E-learning and the impact of new technologies across contemporary life is a very significant field to education. The challenge of the technology to conventional learning patterns cannot be ignored and in itself raises a host of questions: can online learning facilitate deep learning? How well does video conferencing alleviate the challenge of distance? In what ways can collaborative learning communities be developed and sustained using current and new technologies? At the same time, new communications technologies are impacting on the ways in which we understand ourselves and the worlds in which we live. Relating to this, the aim of today’s education is not to learn certain contents, but rather learn to learn in the course of a whole lifetime. The study of the learning process can help us to find the relevant points to set up some interesting characteristics of a really functional e-learning system.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"18 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":"125769358","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":"Adaptive Technology and Its Applications","authors":"João Paulo da Silva Neto","doi":"10.4018/978-1-59904-849-9.CH006","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH006","url":null,"abstract":"Before the advent of software engineering, the lack of memory space in computers and the absence of established programming methodologies led early programmers to use self-modification as a regular coding strategy. Although unavoidable and valuable for that class of software, solutions using self-modification proved inadequate while programs grew in size and complexity, and security and reliability became major requirements. Software engineering, in the 70’s, almost led to the vanishing of self-modifying software, whose occurrence was afterwards limited to small low-level machinelanguage programs with very special requirements. Nevertheless, recent research developed in this area, and the modern needs for powerful and effective ways to represent and handle complex phenomena in hightechnology computers are leading self-modification to be considered again as an implementation choice in several situations. Artificial intelligence strongly contributed for this scenario by developing and applying non-conventional approaches, e.g. heuristics, knowledge representation and handling, inference methods, evolving software/ hardware, genetic algorithms, neural networks, fuzzy systems, expert systems, machine learning, etc. In this publication, another alternative is proposed for developing Artificial Intelligence applications: the use of adaptive devices, a special class of abstractions whose practical application in the solution of current problems is called Adaptive Technology. The behavior of adaptive devices is defined by a dynamic set of rules. In this case, knowledge may be represented, stored and handled within that set of rules by adding and removing rules that represent the addition or elimination of the information they represent. Because of the explicit way adopted for representing and acquiring knowledge, adaptivity provides a very simple abstraction for the implementation of artificial learning mechanisms: knowledge may be comfortably gathered by inserting and removing rules, and handled by tracking the evolution of the set of rules and by interpreting the collected information as the representation of the knowledge encoded in the rule set.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"4 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":"127578016","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":"Particle Swarm Optimization and Image Analysis","authors":"S. Cagnoni, M. Mordonini","doi":"10.4018/978-1-59904-849-9.CH191","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH191","url":null,"abstract":"Particle Swarm Optimization (PSO) is a simple but powerful optimization algorithm, introduced by Kennedy and Eberhart (Kennedy 1995). Its search for function optima is inspired by the behavior of flocks of birds looking for food. Similarly to birds, a set (swarm) of agents (particles) fly over the search space, which is coincident with the function domain, looking for the points where the function value is maximum (or minimum). In doing so, each particle’s motion obeys two very simple difference equations which describe the particle’s position and velocity update. A particle’s motion has a strong random component (exploration) and is mostly independent from the others’; in fact, the only piece of information which is shared among all members of the swarm, or of a large neighborhood of each particle, is the point where the best value for the function has been found so far. Therefore, the search behavior of the swarm can be defined as emergent, since no particle is specifically programmed to achieve the final collective behavior or to play a specific role within the swarm, but just to perform a much simpler local task. This chapter introduces the basics of the algorithm and describes the main features which make it particularly efficient in solving a large number of problems, with particular regard to image analysis and to the modifications that must be applied to the basic algorithm, in order to exploit its most attractive features in a domain which is different from function optimization.","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":"128935231","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":"Adaptive Business Intelligence","authors":"Z. Michalewicz","doi":"10.4018/9781599048499.ch003","DOIUrl":"https://doi.org/10.4018/9781599048499.ch003","url":null,"abstract":"Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"4 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":"128949828","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":"Active Learning with SVM","authors":"Jun Jiang, H. Ip","doi":"10.4018/978-1-59904-849-9.CH001","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH001","url":null,"abstract":"With the increasing demand of multimedia information retrieval, such as image and video retrieval from the Web, there is a need to find ways to train a classifier when the training dataset is combined with a small number of labelled data and a large number of unlabeled one. Traditional supervised or unsupervised learning methods are not suited to solving such problems particularly when the problem is associated with data in a high-dimension space. In recent years, many methods have been proposed that can be broadly divided into two groups: semi-supervised and active learning (AL). Support Vector Machine (SVM) has been recognized as an efficient tool to deal with high-dimensionality problems, a number of researchers have proposed algorithms of Active Learning with SVM (ALSVM) since the turn of the Century. Considering their rapid development, we review, in this chapter, the state-of-the-art of ALSVM for solving classification problems.","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":"115328847","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":"Differential Evolution with Self-Adaptation","authors":"J. Brest","doi":"10.4018/978-1-59904-849-9.CH074","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH074","url":null,"abstract":"Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boskovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"39 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":"115703654","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":"Neural/Fuzzy Computing Based on Lattice Theory","authors":"V. Kaburlasos","doi":"10.4018/978-1-59904-849-9.CH181","DOIUrl":"https://doi.org/10.4018/978-1-59904-849-9.CH181","url":null,"abstract":"Computational Intelligence (CI) consists of an evolving collection of methodologies often inspired from nature (Bonissone, Chen, Goebel & Khedkar, 1999, Fogel, 1999, Pedrycz, 1998). Two popular methodologies of CI include neural networks and fuzzy systems. Lately, a unification was proposed in CI, at a “data level”, based on lattice theory (Kaburlasos, 2006). More specifically, it was shown that several types of data including vectors of (fuzzy) numbers, (fuzzy) sets, 1D/2D (real) functions, graphs/trees, (strings of) symbols, etc. are partially(lattice)-ordered. In conclusion, a unified cross-fertilization was proposed for knowledge representation and modeling based on lattice theory with emphasis on clustering, classification, and regression applications (Kaburlasos, 2006). Of particular interest in practice is the totally-ordered lattice (R,≤) of real numbers, which has emerged historically from the conventional measurement process of successive comparisons. It is known that (R,≤) gives rise to a hierarchy of lattices including the lattice (F,≤) of fuzzy interval numbers, or FINs for short (Papadakis & Kaburlasos, 2007). This article shows extensions of two popular neural networks, i.e. fuzzy-ARTMAP (Carpenter, Grossberg, Markuzon, Reynolds & Rosen 1992) and self-organizing map (Kohonen, 1995), as well as an extension of conventional fuzzy inference systems (Mamdani & Assilian, 1975), based on FINs. Advantages of the aforementioned extensions include both a capacity to rigorously deal with nonnumeric input data and a capacity to introduce tunable nonlinearities. Rule induction is yet another advantage.","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"45 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":"115094718","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":"AVI of Surface Flaws on Manufactures I","authors":"G. Fornarelli, A. Giaquinto","doi":"10.4018/9781599048499.ch032","DOIUrl":"https://doi.org/10.4018/9781599048499.ch032","url":null,"abstract":"INTRODUCTION The defect detection on manufactures is of utmost importance in the optimization of industrial processes (Garcia 2005). In fact, the industrial inspection of engineering materials and products tends to the detection , localization and classification of flaws as quickly and as accurately as possible in order to improve the production quality. In this field a relevant area is constituted by visual inspection. Nowadays, this task is often carried out by a human expert. Nevertheless, such kind of inspection could reveal time-consuming and suffer of low repeatability because the judgment criteria can differ from operator to operator. Furthermore , visual fatigue or loss of concentration inevitably In order to reduce the burden of human testers and improve the detection of faulty products, recently many researchers have been engaged in developing systems in Automated Visual Inspection (AVI) of manufactures These systems reveal easily reliable from technical point of view and mimic the experts in the evaluation process of defects appropriately (Bahlmann, Heidemann & Ritter 1999), even if defect detection in visual inspection can become a hard task. In fact, in industrial processes a large amount of data has to be handled and flaws belong to a great number of classes with dynamic defect populations, because defects could present similar characteristics among different classes and different interclass features (R. visual inspection systems are able to adapt to dynamic operating conditions. To this purpose soft computing techniques based on the use of Artificial Neural Networks (ANNs) have already been proposed in several different areas of industrial production. In fact, neural networks are often exploited for their ability to recognize a wide spread of different defects 2006). Although adequate in many instances, in other cases Neural Networks cannot represent the most suitable solution. In fact, the design of ANNs often requires the extraction of parameters and features, during a preprocessing stage, from a suitable data set, in which the most possible defects are recognized (Bahlmann, Heidemann & Ritter 1999, Karras 2003, Rimac-Drlje, Keller & Hocenski 2005). Therefore, methods based on neural networks could be time expensive for in-line applications because such preliminary steps and reason, when in an industrial process time constraints play an important role, a hardware solution of the abovementioned methods can be proposed (R. of solution implies a further design effort which can be avoided by considering Cellular Neural Networks (CNNs) (Chua & Roska 2002). Cellular Neural Networks have good potentiality …","PeriodicalId":320314,"journal":{"name":"Encyclopedia of Artificial Intelligence","volume":"53 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":"117164563","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}