bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Tree Applications for Data Modelling
Authors: Wai Lee Man , Chrysostomou Kyriacos , Chen Sherry Y. , Liu Xiaohui
Published: May 2011

Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and identify hidden relationships, there is a need to employ intelligent techniques, for example, data mining. Data mining is a process of knowledge discovery (Roiger & Geatz, 2003). There are a wide range of data mining techniques, one of which is decision trees. Decision trees, which can be used for the purposes of classifications and predictions, are a tool to support decision making (Lee et al., 2007). As a decision tree can accurately classify data and make effective predictions, it has already been employed for data analyses in many application domains. In this paper, we attempt to provide an overview of the applications that decision trees can support. In particular, we focus on business management, engineering, and health-care management. The structure of the paper is as follows. Firstly, Section 2 provides the theoretical background of decision trees. Section 3 then moves to discuss the applications that decision trees can support, with an emphasis on business management, engineering, and health-care management. For each application, how decision trees can help identify hidden relationships is described. Subsequently, Section 4 provides a critical discussion of limitations and identifies potential directions for future research. Finally, Section 5 presents the conclusions of the paper.
Developmental Robotics
Authors: Lungarella Max , Gómez Gabriel
Published: May 2011

Human intelligence is acquired through a prolonged period of maturation and growth during which a single fertilized egg first turns into an embryo, then grows into a newborn baby, and eventually becomes an adult individual—which, typically before growing old and dying, reproduces. The developmental process is inherently robust and flexible, and biological organisms show an amazing ability during their development to devise adaptive strategies and solutions to cope with environmental changes and guarantee their survival. Because evolution has selected development as the process through which to realize some of the highest known forms of intelligence, it is plausible to assume that development is mechanistically crucial to emulate such intelligence in human-made artifacts.
Distributed Representation of Compositional Structure
Authors: Levy Simon D.
Published: May 2011

AI models are often categorized in terms of the connectionist vs. symbolic distinction. In addition to being descriptively unhelpful, these terms are also typically conflated with a host of issues that may have nothing to do with the commitments entailed by a particular model. A more useful distinction among cognitive representations asks whether they are local or distributed (van Gelder 1999). Traditional symbol systems (grammar, predicate calculus) use local representations: a given symbol has no internal content and is located at a particular address in memory. Although well understood and successful in a number of domains, traditional representations suffer from brittleness. The number of possible items to be represented is fixed at some arbitrary hard limit, and a single corrupt memory location or broken pointer can wreck an entire structure. In a distributed representation, on the other hand, each entity is represented by a pattern of activity distributed over many computing elements, and each computing element is involved in representing many different entities (Hinton 1984). Such representations have a number of properties that make them attractive for knowledge representation (McClelland, Rumelhart, & Hinton 1986): they are robust to noise, degrade gracefully, and support graded comparison through distance metrics. These properties enable fast associative memory and efficient comparison of entire structures without unpacking the structures into their component parts. This article provides an overview of distributed representations, setting the approach in its historical context. The two essential operations necessary for building distributed representation of structures – binding and bundling – are described. We present example applications of each model, and conclude by discussing the current state of the art.
Evolved Synthesis of Digital Circuits
Authors: Ionescu Laurentiu , Mazare Alin , Serban Gheorghe , Sofron Emil
Published: May 2011

Traditionally physical systems have been designed by engineers using complex collections of rules and principles. The design process is top-down in nature and begins with a precise specification. This contrasts very strongly with the mechanisms which have produced the extraordinary diversity and sophistication of living creatures. In this case the ‘‘designs’’ are evolved by a process of natural selection. The design starts as a set of instructions encoded in the DNA whose coding regions are first transcribed into RNA in the cell nucleus and then later translated into proteins in the cell cytoplasm. The DNA carries the instructions for building molecules using sequences of amino acids. Eventually after a number of extraordinarily complex and subtle biochemical reactions an entire living organism is created. The survivability of the organism can be seen as a process of assembling a larger system from a number of component parts and then testing the organism in the environment in which it finds itself (Miller, 2000). The main target of the evolvable hardware is to build a digital circuit using bio inspired methods like genetic algorithms. Here the potential solutions are coded like configuration vectors which command interconnection between logical cells inside the reconfigurable circuit. All configuration vectors represent the genotype and one single configuration vector is the individual with its own characteristics (like chromosome). The individuals are generated by genetic operators like crossover or mutation. One individual give one solution circuit which is tested in evaluation module. The circuit obtained from the individual consist the phenotype. The circuit behavior is compared with target functions, which we desire to implement. The result is fitness: if the circuit approximates the behavior of the target function, we have a good fitness for the individual which generate the circuit. Then each individual whit its fitness gets into selection module where the future parents in crossover and mutation are decided. Finally we have a circuit solution which implements the target function. We have an evolved synthesis of digital circuit – a method like assemble and test. This method can be useful because explore the design space beyond the limits imposed by traditional design methods. Two research directions are developed in evolvable hardware. In extrinsic evolvable hardware the individuals are obtained from software implementation on computer and phenotype consist in high level abstract circuits like SPICE object files or FPGA configuration files (.bit). The intrinsic evolution, on the other hand, supposes that entire evolution process is inside one or more chips (FPGA): the hardware implementation of evolved hardware. The challenge is to design an intrinsic evolution because can be used for applications like robots control system. But this involves implementation of the software based algorithms in hardware modules.
Genetic Algorithm Applications to Optimization Modeling
Authors: Deng Pi-Sheng
Published: May 2011

Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in identifying optimal or near optimal solutions (Davis, 1991)(Holland, 1992)(Reeves & Rowe, 2003), and are more likely able to avoid the local optima problem than traditional gradient based hill-climbing optimization techniques when solving complex problems. In essence, GAs are a type of reinforcement learning technique (Grefenstette, 1993), which are able to improve solutions gradually on the basis of the previous solutions. GAs are characterized by their abilities to combine candidate solutions to exploit efficiently a promising area in the solution space while stochastically exploring new search regions with expected improved performance. Many successful applications of this technique are frequently reported across various kinds of industries and businesses, including function optimization (Ballester & Carter, 2004)(Richter & Paxton, 2005), financial risk and portfolio management (Shin & Han, 1999), market trading (Kean, 1995), machine vision and pattern recognition (Vafaie & De Jong, 1998), document retrieval (Gordon, 1988), network topological design (Pierre & Legault, 1998)(Arabas & Kozdrowski, 2001), job shop scheduling (Özdamar, 1999), and optimization for operating system’s dynamic memory configuration (Del Rosso, 2006), among others. In this research we introduce the concept and components of GAs, and then apply the GA technique to the modeling of the batch selection problem of flexible manufacturing systems (FMSs). The model developed in this paper serves as the basis for the experiment in Deng (2007).
Hierarchical Neuro-Fuzzy Systems Part I
Authors: Vellasco Marley , Pacheco Marco , Figueiredo Karla , Souza Flavio
Published: May 2011

Neuro-fuzzy [Jang,1997][Abraham,2005] are hybrid systems that combine the learning capacity of neural nets [Haykin,1999] with the linguistic interpretation of fuzzy inference systems [Ross,2004]. These systems have been evaluated quite intensively in machine learning tasks. This is mainly due to a number of factors: the applicability of learning algorithms developed for neural nets; the possibility of promoting implicit and explicit knowledge integration; and the possibility of extracting knowledge in the form of fuzzy rules. Most of the well known neuro-fuzzy systems, however, present limitations regarding the number of inputs allowed or the limited (or nonexistent) form to create their own structure and rules [Nauck,1997][Nauck,19 98][Vuorimaa,1994][Zhang,1995]. This paper describes a new class of neuro-fuzzy models, called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ the BSP partitioning (Binary Space Partitioning) of the input space [Chrysanthou,1996] and have been developed to bypass traditional drawbacks of neuro-fuzzy systems. This paper introduces the HNFB models based on supervised learning algorithm. These models were evaluated in many benchmark applications related to classification and time-series forecasting. A second paper, entitled Hierarchical Neuro-Fuzzy Systems Part II, focuses on hierarchical neuro-fuzzy models based on reinforcement learning algorithms.
HOPS
Authors: Cagnoni Stefano , Mordonini Monica , Mussi Luca , Adorni Giovanni
Published: May 2011

Biological vision processes are usually characterized by the following different phases: • Awareness: natural or artificial agents operating in dynamic environments can benefit from a, possibly rough, global description of the surroundings. In human this is referred to as peripheral vision, since it derives from stimuli coming from the edge of the retina. • Attention: once an interesting object/event has been detected, higher resolution is required to set focus on it and plan an appropriate reaction. In human this corresponds to the so-called foveal vision, since it originates from the center of the retina (fovea). • Analysis: extraction of detailed information about objects of interest, their three-dimensional structure and their spatial relationships completes the vision process. Achievement of these goals requires at least two views of the surrounding scene with known geometrical relations. In humans, this function is performed exploiting binocular (stereo) vision. Computer Vision has often tried to emulate natural systems or, at least, to take inspiration from them. In fact, different levels of resolution are useful also in machine vision. In the last decade a number of studies dealing with multiple cameras at different resolutions have appeared in literature. Furthermore, the ever-growing computer performances and the ever-decreasing cost of video equipment make it possible to develop systems which rely mostly, or even exclusively, on vision for navigating and reacting to environmental changes in real time. Moreover, using vision as the unique sensory input makes artificial perception closer to human perception, unlike systems relying on other kinds of sensors and allows for the development of more direct biologically-inspired approaches to interaction with the external environment (Trullier 1997). This article presents HOPS (Hybrid Omnidirectional Pin-hole Sensor), a class of dual camera vision sensors that try to exalt the connection between machine vision and biological vision.
Incorporating Fuzzy Logic in Data Mining Tasks
Authors: Rokach Lior
Published: May 2011

In this chapter we discuss how fuzzy logic extends the envelop of the main data mining tasks: clustering, classification, regression and association rules. We begin by presenting a formulation of the data mining using fuzzy logic attributes. Then, for each task, we provide a survey of the main algorithms and a detailed description (i.e. pseudo-code) of the most popular algorithms. There are two main types of uncertainty in supervised learning: statistical and cognitive. Statistical uncertainty deals with the random behavior of nature and all existing data mining techniques can handle the uncertainty that arises (or is assumed to arise) in the natural world from statistical variations or randomness. Cognitive uncertainty, on the other hand, deals with human cognition. Fuzzy set theory, first introduced by Zadeh in 1965, deals with cognitive uncertainty and seeks to overcome many of the problems found in classical set theory. For example, a major problem faced by researchers of control theory is that a small change in input results in a major change in output. This throws the whole control system into an unstable state. In addition there was also the problem that the representation of subjective knowledge was artificial and inaccurate. Fuzzy set theory is an attempt to confront these difficulties and in this chapter we show how it can be used in data mining tasks.
Intelligent Classifier for Atrial Fibrillation (ECG)
Authors: Valenzuela O. , Rojas I. , Rojas F. , Guillen A. , Herrera L. J. , Rojas F. J. , Cepero M.
Published: May 2011

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.
Intelligent Query Answering Mechanism in Multi Agent Systems
Authors: Turgay Safiye , Yaman Fahrettin
Published: May 2011

The query answering system realizes the selection of the data, preparation, pattern discovering, and pattern development processes in an agent-based structure within the multi agent system, and it is designed to ensure communication between agents and an effective operation of agents within the multi agent system. The system is suggested in a way to process and evaluate fuzzy incomplete information by the use of fuzzy SQL query method. The modelled system gains the intelligent feature, thanks to the fuzzy approach and makes predictions about the future with the learning processing approach. The operation mechanism of the system is a process in which the agents within the multi agent system filter and evaluate both the knowledge in databases and the knowledge received externally by the agents, considering certain criteria. The system uses two types of knowledge. The first one is the data existing in agent databases within the system and the latter is the data agents received from the outer world and not included in the evaluation criteria. Upon receiving data from the outer world, the agent primarily evaluates it in knowledgebase, and then evaluates it to be used in rule base and finally employs a certain evaluation process to rule bases in order to store the knowledge in task base. Meanwhile, the agent also completes the learning process. This paper presents an intelligent query answering mechanism, a process in which the agents within the multi-agent system filter and evaluate both the knowledge in databases and the knowledge received externally by the agents. The following sections include some necessary literature review and the query answering approach Then follow the future trends and the conclusion.
Intelligent Software Agents with Applications in Focus
Authors: Jankovic-Romano Mario , Stankovic Milan , Krcadinac Uroš
Published: May 2011

Most people are familiar with the concept of agents in real life. There are stock-market agents, sports agents, real-estate agents, etc. Agents are used to filter and present information to consumers. Likewise, during the last couple of decades, people have developed software agents, that have the similar role. They behave intelligently, run on computers, and are autonomous, but are not human beings. Basically, an agent is a computer program that is capable of performing a flexible and independent action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents are capable of performing actions and making decisions without the guidance of a human. Software agents emerged in the IT because of the ever-growing need for information processing, and the problems concerning dealing and working with large quantities of data. Especially important is how agents act with other agents in the same environment, and the connections they form to find, refine and present the information in a best way. Agents certainly can do tasks better if they perform together, and that is why the multi-agent systems were developed. The concept of an agent has become important in a diverse range of sub-disciplines of IT, including software engineering, networking, mobile systems, control systems, decision support, information recovery and management, e-commerce, and many others. Agents are now used in an increasingly wide number of applications — ranging from comparatively small systems such as web or e-mail filters to large, complex systems such as air-traffic control, that have a large dependency on fast and precise decision making. Undoubtedly, the main contribution to the field of intelligent software agents came from the field of artificial intelligence (AI). The main focus of AI is to build intelligent entities and if these entities sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, and Yonezawa, 1993), and human- computer interaction (Maes, 1994) are fields that constantly drive forward the development of agents.
Intuitionistic Fuzzy Image Processing
Authors: Vlachos Ioannis K. , Sergiadis George D.
Published: May 2011

Since its genesis, fuzzy sets (FSs) theory (Zadeh, 1965) provided a flexible framework for handling the indeterminacy characterizing real-world systems, arising mainly from the imprecise and/or imperfect nature of information. Moreover, fuzzy logic set the foundations for dealing with reasoning under imprecision and offered the means for developing a context that reflects aspects of human decision-making. Images, on the other hand, are susceptible of bearing ambiguities, mostly associated with pixel values. This observation was early identified by Prewitt (1970), who stated that “a pictorial object is a fuzzy set which is specified by some membership function defined on all picture points”, thus acknowledging the fact that “some of its uncertainty is due to degradation, but some of it is inherent”. A decade later, Pal & King (1980) (1981) (1982) introduced a systematic approach to fuzzy image processing, by modelling image pixels using FSs expressing their corresponding degrees of brightness. A detailed study of fuzzy techniques for image processing and pattern recognition can be found in Bezdek et al and Chi et al (Bezdek, Keller, Krisnapuram, & Pal, 1999) (Chi, Yan, & Pham, 1996). However, FSs themselves suffer from the requirement of precisely assigning degrees of membership to the elements of a set. This constraint raises some of the flexibility of FSs theory to cope with data characterized by uncertainty. This observation led researchers to seek more efficient ways to express and model imprecision, thus giving birth to higher-order extensions of FSs theory. This article aims at outlining an alternative approach to digital image processing using the apparatus of Atanassov’s intuitionistic fuzzy sets (A-IFSs), a simple, yet efficient, generalization of FSs. We describe heuristic and analytic methods for analyzing/synthesizing images to/from their intuitionistic fuzzy components and discuss the particular properties of each stage of the process. Finally, we describe various applications of the intuitionistic fuzzy image processing (IFIP) framework from diverse imaging domains and provide the reader with open issues to be resolved and future lines of research to be followed.
Managing Uncertainties in Interactive Systems
Authors: Chen Qiyang , Wang John
Published: May 2011

To adapt users’ input and tasks an interactive system must be able to establish a set of assumptions about users’ profiles and task characteristics, which is often referred as user models. However, to develop a user model an interactive system needs to analyze users’ input and recognize the tasks and the ultimate goals users trying to achieve, which may involve a great deal of uncertainties. In this chapter the approaches for handling uncertainty are reviewed and analyzed. The purpose is to provide an analytical overview and perspective concerning the major methods that have been proposed to cope with uncertainties.
Mapping Ontologies by Utilising Their Semantic Structure
Authors: Zhao Yi , Halang Wolfgang A.
Published: May 2011

As a key factor to enable interoperability in the Semantic Web (Berners-Lee, Hendler & Lassila, 2001), ontologies are developed by different organisations at a large scale, also in overlapping areas. Therefore, ontology mapping has come into forth to achieve knowledge sharing and semantic integration in an environment where knowledge and information are represented by different underlying ontologies. The ontology mapping problem can be defined as acquiring the relationships that hold between the entities of two ontologies. Mapping results can be used for various purposes such as schema/ontology integration, information retrieval, query mediation, or web service mapping. In this article, a method to map concepts and properties between ontologies is presented. First, syntactic analysis is applied based on token strings, and then semantic analysis is executed according to WordNet (Fellbaum, 1999) and tree-like graphs representing the structures of ontologies. The experimental results exemplify that our algorithm finds mappings with high precision.
Mobile Robots Navigation, Mapping, and Localization Part II
Authors: Hee Lee Gim , Ang Jr. Marcelo H.
Published: May 2011

In addition to the capability to navigate from a point of origin to a given goal and avoiding all static and dynamic obstacles, a mobile robot must posses another two competencies: map building and localization in order to be useful. A mobile robot acquires information of its environment via the process of map building. Map building for mobile robots are commonly divided into occupancy grid and topological maps. Occupancy-grid maps seek to represent the geometric properties of the environment. Occupancy-grid mapping was first suggested by Elfes in 1987 and the idea was published in his Ph.D. thesis (A. Elfes, 1989) in 1989. Topological mapping was first introduced in 1985 as an alternative to the occupancy- grid mapping by R. Chatila and J.-P. Laumond (R. Chatila, & J.-P. Laumond, 1985). Topological maps describe the connectivity of different locations in the environment. The pose of a mobile robot must be known at all times for it to navigation and build a map accurately. This is the problem of localization and it was first described in the late 1980’s by R. Smith et al (R. Smith et al, 1980). Some key algorithms for map building and localization will be discussed in this article.
Multi-Layered Semantic Data Models
Authors: Kovács László , Sieber Tanja
Published: May 2011

One of the basic terms in information engineering is data. In our approach, data item is defined as representation of an information atom stored in digital computers. Although an information atom can be considered as a subject-predicate-value triplet (Lassila, 1999), data is usually given only with its value representation. This fact can lead to definitions where data is just numbers, words or pictures without context. For example in (WO, 2007), data is given as information in numerical form that can be digitally transmitted or processed. It is interesting that we can often recognize that the term ‘data’ is used without any exact terminological definition with the effect that the term often remains confusing, sometimes even contradicting the definitions of the term presented. Sieber and Kammerer (2006) introduce a new interpretation of data containing several levels. The lowest level belongs to data instances that describe the form and appearance of symbols. The intermediate level is the level of representatives which includes the applied encoding system. The highest level is related to the meaning with context description. All three levels are needed to get to know the information atom. For example the symbol ‘36’ in a database determines only the value and representation system, but not the meaning. To cover the whole information atom, the database should store some additional data items to describe the original data. The main purpose of semantic data models is to describe both context and the main structure of data items in the problem area. These additional data items are called metadata. It is important to see that: • metadata are data, • metadata are relative, and • metadata describe data. Metadata constitute a basis for bringing together data that are related in terms of content, and for processing them further. They can be understood as a pre-requisite for intelligent and efficient administration and processing, and not least as a focused, formal means of providing relevant data.
"Narrative" Information Problems
Authors: Piero Zarri Gian
Published: May 2011

‘Narrative’ information concerns in general the account of some real-life or fictional story (a ‘narrative’) involving concrete or imaginary ‘personages’. In this article we deal with (multimedia) nonfictional narratives of an economic interest. This means, first, that we are not concerned with all sorts of fictional narratives that have mainly an entertainment value, and represent an imaginary narrator’s account of a story that happened in an imaginary world: a novel is a typical example of fictional narrative. Secondly, our ‘nonfictional narratives’ must have an economic value: they are then typically embodied into corporate memory documents, they concern news stories, normative and legal texts, medical records, intelligence messages, surveillance videos or visitor logs, actuality photos and video fragments for newspapers and magazines, eLearning and multimedia Cultural Heritage material, etc. Because of the ubiquity of these ‘narrative’, ‘dynamic’ resources, it is particularly important to build up computer-based applications able to represent and to exploit in a general, accurate, and effective way the semantic content – i.e., the key ‘meaning’ – of these resources.
Neural Networks and Equilibria, Synchronization, and Time Lags
Authors: Danciu Daniela , Rasvan Vladimir
Published: May 2011

All neural networks, both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call “learning dynamics”, in fact the sequential (discrete time) dynamics of the choice of synaptic weights. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. Regarding the second dynamics, the emergent computational capabilities of a recurrent neural network can be achieved provided it has many equilibria. The network task is achieved provided it approaches these equilibria. But the dynamical system has a dynamics induced a posteriori by the learning process that had established the synaptic weights. It is not compulsory that this a posteriori dynamics should have the required properties, hence they have to be checked separately. The standard stability properties (Lyapunov, asymptotic and exponential stability) are defined for a single equilibrium. Their counterpart for several equilibria are: mutability, global asymptotics, gradient behavior. For the definitions of these general concepts the reader is sent to Gelig et. al., (1978), Leonov et. al., (1992). In the last decades, the number of recurrent neural networks’ applications increased, they being designed for classification, identification and complex image, visual and spatio-temporal processing in fields as engineering, chemistry, biology and medicine (see, for instance: Fortuna et. al., 2001; Fink, 2004; Atencia et. al., 2004; Iwahori et. al., 2005; Maurer et. al., 2005; Guirguis & Ghoneimy, 2007). All these applications are mainly based on the existence of several equilibria for such networks, requiring them the “good behavior” properties above discussed. Another aspect of the qualitative analysis is the so-called synchronization problem, when an external stimulus, in most cases periodic or almost periodic has to be tracked (Gelig, 1982; Danciu, 2002). This problem is, from the mathematical point of view, nothing more but existence, uniqueness and global stability of forced oscillations.
Ontologies and Processing Patterns for Microarrays
Authors: Miguélez Rico Mónica , Antonio Seoane Fernández José , Dorado de la Calle Julián
Published: May 2011

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.
Ontology Alignment Overview
Authors: Manuel Vázquez Naya José , Martínez Romero Marcos , Pereira Loureiro Javier , Pazos Sierra Alejandro
Published: May 2011

At present, ontologies are considered to be an appropriate solution to the problem of heterogeneity in data, since ontological methods make it possible to reach a common understanding of concepts in a particular domain. However, utilizing a single ontology is neither always possible nor recommendable, given that different tasks or different points of view usually require different conceptualizations. This can lead to the usage of different ontologies, although in some cases the different ontologies collectively might contain information that could be overlapping and possibly even contradictory. This, in turn, represents another type of heterogeneity that can result in inefficient processing or misinterpretation of data, information, and knowledge. To address this problem while at the same time insure an appropriate level of interoperability between heterogeneous systems, it is necessary to find correspondences or mappings that exist between the elements of the (different) ontologies being used. This process is known as ontology alignment. This article offers an updated overview of ontology alignment, including a detailed explanation of what alignment consists of, and how it can be achieved. First, ontologies are defined using a fusion of different interpretations. This is followed by a definition of the concept of ontology alignment and, using a simple example, some of the most commonly used alignment techniques are illustrated. Subsequently, a case is made for the importance of automating the process of ontology alignment, summarizing some of the main alignment systems currently in use. Finally, in the context of future directions, a discussion is presented of the advantages associated with integrating ontology alignment into systems that require exchanging information in an automatic fashion.
Personalized Decision Support Systems
Authors: Shambaugh Neal
Published: May 2011

Decision support systems (DSS) are computerized systems that assist humans to make decisions. Early versions were designed for executives, but over time DSSs were designed for workers at any level in the organization (Keen & Morton, 1978; Rockart, 1979). Due to increasing costs in providing benefits and services, organizations are forcing workers and consumers to take increasing responsibility for insurance, health care, and financial planning decisions. Extreme events, such as terrorism, pandemics, and natural disasters will swamp the capacity of governmental agencies to serve their citizenry. Individuals in affected communities must turn to local agencies or ad hoc groups for assistance. Personal decision support systems (PDSS), consisting of databases, model-based expertise, and intelligent interfaces, along with wireless communications, Internet resources, and personal computing, provide sufficient resources to assist informed individuals and groups in solving problems. This article reviews the typical components of a DSS and the different types of systems that have evolved. The article poses three types of problems facing individuals, including routine problem solving, immediate survival needs, and long-term evolutionary growth. Personal decision support issues of acquiring information, processing information, and dissemination are outlined. Future trends and research opportunities are discussed
Planning Agent for Geriatric Residences
Authors: Bajo Javier , Tapia Dante I. , Rodríguez Sara , Corchado Juan M.
Published: May 2011

Agents and Multi-Agent Systems (MAS) have become increasingly relevant for developing distributed and dynamic intelligent environments. The ability of software agents to act somewhat autonomously links them with living animals and humans, so they seem appropriate for discussion under nature-inspired computing (Marrow, 2000). This paper presents AGALZ (Autonomous aGent for monitoring ALZheimer patients), and explains how this deliberative planning agent has been designed and implemented. A case study is then presented, with AGALZ working with complementary agents into a prototype environment-aware multi-agent system (ALZ-MAS: ALZheimer Multi-Agent System) (Bajo, Tapia, De Luis, Rodríguez & Corchado, 2007). The elderly health care problem is studied, and the possibilities of Radio Frequency Identification (RFID) (Sokymat, 2006) as a technology for constructing an intelligent environment and ascertaining patient location to generate plans and maximize safety are examined. This paper focuses in the development of natureinspired deliberative agents using a Case-Based Reasoning (CBR) (Aamodt & Plaza, 1994) architecture, as a way to implement sensitive and adaptive systems to improve assistance and health care support for elderly and people with disabilities, in particular with Alzheimer. Agents in this context must be able to respond to events, take the initiative according to their goals, communicate with other agents, interact with users, and make use of past experiences to find the best plans to achieve goals, so we propose the development of an autonomous deliberative agent that incorporates a Case-Based Planning (CBP) mechanism, derivative from Case-Based Reasoning (CBR) (Bajo, Corchado & Castillo, 2006), specially designed for planning construction. CBP-BDI facilitates learning and adaptation, and therefore a greater degree of autonomy than that found in pure BDI (Believe, Desire, Intention) architecture (Bratman, 1987). BDI agents can be implemented by using different tools, such as Jadex (Pokahr, Braubach & Lamersdorf, 2003), dealing with the concepts of beliefs, goals and plans, as java objects that can be created and handled within the agent at execution time.
RBF Networks for Power System Topology Verification
Authors: Lukomski Robert , Wilkosz Kazimierz
Published: May 2011

A necessary condition for monitoring and control of a Power System (PS) is possessing a credible model of this system. The PS model for a need of dispatchers in national control centre is created in real time. An important element of such a model is a topology model. PS Topology Verification (PSTV) is an important problem in PS engineering. Often this problem is solved together with PS state estimation (Lukomski, & Wilkosz, 2000; Mai, Lefebvre, & Xuan, 2003). Methods, that enable such a solution of the problem, are sophisticated and usually time consuming. They require successful state estimation performance but convergence problems may occur in the case of certain Topology Errors (TEs). Thus, a robust method for PSTV before a state estimation is desired.
Rough Set-Based Neuro-Fuzzy System
Authors: Keng Ang Kai , Quek Chai
Published: May 2011

Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000). NFS is capable of abstracting a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises of if-then fuzzy rules and linguistic terms described by membership functions. Hence, the main strength of NFS in modeling data is universal approximation (Tikk, Kóczy, & Gedeon, 2003) with the ability to solicit interpretable if-then fuzzy rules (Guillaume, 2001). However, modeling data using NFS involves the contradictory requirements of interpretability versus accuracy. Prevailingly, NFS that focused on accuracy employed optimization which resulted in membership functions that derailed from human-interpretable linguistic terms, or employed large number of if-then fuzzy rules on high-dimensional data that exceeded human level interpretation. This article presents a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS). RNFS synergizes the sound concept of knowledge reduction from rough set theory with NFS. RNFS reinforces the strength of NFS by employing rough set-based techniques to perform attribute and rule reductions, thereby improving the interpretability without compromising the accuracy of the abstracted fuzzy model.
Swarm Intelligence Approach for Ad-Hoc Networks
Authors: Narula Prayag , Misra Sudip , Kumar Dhurandher Sanjay
Published: May 2011

Wireless ad-hoc networks are infrastructureless and they consist of nodes that come together and start communicating dynamically without requiring any backbone support. The nodes can enter and leave the network at will and can move about in the network at will. Ad-hoc networks present the perfect test-beds for bio-inspired computing algorithms. Both ad-hoc networks and bio-inspired computing approaches are characterized by self-organization, feedback and structural and functional complexity (Toh, 2002) (deCastro & Von Zuben, 2005). Hence, bio-inspired algorithms often provide us an opportunity to solve the most complex problems of ad-hoc networks in a satisfactory manner. In this chapter, we present the works done in the field of ad-hoc networks using bio-inspired Swarm Intelligence (SI). In particular, we look at how we can use Ant Colony Optimization (ACO) technique, a SI technique, for optimal routing in ad-hoc networks.
Swarm Robotics
Authors: Sharkey Amanda J.C.
Published: May 2011

Swarm Robotics is a biologically inspired approach to the organisation and control of groups of robots. Its biological inspiration is mainly drawn from social insects, but also from herding and flocking phenomena in mammals and fish. The promise of emulating some of the efficient organisational principles of biological swarms is an alluring one. In biological systems such as colonies of ants, sophisticated cooperative behaviour emerges despite the simplicity of the individual members, and the absence of centralised control and explicit directions. Such societies are able to maintain themselves as a collective, and to accomplish coordinated actions such as those required to construct and maintain nests, to find food, and to raise their young. The central idea behind swarm robotics is to find similar ways of coordinating and controlling collections of robots.
Socio-Cultural Interpretations to the Diffusion and Use of Broadband Services in a Korean Digital Society
Authors: Jin Dal Yong
Published: May 2011

This chapter attempts to ascertain the causes of the rapid growth of broadband services in the context of the broader socio-cultural elements. It recognizes technology as a socio-cultural product which has historically been constituted by certain forms of knowledge and social practice, so this chapter explores cultural elements contributing to the diffusion of broadband services in the context of the cultural environment in Korea. Further, this chapter discusses the significant role of the people, as users, in the process of the rapid diffusion and growth of broadband services. In particular, it emphasizes the way in which the 1997 economic crisis, as one of the most significant socio-cultural turning points in modern Korean history, has influenced the deployment of broadband services as high-speed Internet connections have developed since 1997.
The Design and Implementation of Business Models in Metropolitan Broadband Networks
Authors: Alexiou Antonios , Bouras Christos , Primpas John , Papagiannopoulos Dimitrios
Published: May 2011

This chapter presents the design principles that cover the implementation of broadband infrastructure in the region of Western Greece, by examining all the necessary parameters that arise while implementing such a critical developmental project. The broadband infrastructure that is deployed is either based on optical fiber (on big municipalities) or on wireless systems (OFDM based and WiFi cells). Furthermore, we present as two case studies all issues of the designing of the Metropolitan Area Network of Patras, the third largest city of Greece and the Wireless Access Network of Messatida. The major target of the broadband networks is to interconnect the buildings of the public sector in the city and also deploy infrastructure (fibers or wireless systems) that will create conditions of competition in providing both access and content services to the advantage of the end consumer. The usage of the broadband infrastructure by service providers will be based on the open availability of the infrastructure in a cost-effective way. Finally, we present the main characteristics of a proposed business plan that ensures financial viability of the broadband infrastructure and guarantees the administration, growth, and exploitation of infrastructure.
Governmental and Cultural Factors in Broadband Adoption
Authors: Fife Elizabeth , Hosman Laura , Pereira Francis
Published: May 2011

Though the potential benefits of broadband Internet adoption are great, the levels of take-up vary greatly around the world. Some governments have adopted aggressive policies to deploy broadband networks and to encourage the use of these applications, while others have not. In the former cases, governments are motivated to promote broadband adoption in order to realize both economic and social benefits. This chapter argues that the high level of broadband adoption rates witnessed in certain Asian economies is attributable in part to the aggressive policies pursued by these governments. Independent of these policies however, social factors can also have an impact on whether broadband-related technology will be adopted. Even if economic and social benefits exist therefore, as in the case of telemedicine in the United States, cultural and social factors may in fact hinder the deployment of such applications and retard the growth rate of broadband access.
Regulation and the Deployment of Broadband
Authors: Prieger James E. , Lee Sunhwa
Published: May 2011

This study examines the impact of telecommunications regulatory policy on broadband service deployment. Using U.S. data covering all forms of access technology (chiefly DSL and cable modem) and all areas served by major carriers, we investigate the impact of state and federal regulation on broadband availability. Alternative regulation increases the probability of broadband availability, particularly for price caps. Unbundled network element (UNE) rates, the prices incumbent carriers charge to competitors for access to the local exchange network, also matter. Areas with lower UNE rates have a slightly higher probability of broadband availability. The effects of UNE rates on broadband deployment are largest where incentive regulation is in place. Our objective in examining regulatory factors is to highlight the role of incentive regulation and local telecommunications competition policy—policies used or available around the world—in stimulating broadband service deployment.