bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Ecosystems and Principles of Community Innovation
Authors: Botero Andrea , Karhu Kimmo , Vihavainen Sami
Published: October 2011

In this chapter, we explore some of the contemporary configurations of what we will refer to as community innovation. We probe the relevance of the phenomena by illustrating and comparing the digital ecosystems that surround some communities that innovate together in a world of social media and Web 2.0 tools. In particular, two cases are used to illustrate the arguments: a collective venture for designing electric car conversion kits (eCars – Now!) and a looser collective representing the development ties of LEGO® user groups with the firm. These cases are presented through their existing ecosystem and communication tools and the ways in which their stories challenge linear and individualistic models of innovation. We argue that, for these communities, configuring and constructing an appropriate set of communication tools and new media seem critical in negotiating a place for themselves between grassroots cultural innovation and corporate control. In doing this, we also suggest some social principles that drive community innovation practices, as they are present through our examples.
Content-Based Image Retrieval for Advancing Medical Diagnostics, Treatment and Education
Authors: Long L. Rodney , Antani Sameer , Thoma George R. , Deserno Thomas M.
Published: October 2011

Content-Based Image Retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large medical image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practical medical problems is a goal yet to be realized. Furthermore, we highlight “gaps” between desired CBIR system functionality and what has been achieved to date, present a comparative analysis of four state-of-the-art CBIR implementations using the gap approach for illustration, and suggest that high-priority gaps to be overcome lie in CBIR interfaces and functionality that better serve the clinical and biomedical research communities.
Healthcare Information Systems Research
Authors: McLeod Alexander J. , Clark Jan G.
Published: October 2011

It is not a simple matter to generalize healthcare IS research, assuming that it is equivalent to organizational IS research. Hospitals, emergency rooms, and laboratories are very different from the normal “business” environment, and “healthcare users” vary considerably in the role that they play. Therefore, IS researchers need to understand the healthcare setting before they can appropriately apply IS theory. Obviously, if we are studying the wrong person, or group of people, we cannot expect to produce relevant research. In order to alleviate confusion regarding who is the user in healthcare IS research, we provide examples of several healthcare scenarios, perform a simplified stakeholder analysis in each scenario, and identify the stakeholders and their roles in each scenario.
Perceptions of an Organizing Vision for Electronic Medical Records by Independent Physician Practices
Authors: Reardon John L.
Published: October 2011

Actual adoption and usage rates of healthcare Information Technology (HIT) in general and electronic medical records (EMR) in particular are well below expectations, even though both show potential to help solve some of the more pressing problems plaguing the U.S. healthcare system. This research explores the role that a community-wide organizing vision (OV) (Ramiller & Swanson, 2003) plays in shaping independent physician practices’ perceptions of EMR technology, and hence, their interest in adopting and using the technology. This chapter reports on an OV for EMRs by analyzing data collected using a mail survey of independent physician practices and uses factor analysis to examine structural properties and content of the OV among the practices sampled. Contributions to theory include exploring the applicability of Ramiller and Swanson’s (Ramiller & Swanson, 2003; Swanson & Ramiller, 2004, 1997) OV on HIT innovations in healthcare research. Contributions to practice include empowering HIT decision makers with a model for addressing the introduction of a technology innovation (EMR) into an independent physician practice.
EMR Implementation and the Import of Theory and Culture
Authors: Cellucci Leigh W. , Wiggins Carla , Trimmer Kenneth
Published: October 2011

Many policymakers, industry experts, and medical practitioners contend that the U.S. healthcare system—in both the public and private sectors—is in crisis. Among the numerous policy issues associated with the provision of US healthcare is the call for increased adoption and use of healthcare information technology (HIT) to address structural inefficiencies and care quality issues (GAO, 2005 p. 33). This chapter reports the first steps in a multi-phased research effort into Electronic Medical Records system adoption. The first two phases of our research apply the Unified Theory of Acceptance and Use of Technology as a lens through which to interpret the responses of physicians completing their residency in Family Medicine; the third phase examines the role of organizational culture as a critical variable for effective strategy implementation in the same setting.
Insight into Healthcare Information Technology Adoption and Evaluation
Authors: Wiggins Carla , Trimmer Ken
Published: October 2011

This chapter is a longitudinal review of Health Information Technology (HIT) research. The adoption, implementation, and use of HIT continue to present challenges to organizations, the research community, and to society in general. The first place that new waves of thought are often aired is at conferences. This chapter explores the evolution taking place in this domain by looking back through the years over work presented at the longest standing international conference track focused on adoption, implementation, diffusion, and evaluation of health Information Technology.
Assessing Online Learning Pedagogically and Andragogically
Authors: Wang Victor X.
Published: October 2011

Online learning occurs among traditional age students as well as among non-traditional age students. Because traditional age students learn differently from non-traditional age students, especially in the virtual environment, educators are required to employ either pedagogical or andragogical assessment methods accordingly. Using pedagogical assessment methods may not work for non-traditional age learners. Likewise, trying to apply andragogical methods to traditional age students may not work for younger learners. Available assessment methods such as objectively-scored tests, subjectively-rated tests and criterion referenced tests will be discussed as well as a new form of assessment-learner self-evaluation. To use the proper tests to assess online learning, educators are required to possess both pedagogical and andragogical knowledge.
Utilizing a Virtual Environment for Academic Advising
Authors: Golubski Pamela M.
Published: October 2011

Students, regardless of age, need access to an assigned academic advisor with which they can develop a long term, mutual relationship during their college tenure. An academic advisor assists students with the difficult transition into college, and ensures that each advisee has the opportunity to gain the professional, personal, interpersonal, career, and/or academic skills necessary for college success. In the past, academic advising was viewed as a prescriptive, non developmental service; today academic advisors are making every effort to support students’ holistic growth. The development approach of advising will require an advisor to have a greater presence in the student’s life, thus meaning an increased time commitment, availability, and interaction with advisees. While traditional, face-to-face methods have been effectively used since the 1900’s to advise students, in a virtual world, more advisors are utilizing virtual and Web 2.0 technologies in an effort to educate, empower, interact, and inform students. These technologies include: Facebook, Instant Message (IM), Skype, chat sessions, electronic mailing lists, blogs, Twitter, online testing, and training.
Summarizing Teaching Approaches in the Traditional Classroom and in the Virtual Environment
Authors: Wang Victor X.
Published: October 2011

Teachers in today’s information society are required to rethink their teaching approaches to accommodate the learning needs of children and adults, either in the traditional classroom settings or the virtual environment. Logically speaking, children require instructors to teach them by using the pedagogical methods. Likewise, adults require teachers to help them learn by using andragogical approaches such as facilitation methods. When it comes to teaching children or helping adults learn in the online teaching and learning environment, it is the epistemological positions of the teachers that predetermine their instructional methods. In this chapter, the author compared and contrasted those pedagogical teaching methods with those andragogical approaches.
SLA-Aware Enterprise Service Computing
Authors: Tang Longji , Dong Jing , Zhao Yajing
Published: October 2011

There is a growing trend towards enterprise system integration across organizational and enterprise boundaries on the global Internet platform. The Enterprise Service Computing (ESC) has been adopted by more and more corporations to meet the growing demand from businesses and the global economy. However the ESC as a new distributed computing paradigm poses many challenges and issues of quality of services. For example, how is ESC compliant with the quality of service (QoS)? How do service providers guarantee services which meet service consumers’ needs as well as wants? How do both service consumers and service providers agree with QoS at runtime? In this chapter, SLA-Aware enterprise service computing is first introduced as a solution to the challenges and issues of ESC. Then, SLA-Aware ESC is defined as new architectural styles which include SLA-Aware Enterprise Service-Oriented Architecture (ESOA-SLA) and SLA-Aware Enterprise Cloud Service Architecture (ECSA-SLA). In addition, the enterprise architectural styles are specified through our extended ESOA and ECSA models. The ECSA-SLA styles include SLA-Aware cloud services, SLA-Aware cloud service consumers, SLA-Aware cloud SOA infrastructure, SLA-Aware cloud SOA management, SLA-Aware cloud SOA process and SLA-Aware SOA quality attributes. The main advantages of viewing and defining SLA-Aware ESC as an architectural style are (1) abstracting the common structure, constraints and behaviors of a family of ESC systems, such as ECSA-SLA style systems and (2) defining general design principles for the family of enterprise architectures. The design principles of ECSA-SLA systems are proposed based on the model of ECSA-SLA. Finally, we discuss the challenges of SLA-Aware ESC and suggest that the autonomic service computing, automated service computing, adaptive service computing, real-time SOA, and event-driven architecture can help to address the challenges.
Trends and Research Issues in SOA Validation
Authors: Bertolino Antonia , De Angelis Guglielmo , Sabetta Antonino , Polini Andrea
Published: October 2011

Service Oriented Architecture (SOA) is changing the way in which software applications are designed, deployed and maintained. A service-oriented application consists of the runtime composition of autonomous services that are typically owned and controlled by different organizations. This decentralization impacts on the dependability of applications that consist of dynamic services agglomerates, and challenges their validation. Different techniques can be used or combined for the verification of dependability aspects, spanning over traditional off-line testing approaches, monitoring, and on-line testing. In this chapter we discuss issues and opportunities of SOA validation, we identify three different stages for validation along the service life-cycle model, and we overview some proposed research approaches and tools. The emphasis is on on-line testing, which to us is the most peculiar stage in the SOA validation process. Finally, we claim that on-line testing is only possible within an agreed governance framework.
Performance Management of Composite Applications in Service Oriented Architectures
Authors: Dubey Vinod K. , Menascé Daniel A.
Published: October 2011

The use of Service Oriented Architectures (SOA) enables the existence of a market of service providers delivering functionally equivalent services at different Quality of Service (QoS) and cost levels. The QoS of composite applications can typically be described in terms of metrics such as response time, availability, and throughput of the services that compose the application. A global utility function of the various QoS metrics is the objective function used to determine a near-optimal selection of service providers that support the composite application. This chapter describes the architecture of a QoS Broker that manages the performance of composite applications. The broker continually monitors the utility of the applications and triggers a new service selection when the utility falls below a pre-established threshold or when a service provider fails. A proof-of-concept prototype of the QoS broker demonstrates how it maintains the average utility of the composite application above the threshold in spite of service provider failures and performance degradation.
Performability Evaluation of Web-Based Services
Authors: Martinello Magnos , Kaâniche Mohamed , Kanoun Karama
Published: October 2011

The joint evaluation of performance and dependability in a unique approach leads to the notion of performability which usually combines different analytical modeling formalisms (Markov chains, queueing models, etc.) for assessing systems behaviors in the presence of faults. This chapter presents a systematic modeling approach allowing designers of web-based services to evaluate the performability of the service provided to the users. We have developed a multi-level modeling framework for analyzing the user perceived performability. Multiple sources of service unavailability are taken into account, particularly i) hardware and software failures affecting the servers, and ii) performance degradation due to e.g. overload of servers and probability of loss. The main concepts and the feasibility of the proposed framework are illustrated using a web-based travel agency. Various analytical models and sensitivity studies are presented considering different assumptions with respect to users profiles, architecture, faults, recovery strategies, and traffic characteristics.
Approaches to Functional, Structural and Security SOA Testing
Authors: Bartolini Cesare , Bertolino Antonia , Lonetti Francesca , Marchetti Eda
Published: October 2011

In this chapter, we provide an overview of recently proposed approaches and tools for functional and structural testing of SOA services. Typically, these two classes of approaches have been considered separately. However, since they focus on different perspectives, they are generally non-conflicting and could be used in a complementary way. Accordingly, we make an attempt at such a combination, briefly showing the approach and some preliminary results of the experimentation. The combined approach provides encouraging results from the point of view of the achievements and the degree of automation obtained. A very important concern in designing and developing web services is security. In the chapter we also discuss the security testing challenges and the currently proposed solutions.
The Link in the Lesson
Authors: Fee Anthony , Budde-Sung Amanda E.K.
Published: October 2011

Video is generally seen as a passive, primarily didactic teaching method; an approach at odds with contemporary cross-cultural training which tends to emphasize highly interactive ”experiential” methods. In this chapter we draw on contemporary theories of learning to argue that video-based cross-cultural training is, in fact, more flexible than it is given credit for, and can play an important role in developing learners’ cultural intelligence. In doing this, we outline several practical and creative ways in which video can be used to develop cultural intelligence.
The Effectiveness of Streaming Media Clips in Skills Teaching
Authors: Saxon Andrew , Griffiths Sheila
Published: October 2011

In this chapter, we present a small-scale comparative study into the use of streaming media Reusable Learning Objects (RLOs) in skills teaching. We consider the use of streaming digital video clips in the teaching of complex technical skills to Fashion Design students in large class sizes, drawing a comparison with the face-to-face demonstration model in widespread use in our curriculum.
Unleashing Dormant Diversity
Authors: Budde-Sung Amanda E.K. , Fee Anthony
Published: October 2011

The chapter will begin with a discussion of the increasing diversity in today’s classrooms and the current pedagogies in higher education, and then move to the challenges of a diverse student audience, followed by the benefits of using video to meet these challenges, finally offering some practice-based suggestions on using video in the cross-cultural classroom.
Machine Learning
Authors: Gama João , de Carvalho André C.P.L.F.
Published: October 2011

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.
Calibration of Machine Learning Models
Authors: Bella Antonio , Ferri Cèsar , Hernández-Orallo José , Ramírez-Quintana María José
Published: October 2011

The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the “total” or the “average” error of the model, it is also important to know how this error is distributed and how well confidence or probability estimations are made. Many current machine learning techniques are good in overall results but have a bad distribution assessment of the error. For these cases, calibration techniques have been developed as postprocessing techniques in order to improve the probability estimation or the error distribution of an existing model. This chapter presents the most common calibration techniques and calibration measures. Both classification and regression are covered, and a taxonomy of calibration techniques is established. Special attention is given to probabilistic classifier calibration.
Classification of Web Pages Using Machine Learning Techniques
Authors: Selvakuberan K. , Devi M. Indra , Rajaram R.
Published: October 2011

The explosive growth of the Web makes it a very useful information resource to all types of users. Today, everyone accesses the Internet for various purposes and retrieving the required information within the stipulated time is the major demand from users. Also, the Internet provides millions of Web pages for each and every search term. Getting interesting and required results from the Web becomes very difficult and turning the classification of Web pages into relevant categories is the current research topic. Web page classification is the current research problem that focuses on classifying the documents into different categories, which are used by search engines for producing the result. In this chapter we focus on different machine learning techniques and how Web pages can be classified using these machine learning techniques. The automatic classification of Web pages using machine learning techniques is the most efficient way used by search engines to provide accurate results to the users. Machine learning classifiers may also be trained to preserve the personal details from unauthenticated users and for privacy preserving data mining.
Different Roles and Definitions of Spatial Data Fusion
Authors: Skogster Patrik
Published: October 2011

Geographic information is created by manipulating geographic (or spatial) data (generally known by the abbreviation geodata) in a computerized system. Geo-spatial information and geomatics are issues of modern business and research. It is essential to provide their different definitions and roles in order to get an overall picture of the issue. This article discusses about the problematic of definitions, but also the technologies and challenges within spatial data fusion.
Designing Light Weight Intrusion Detection Systems
Authors: Snášel Václav , Platoš Jan , Krömer Pavel , Abraham Ajith
Published: October 2011

Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low false alarm rate and reduced number of features. Finally, the authors present the Non-negative matrix factorization method for detecting real attacks and the performance comparison with other computational intelligence techniques.
Modelling Gene Regulatory Networks Using Computational Intelligence Techniques
Authors: Ram Ramesh , Chetty Madhu
Published: October 2011

This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.
Hybrid Meta-Heuristics Based System for Dynamic Scheduling
Authors: Madureira Ana Maria
Published: October 2011

The complexity of current computer systems has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields, e.g. robotics, artificial intelligence or biology, to find new ways of designing and managing systems. Hybridization and combination of different approaches seems to be a promising research field of computational intelligence focusing on the development of the next generation of intelligent systems. A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics to the resolution of this class of dynamic scheduling problems seems really promising. In this article, we propose a hybrid Meta-Heuristic based approach for complex scheduling with several manufacturing and assembly operations, in dynamic Extended Job-Shop environments. Some self-adaptation mechanisms are proposed.
Differential Learning Expert System in Data Management
Authors: Manjunath R.
Published: October 2011

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.
Automatic Semantic Annotation Using Machine Learning
Authors: Tang Jie , Zhang Duo , Yao Limin , Li Yi
Published: October 2011

This chapter aims to give a thorough investigation of the techniques for automatic semantic annotation. The Semantic Web provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. However, lack of annotated semantic data is a bottleneck to make the Semantic Web vision a reality. Therefore, it is indeed necessary to automate the process of semantic annotation. In the past few years, there was a rapid expansion of activities in the semantic annotation area. Many methods have been proposed for automating the annotation process. However, due to the heterogeneity and the lack of structure of the Web data, automated discovery of the targeted or unexpected knowledge information still present many challenging research problems. In this chapter, we study the problems of semantic annotation and introduce the state-of-the-art methods for dealing with the problems. We will also give a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation will be introduced as well. Finally, some emerging challenges in semantic annotation will be discussed.
Electricity Load Forecasting Using Machine Learning Techniques
Authors: Acera Manuel Martín-Merino
Published: October 2011

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.
Class Prediction in Test Sets with Shifted Distributions
Authors: Pérez Óscar , Sánchez-Montañés Manuel
Published: October 2011

Machine learning has provided powerful algorithms that automatically generate predictive models from experience. One specific technique is supervised learning, where the machine is trained to predict a desired output for each input pattern x. This chapter will focus on classification, that is, supervised learning when the output to predict is a class label. For instance predicting whether a patient in a hospital will develop cancer or not. In this example, the class label c is a variable having two possible values, “cancer” or “no cancer”, and the input pattern x is a vector containing patient data (e.g. age, gender, diet, smoking habits, etc.). In order to construct a proper predictive model, supervised learning methods require a set of examples xi together with their respective labels ci. This dataset is called the “training set”. The constructed model is then used to predict the labels of a set of new cases xj called the “test set”. In the cancer prediction example, this is the phase when the model is used to predict cancer in new patients.

One common assumption in supervised learning algorithms is that the statistical structure of the training and test datasets are the same (Hastie, Tibshirani & Friedman, 2001). That is, the test set is assumed to have the same attribute distribution p(x) and same class distribution p(c|x) as the training set. However, this is not usually the case in real applications due to different reasons. For instance, in many problems the training dataset is obtained in a specific manner that differs from the way the test dataset will be generated later. Moreover, the nature of the problem may evolve in time. These phenomena cause pTr(x, c)  pTest(x, c), which can degrade the performance of the model constructed in training.

Here we present a new algorithm that allows to re-estimate a model constructed in training using the unlabelled test patterns. We show the convergence properties of the algorithm and illustrate its performance with an artificial problem. Finally we demonstrate its strengths in a heart disease diagnosis problem where the training set is taken from a different hospital than the test set.
Bankruptcy Prediction by Supervised Machine Learning Techniques
Authors: Tsai Chih-Fong , Lu Yu-Hsin , Hsu Yu-Feng
Published: October 2011

It is very important for financial institutions which are capable of accurately predicting business failure. In literature, numbers of bankruptcy prediction models have been developed based on statistical and machine learning techniques. In particular, many machine learning techniques, such as neural networks, decision trees, etc. have shown better prediction performances than statistical ones. However, advanced machine learning techniques, such as classifier ensembles and stacked generalization have not been fully examined and compared in terms of their bankruptcy prediction performances. The aim of this chapter is to compare two different machine learning techniques, one statistical approach, two types of classifier ensembles, and three stacked generalization classifiers over three related datasets. The experimental results show that classifier ensembles by weighted voting perform the best in term of predication accuracy. On the other hand, for Type II errors on average stacked generalization and single classifiers perform better than classifier ensembles.
Bankruptcy Prediction through Artificial Intelligence
Authors: Goletsis Y. , Papaloukas C. , Exarhos Th. , Katsis C.D.
Published: October 2011

Bankruptcy prediction or corporate failure is considered a classic issue in both, academic and business communities. Bankruptcy risk is one of the most important factors (if not the most important one) to be considered when credit requests are screened or even existing debtors are evaluated. On the other hand, all potential stakeholders (shareholders, suppliers, customers, employees, creditors, auditors, etc.) have potential interest to identify if a company is on a trajectory that is tending towards failure. Commercial banks, public accounting firms and other institutional entities (e.g., bond rating agencies) appear to be the primary beneficiaries of accurate bankruptcy prediction, since they can use research results to minimize exposure to potential client failures. In addition to avoiding potentially troubled obligors, the research can also benefit in other ways. It can help in accurately assessing the credit risk of bank loan portfolios. Credit risk has been the subject of much research activity, since the regulators are acknowledging the need and are urging the banks to assess the credit risk in their portfolios. Measuring the credit risk accurately also allows banks to engineer future lending transactions, so as to achieve targeted return/risk characteristics. The other benefit of the prediction of bankruptcies is for accounting firms. If an accounting firm audits a potentially troubled firm, and misses giving a warning signal then it faces costly lawsuits (Atiya, 2001). A series of techniques have been applied in literature. Econometric / statistical methods have first appeared in literature: In late 1960’s (multiple) discriminant analysis (DA) was the dominant method; during the 1980’s logistic analysis. In the 1990’s artificial intelligence starts appearing in financial literature with neural networks (Odom & Sharda 1990) serving as an alternative to statistical methods demonstrating promising results. The goal of this chapter is therefore two-fold: First, it intends to give an overview of the artificial intelligence techniques successfully applied to the problem, ranging from the first neural network applications to recent applications of biologically inspired algorithms, such as genetic algorithms. Then, two kernel based methods, namely the Radial Basis Function Neural Networks and the Support Vector Machines are applied to the bankruptcy problem.
Ensemble of Neural Networks for Automated Cell Phenotype Image Classification
Authors: Nanni Loris , Lumini Alessandra
Published: October 2011

Subcellular location is related to the knowledge of the spatial distribution of a protein within the cell. The knowledge of the location of all proteins is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. This chapter focuses on the study of machine learning techniques for cell phenotype image classification and is aimed at pointing out some of the advantages of using a multi-classifier system instead of a stand-alone method to solve this difficult classification problem. The main problems and solutions proposed in this field are discussed and a new approach is proposed based on ensemble of neural networks trained by local and global features. Finally, the most used benchmarks for this problem are presented and an experimental comparison among several state-of-the-art approaches is reported which allows to quantify the performance improvement obtained by the approach proposed in this chapter.
Machine Learning for Brain Image Segmentation
Authors: Morra Jonathan , Tu Zhuowen , Toga Arthur , Thompson Paul
Published: October 2011

In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.
A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
Authors: Conover Adam J.
Published: October 2011

This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous” environments. Many traditional agent and swarm simulation environments divide time into discrete “ticks” where all entity behavior is synchronized to a master “world clock”. In other words, all agent behavior is governed by a single timer where all agents act and interact within deterministic time intervals. This discrete timing mechanism produces a somewhat restricted and artificial model of autonomous agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents should also have “temporal autonomy” in order to interact realistically. This chapter focuses on the exploration of a grid of specially embedded, message-passing agents, where each message represents the communication of a core “belief”. Here, we focus our attention on the how the temporal variance of belief propagation from individual agents induces emergent and dynamic effects on a global population.
Annotating Images by Mining Image Search
Authors: Wang Xin-Jing , Zhang Lei , Li Xirong , Ma Wei-Ying
Published: October 2011

Although it has been studied for years by computer vision and machine learning communities, image annotation is still far from practical. In this chapter, the authors propose a novel attempt of modeless image annotation, which investigates how effective a data-driven approach can be, and suggest annotating an uncaptioned image by mining its search results. The authors collected 2.4 million images with their surrounding texts from a few photo forum Web sites as our database to support this data-driven approach. The entire process contains three steps: (1) the search process to discover visually and semantically similar search results; (2) the mining process to discover salient terms from textual descriptions of the search results; and (3) the annotation rejection process to filter noisy terms yielded by step 2. To ensure real time annotation, two key techniques are leveraged – one is to map the high dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training dataset is required, our proposed approach enables annotating with unlimited vocabulary, and is highly scalable and robust to outliers. Experimental results on real Web images show the effectiveness and efficiency of the proposed algorithm.
Computational Intelligence for Functional Testing
Authors: Lam C. Peng
Published: October 2011

Software testing is primarily a technique for achieving some degree of software quality and to gain consumer confidence. It accounts for 50% -75% of development cost. Test case design supports effective testing but is still a human centered and labour-intensive task. The Unified Modelling language (UML) is the de-facto industrial standard for specifying software system and techniques for automatic test case generation from UML models are very much needed. While extensive research has explored the use of meta-heuristics in structural testing, few have involved its use in functional testing, particularly with respect to UML. This chapter details an approach that incorporates an anti-Ant Colony Optimisation algorithm for the automatic generation of test scenarios directly from UML Activity Diagrams, thus providing a seamless progression from design to generation of test scenarios. Owing to its anti-ant behaviour, the approach generates non-redundant test scenarios.
FOL Learning for Knowledge Discovery in Documents
Authors: Ferilli Stefano , Esposito Floriana , Biba Marenglen , Basile Teresa M.A. , Di Mauro Nicola
Published: October 2011

This chapter proposes the application of machine learning techniques, based on first-order logic as a representation language, to the real-world application domain of document processing. First, the tasks and problems involved in document processing are presented, along with the prototypical system DOMINUS and its architecture, whose components are aimed at facing these issues. Then, a closer look is provided for the learning component of the system, and the two sub-systems that are in charge of performing supervised and unsupervised learning as a support to the system performance. Finally, some experiments are reported that assess the quality of the learning performance. This is intended to prove to researchers and practitioners of the field that first-order logic learning can be a viable solution to tackle the domain complexity, and to solve problems such as incremental evolution of the document repository.
Computer-Based Learning Environments with Emotional Agents
Authors: Gorga Dorel , Schneider Daniel K.
Published: October 2011

The purpose of this contribution is to discuss conceptual issues and challenges related to the integration of emotional agents in the design of computer-based learning environments and to propose a framework for the discussion of future research. We review some emotion theories and computational models that have been developed in cognitive science and Artificial Intelligence (AI). We then will discuss some basic principles pertaining to motivation and emotion in instructional design. Grounded on these principles, we then shall present the state of the art of integrating emotions into the design of educational systems, and notably examine how to create intelligent emotional agents that enhance interaction with users. We will introduce the concept of “socio-emotional climate” as an evaluative indicator of the diversity of desirable interactions within a computer-based learning environment. We formulate the conjecture that a socio-emotional climate capable of enhancing learner motivation, self-assessment and self-motivation could be developed through the use of various socio-emotional agents.
Improving Automated Planning with Machine Learning
Authors: Fernández Arregui Susana , Jiménez Celorrio Sergio , de la Rosa Turbides Tomás
Published: October 2011

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.
Diagnostic Support Systems and Computational Intelligence
Authors: Mougiakakou Stavroula , Valavanis Ioannis , Nikita Alexandra , Nikita Konstantina S.
Published: October 2011

Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that promise to increase the efficiency of physicians during their clinical practice. This chapter provides a brief overview of the use of computational intelligence methods in the design and development of DSSs aimed at the differential diagnosis of hepatic lesions from Computed Tomography (CT) images. Furthermore, examples of DSSs developed by our research team for supporting the diagnosis of focal liver lesions from non-enhanced CT images are presented.
Computerised Decision Support for Women’s Health Informatics
Authors: Parry David
Published: October 2011

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.
Machine Learning in Personalized Anemia Treatment
Authors: Gaweda Adam E.
Published: October 2011

This chapter presents application of reinforcement learning to drug dosing personalization in treatment of chronic conditions. Reinforcement learning is a machine learning paradigm that mimics the trialand- error skill acquisition typical for humans and animals. In treatment of chronic illnesses, finding the optimal dose amount for an individual is also a process that is usually based on trial-and-error. In this chapter, the author focuses on the challenge of personalized anemia treatment with recombinant human erythropoietin. The author demonstrates the application of a standard reinforcement learning method, called Q-learning, to guide the physician in selecting the optimal erythropoietin dose. The author further addresses the issue of random exploration in Q-learning from the drug dosing perspective and proposes a “smart” exploration method. Finally, the author performs computer simulations to compare the outcomes from reinforcement learning-based anemia treatment to those achieved by a standard dosing protocol used at a dialysis unit.
Data Warehousing and Decision Support in Mobile Wireless Patient Monitoring
Authors: Nag Barin N. , Siegal Mark
Published: October 2011

The recent advances in wireless communication technologies have made possible the development of wireless systems for monitoring the health and disease status of patients, in both in-patient hospital settings and outside. The volume of patient monitoring data requires Data Warehousing technologies for storage intended for analysis. The analysis is performed by Decision Support Systems (DSS) that provide clinical diagnoses and treatment methodology consistent with the urgency. The clinical DSS is critical in the analysis of a volume of data beyond the capabilities of a healthcare professional, and is effective in reducing workload, saving money, and providing better care for patients. This chapter also analyzes the technical aspects of the process.
Ethology-Based Approximate Adaptive Learning
Authors: Peters James F. , Shahfar Shabnam
Published: October 2011

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.