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Computational Intelligence for Information Technology Project Management

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DOI: 10.4018/978-1-60960-818-7.ch606
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MLA

Hammell, Robert J., Julie Hoksbergen, James Wood and Mark Christensen. "Computational Intelligence for Information Technology Project Management." Machine Learning: Concepts, Methodologies, Tools and Applications. IGI Global, 2012. 1601-1624. Web. 30 Sep. 2014. doi:10.4018/978-1-60960-818-7.ch606

APA

Hammell, R. J., Hoksbergen, J., Wood, J., & Christensen, M. (2012). Computational Intelligence for Information Technology Project Management. In Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 1601-1624). Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960-818-7.ch606

Chicago

Hammell, Robert J., Julie Hoksbergen, James Wood and Mark Christensen. "Computational Intelligence for Information Technology Project Management." In Machine Learning: Concepts, Methodologies, Tools and Applications, 1601-1624 (2012), accessed September 30, 2014. doi:10.4018/978-1-60960-818-7.ch606

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Abstract

With the growing complexity of information technology (IT) projects, the management of these projects is proving to be a daunting task. The magnitude of this problem is underscored by the assertion that approximately 70% of IT projects fail to meet their objectives (Lewis, 2007). Computational intelligence (CI) is an area of research focused on developing intelligent systems to help with complex problems. Specifically, CI seeks to integrate techniques and methodologies to assist in problem domains in which information, data and perhaps even the problem itself are vague, approximate, and uncertain. It would seem that research aimed at leveraging the power of CI against IT project management problems is critical if IT project success rates are to be improved. This work examines the core CI technologies – fuzzy logic, neural networks, and genetic algorithms – and looks at current and potential future applications of these techniques to assist IT project managers.
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Introduction

While the concept of project management has been around for centuries, the methodologies with which we are most familiar are not nearly that old. Tools developed during World War I and II, such as Gantt charts, PERT charts, and network diagrams laid the groundwork for the primary project management techniques we use today (Sisk, 1998). Even with these tools and the powerful information technologies at our disposal, successful performance of the multiple steps in project management is still a difficult task. The fact that approximately 70% of information technology (IT) projects fail to meet their objectives further emphasizes the difficulties throughout the life of a project (Lewis, 2007).

The sheer complexity of managing an IT project is a large part of the problem and has been cited as a primary cause in the failure of such projects (Daniels & LaMarsh, 2007). The IT industry is constantly changing which makes it difficult for users to envision and develop potential tools based on the newest technologies. Additionally, project management is still a relatively new science that is continuously evolving. Throughout the life cycle of an IT project, project leaders and their respective teams encounter many situations where they are faced with making important decisions which could determine the success or failure of the project. Often times many of these decisions are made with insufficient tools or resources. The lack of sophisticated IT tools directed specifically toward helping the project manager may lead to decreased performance in all aspects of project management. Problems are many, but examples include outdated or incomplete information for problem identification, planning and execution; basing decisions on past project experiences which may not apply to the current project; difficulty in monitoring the project due to inaccurate knowledge of the current project status; and perhaps even problems with capturing lessons learned from a project, regardless of whether it is a success or failure.

Computational intelligence (CI) is an area of research focused on developing intelligent systems to help with complex problems. Specifically, CI seeks to integrate techniques and methodologies to assist in problem domains in which information, data and perhaps even the problem itself are vague, approximate, and uncertain. The information technology project management environment is certainly one of these problem domains. With the ever increasing complexity of today’s IT projects, the need for intelligent tools, processes, methods, and systems to assist in the management of these projects is also growing. While some work has been done in applying CI to IT project management problems, it appears that efforts in this area are still in their infancy. We submit that research aimed at leveraging the power of CI toward the problems in IT project management will be vital in the quest to improve IT project success rates.

The next section defines computational intelligence with respect to the work presented here. The core CI technologies that we will explore are also introduced and explained. A broad survey of current work related to applying these CI technologies to the field of IT project management is then given. We end with a section that describes potential future applications of CI to IT project management.

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Chapter 1
Brian Whitworth, Hokyoung Ryu
Over 30 years ago, TV shows from The Jetsons to Star Trek suggested that by the millennium’s end computers would read, talk, recognize, walk... Sample PDF
A Comparison of Human and Computer Information Processing
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Chapter 2
Machine Learning  (pages 13-22)
João Gama, André C.P.L.F. de Carvalho
Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web... Sample PDF
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Chapter 3
Diego Liberati
In dealing with information it often turns out that one has to face a huge amount of data, often not completely homogeneous and often without an... Sample PDF
Machine Learning Through Data Mining
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Chapter 4
Antonio Bella, Cèsar Ferri, José Hernández-Orallo, María José Ramírez-Quintana
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Calibration of Machine Learning Models
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Chapter 5
K. Selvakuberan, M. Indra Devi, R. Rajaram
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Classification of Web Pages Using Machine Learning Techniques
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Chapter 6
G. N. Marichal, A. Hernández, E. J. González, L. Acosta, J. L. Saorin
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3D Modelling and Artificial Intelligence: A Descriptive Overview
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Chapter 7
Chris Groeneboer, Monika Whitney
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An Overview of Knowledge Translation
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Chapter 8
João José Neto
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Adaptive Technology and Its Applications
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Chapter 9
M. L. Gavrilova
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Adaptive Algorithms for Intelligent Geometric Computing
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Chapter 10
Patrik Skogster
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Chapter 11
Xenia Naidenova
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Chapter 12
Janusz A. Starzyk
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Chapter 13
David Gibson
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Chapter 14
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Chapter 15
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Information Hiding by Machine Learning: A Method of Key Generation for Information Extracting Using Neural Network
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Chapter 16
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Rule Engines and Agent-Based Systems
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Chapter 17
John Seiffertt, Donald C. Wunsch II
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Chapter 18
Shu-Chiang Lin
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Chapter 19
Lei Xu, Shun-ichi Amari
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Chapter 20
M. Mohammadian
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Chapter 21
Marcelo Keese Albertini, Rodrigo Fernandes de Mello
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Chapter 22
Christian Hillbrand
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Chapter 23
Václav Snášel, Jan Platoš, Pavel Krömer, Ajith Abraham
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Chapter 24
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Chapter 25
Ramesh Ram, Madhu Chetty
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Modelling Gene Regulatory Networks Using Computational Intelligence Techniques
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Chapter 26
Ramakanta Mohanty, V. Ravi, M. R. Patra
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Chapter 27
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Chapter 28
Gabriella Carrozza, Roberto Natella
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Chapter 29
Marco Vannucci, Valentina Colla, Silvia Cateni, Mirko Sgarbi
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Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks
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Chapter 30
Ana Maria Madureira
The complexity of current computer systems has led the software engineering, distributed systems and management communities to look for inspiration... Sample PDF
Hybrid Meta-Heuristics Based System for Dynamic Scheduling
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Chapter 31
R. Manjunath
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Chapter 32
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Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making
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Chapter 33
Isak Taksa, Sarah Zelikovitz, Amanda Spink
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Chapter 34
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Machine Learning in Morphological Segmentation
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Chapter 35
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Machine Learning Techniques for Network Intrusion Detection
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Chapter 36
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A Machine Learning Based Meta-Scheduler for Multi-Core Processors
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Chapter 37
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Chapter 38
Manuel Martín-Merino Acera
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Chapter 39
Isak Taksa, Sarah Zelikovitz, Amanda Spink
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Non-Topical Classification of Query Logs Using Background Knowledge
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Chapter 40
Yoshihiro Yamanishi, Hisashi Kashima
In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this... Sample PDF
Prediction of Compound-protein Interactions with Machine Learning Methods
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Chapter 41
Kensuke Naoe, Hideyasu Sasaki, Yoshiyasu Takefuji
The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One... Sample PDF
Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services
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Chapter 42
Tarek Helmy
The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion... Sample PDF
Adaptive Ensemble Multi-Agent Based Intrusion Detection Model
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Chapter 43
Óscar Pérez, Manuel Sánchez-Montañés
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Chapter 44
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Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study
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Chapter 45
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Bankruptcy Prediction through Artificial Intelligence
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Chapter 46
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Machine Learning and Data Mining in Bioinformatics
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Chapter 47
Albert Ali Salah
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Machine Learning for Biometrics
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Chapter 48
Jesmin Nahar, Kevin S. Tickle, A. B.M. Shawkat Ali
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Chapter 49
Geraldo Braz Júnior, Leonardo de Oliveira Martins, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva
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Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features
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Chapter 50
Loris Nanni, Alessandra Lumini
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Ensemble of Neural Networks for Automated Cell Phenotype Image Classification
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Chapter 51
Nikolaos Giannakeas, Dimitrios I. Fotiadis
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Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images
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Chapter 52
Abhilash Alexander Miranda, Olivier Caelen, Gianluca Bontempi
This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular... Sample PDF
Machine Learning for Automated Polyp Detection in Computed Tomography Colonography
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Chapter 53
Jonathan Morra, Zhuowen Tu, Arthur Toga, Paul Thompson
In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images... Sample PDF
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Chapter 54
Guo-Zheng Li
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Chapter 55
Adam J. Conover
This chapter presents a description of ongoing experimental research into the emergent properties of multi-agent communication in “temporally... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
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Chapter 56
Adam J. Conover
This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous”... Sample PDF
A Simulation of Temporally Variant Agent Interaction via Belief Promulgation
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Chapter 57
Donat Orski
The chapter concerns a class of systems composed of operations performed with the use of resources allocated to them. In such operation systems... Sample PDF
Application of Uncertain Variables to Knowledge-Based Resource Distribution
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Chapter 58
Marco Mamei
Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications... Sample PDF
Applying Commonsense Reasoning to Place Identification
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Chapter 59
Floriana Esposito, Teresa M.A. Basile, Nicola Di Mauro, Stefano Ferilli
One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users.... Sample PDF
Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems
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Chapter 60
Nadeem Bhatti, Dieter W. Fellner
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Visual Semantic Analysis to Support Semi-Automatic Modeling of Semantic Service Descriptions
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Chapter 61
Lei Xu
Among extensive studies on radial basis function (RBF), one stream consists of those on normalized RBF (NRBF) and extensions. Within a probability... Sample PDF
Learning Algorithms for RBF Functions and Subspace Based Functions
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Chapter 62
Xin-Jing Wang, Lei Zhang, Xirong Li, Wei-Ying Ma
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Annotating Images by Mining Image Search
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Chapter 63
Artem A. Lenskiy, Jong-Soo Lee
The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention.... Sample PDF
Machine Learning for Visual Navigation of Unmanned Ground Vehicles
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Chapter 64
Gang Wang, Jin-xing Hao, Jian Ma, Li-hua Huang
Credit scoring is an important finance activity. Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for this... Sample PDF
Empirical Evaluation of Ensemble Learning for Credit Scoring
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Chapter 65
Mehdi Najjar
Despite a growing development of virtual laboratories which use the advantages of multimedia and the Internet for distance education, learning by... Sample PDF
A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments
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Chapter 66
Max E. Stachura, Elena V. Astapova, Hui-Lien Tung, Donald A. Sofge, James Grayson, Margo Bergman, Joseph Wood
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Conservation of Information (COI): Geospatial and Operational Developments in E-Health and Telemedicine for Virtual and Rural Communities
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Chapter 67
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Computational Intelligence for Functional Testing
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Chapter 68
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
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Chapter 69
Farid Meziane, Sunil Vadera
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Artificial Intelligence in Software Engineering: Current Developments and Future Prospects
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Chapter 70
Stefano Ferilli, Floriana Esposito, Marenglen Biba, Teresa M.A. Basile, Nicola Di Mauro
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FOL Learning for Knowledge Discovery in Documents
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Chapter 71
Dorel Gorga, Daniel K. Schneider
The purpose of this contribution is to discuss conceptual issues and challenges related to the integration of emotional agents in the design of... Sample PDF
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Chapter 72
Anthony G. Francis Jr., Manish Mehta, Ashwin Ram
Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while... Sample PDF
Emotional Memory and Adaptive Personalities
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Chapter 73
Giovanni Vincenti, James Braman
Emotions influence our everyday lives, guiding and misguiding us. They lead us to happiness and love, but also to irrational acts. Artificial... Sample PDF
Hybrid Emotionally Aware Mediated Multiagency
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Chapter 74
Cyril Laurier, Perfecto Herrera
Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling... Sample PDF
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Chapter 75
Susana Fernández Arregui, Sergio Jiménez Celorrio, Tomás de la Rosa Turbides
This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have... Sample PDF
Improving Automated Planning with Machine Learning
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Chapter 76
Stavroula Mougiakakou, Ioannis Valavanis, Alexandra Nikita, Konstantina S. Nikita
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that... Sample PDF
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
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Chapter 77
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Computer Aided Knowledge Discovery in Biomedicine
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Chapter 78
David Parry
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Computerised Decision Support for Women’s Health Informatics
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Chapter 79
Vincent Claveau
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Translation of Biomedical Terms by Inferring Rewriting Rules
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Chapter 80
Adam E. Gaweda
This chapter presents application of reinforcement learning to drug dosing personalization in treatment of chronic conditions. Reinforcement... Sample PDF
Machine Learning in Personalized Anemia Treatment
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Chapter 81
Ahsan H. Khandoker
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An Intelligent Algorithm for Home Sleep Apnoea Test Device
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Chapter 82
Shuxing Zhang
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Application of Machine Leaning in Drug Discovery and Development
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Chapter 83
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Chapter 84
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Artificial Intelligence and Rubble-Mound Breakwater Stability
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Chapter 85
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Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques
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Chapter 86
Ioanna Stamatopoulou, Maria Fasli, Petros Kefalas
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Introducing AI and IA into a Non Computer Science Graduate Programme
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Chapter 87
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This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic... Sample PDF
Distributed Intelligence for Constructing Economic Models
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Chapter 88
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Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within... Sample PDF
A Computational Intelligence Approach to Supply Chain Demand Forecasting
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Chapter 89
Diana F. Adamatti, Marilton S. de Aguiar
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Artificial Intelligence Applied to Natural Resources Management
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Chapter 90
Bueno Borges de Souza, Li Weigang, Antonio Marcio Ferreira Crespo, Victor Rafael Rezende Celestino
This work describes a decision making support system with Graph Theory and Artificial Intelligence methodologies applied to the Brazilian Air... Sample PDF
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Chapter 91
Robert J. Hammell, Julie Hoksbergen, James Wood, Mark Christensen
With the growing complexity of information technology (IT) projects, the management of these projects is proving to be a daunting task. The... Sample PDF
Computational Intelligence for Information Technology Project Management
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Chapter 92
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
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Chapter 93
Barin N. Nag, Mark Siegal
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Chapter 94
Réal Carbonneau, Rustam Vahidov, Kevin Laframboise
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Forecasting Supply Chain Demand Using Machine Learning Algorithms
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Chapter 95
Jie Du, Roy Rada
This chapter presents the case for knowledge-based machine learning in financial investing. Machine learning here, while it will exploit knowledge... Sample PDF
Machine Learning and Financial Investing
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Chapter 96
Frank Wimberly, David Danks, Clark Glymour, Tianjiao Chu
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Chapter 97
Witold Pedrycz
Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity.... Sample PDF
Granular Computing and Human-Centricity in Computational Intelligence
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Chapter 98
A. Pablo Iannone
This chapter asks: What is information overload? At what levels of existence does it occur? Are there any features common to information overload at... Sample PDF
Walking the Information Overload Tightrope
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Chapter 99
Antoni Gomila, Alberto Amengual
In this chapter we raise some of the moral issues involved in the current development of robotic autonomous agents. Starting from the connection... Sample PDF
Moral Emotions for Autonomous Agents
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Chapter 100
John P. Sullins
This chapter will argue that artificial agents created or synthesized by technologies such as artificial life (ALife), artificial intelligence (AI)... Sample PDF
Artificial Moral Agency in Technoethics
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Chapter 101
C. Gros
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Chapter 102
David Casacuberta , Saray Ayala, Jordi Vallverdú
After several decades of success in different areas and numerous effective applications, algorithmic Artificial Intelligence has revealed its... Sample PDF
Embodying Cognition: A Morphological Perspective
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Chapter 103
Mehdi Najjar, André Mayers
Encouraging results of last years in the field of knowledge representation within virtual learning environments confirms that artificial... Sample PDF
A Cognitive Computational Knowledge Representation Theory
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Chapter 104
Thomas S. Barbalet
Inspired by observing bacterial growth in agar and by the transfer of information through simple agar simulations, the cognitive simulation of Noble... Sample PDF
Noble Ape’s Cognitive Simulation: From Agar to Dreaming and Beyond
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Chapter 105
James F. Peters, Shabnam Shahfar
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... Sample PDF
Ethology-Based Approximate Adaptive Learning: A Near Set Approach
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Chapter 106
Abdelhamid Bouchachia
Recently the field of machine learning, pattern recognition, and data mining has witnessed a new research stream that is learning with partial... Sample PDF
Learning with Partial Supervision
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Chapter 107
Michael Schmuker, Gisbert Schneider
The purpose of the olfactory system is to encode and classify odorants. Hence, its circuits have likely evolved to cope with this task in an... Sample PDF
Brain-Like Processing and Classification of Chemical Data: An Approach Inspired by the Sense of Smell
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Chapter 108
Ali Dogru, Pinar Senkul, Ozgur Kaya
The amazing evolution fuelled by the introduction of the computational element has already changed our lives and continues to do so. Initially, the... Sample PDF
Modern Approaches to Software Engineering in the Compositional Era
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Chapter 109
Andrew K.C. Wong, Yang Wang, Gary C.L. Li
A basic task of machine learning and data mining is to automatically uncover patterns that reflect regularities in a data set. When dealing with a... Sample PDF
Pattern Discovery as Event Association
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Chapter 110
Lung-Hsiang Wong, Chee-Kit Looi
One major direction in research on technology-enabled learning systems revolves round the notion of generating optimal learning pathways. Two... Sample PDF
A Survey of Optimized Learning Pathway Planning and Assessment Paper Generation with Swarm Intelligence
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Chapter 111
Nik Bessis, Eleana Asimakopoulou, Peter Norrington, Suresh Thomas, Ravi Varaganti
Much work is underway within the broad next generation technologies community on issues associated with the development of services to support... Sample PDF
A Next Generation Technology Victim Location and Low Level Assessment Framework for Occupational Disasters Caused by Natural Hazards
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Chapter 112
Siu-Yeung Cho, Teik-Toe Teoh, Yok-Yen Nguwi
Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face... Sample PDF
Facial Expression Analysis by Machine Learning
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Chapter 113
Zhiyong Wang, Dagan Feng
Visual information has been immensely used in various domains such as web, education, health, and digital libraries, due to the advancements of... Sample PDF
Discovering Semantics from Visual Information
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Chapter 114
Eduardo Romero, Fabio González
This chapter introduces the reader into the main topics covered by the book: biomedical images, biomedical image analysis and machine learning. The... Sample PDF
From Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning
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Chapter 115
C. Ugwu, N. L Onyejegbu, I. C Obagbuwa
Healthcare delivery in African nations has long been a worldwide issue, which is why the United Nations and World Health Organization seek for ways... Sample PDF
The Application of Machine Learning Technique for Malaria Diagnosis
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Chapter 116
G.M. Shafiullah, Adam Thompson, Peter J. Wolfs, A.B.M. Shawkat Ali
Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health... Sample PDF
Application of Machine Learning Techniques for Railway Health Monitoring
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Chapter 117
A. Maffezzoli, E. Wanke
In the present chapter authors want to expose new insights in the field of Computational Neuroscience at regard to the study of neuronal networks... Sample PDF
Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach
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Chapter 118
Mohamed M. Mostafa
The per capita Ecological Footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the... Sample PDF
Modeling the Ecological Footprint of Nations via Evolutionary Computation and Machine Learning Models
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Chapter 119
Gulden Uchyigit
The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called... Sample PDF
Adaptive Interaction for Mass Customisation
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Chapter 120
Ruket Çakici
Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for... Sample PDF
Dependency Parsing: Recent Advances
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