bluuebunny/crossref_metadata_2025 huggingface dataset:
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
Authors: Hillbrand Christian
Published: October 2011

The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.
A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management
Authors: Guo Ying , Li Rongxin
Published: October 2011

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this chapter the authors explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. The authors apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable. They test the robustness of their algorithm by applying it to two separate systems, one with indirect feedback and one with direct feedback from local load agents. Results show that their method is not only adaptive to multiple systems, but is also able to find the optimal balance between both system stability and energy cost.
A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems
Authors: Carrozza Gabriella , Natella Roberto
Published: October 2011

This paper proposes an approach to software faults diagnosis in complex fault tolerant systems, encompassing the phases of error detection, fault location, and system recovery. Errors are detected in the first phase, exploiting the operating system support. Faults are identified during the location phase, through a machine learning based approach. Then, the best recovery action is triggered once the fault is located. Feedback actions are also used during the location phase to improve detection quality over time. A real world application from the Air Traffic Control field has been used as case study for evaluating the proposed approach. Experimental results, achieved by means of fault injection, show that the diagnosis engine is able to diagnose faults with high accuracy and at a low overhead.
Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making
Authors: Chohra Amine , Kanaoui Nadia , Amarger Véronique , Madani Kurosh
Published: October 2011

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.
Prediction of Compound-protein Interactions with Machine Learning Methods
Authors: Yamanishi Yoshihiro , Kashima Hisashi
Published: October 2011

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.
Adaptive Ensemble Multi-Agent Based Intrusion Detection Model
Authors: Helmy Tarek
Published: October 2011

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 detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.
Machine Learning for Biometrics
Authors: Salah Albert Ali
Published: October 2011

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. The author focuses on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, the author hopes to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.
Pattern Discovery from Biological Data
Authors: Nahar Jesmin , Tickle Kevin S. , Shawkat Ali A. B.M.
Published: October 2011

Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate survival time of a heart disease patient, or recognize uncommon disease characteristics which suddenly appear. Currently there is an explosion in biological data available in the data bases. But information extraction and true open access to data are require time to resolve issues such as ethical clearance. The emergence of novel IT technologies allows health practitioners to facilitate the comprehensive analyses of medical images, genomes, transcriptomes, and proteomes in health and disease. The information that is extracted from such technologies may soon exert a dramatic change in the pace of medical research and impact considerably on the care of patients. The current research will review the existing technologies being used in heart and cancer research. Finally this research will provide some possible solutions to overcome the limitations of existing technologies. In summary the primary objective of this research is to investigate how existing modern machine learning techniques (with their strength and limitations) are being used in the indent of heartbeat related disease and the early detection of cancer in patients. After an extensive literature review these are the objectives chosen: to develop a new approach to find the association between diseases such as high blood pressure, stroke and heartbeat, to propose an improved feature selection method to analyze huge images and microarray databases for machine learning algorithms in cancer research, to find an automatic distance function selection method for clustering tasks, to discover the most significant risk factors for specific cancers, and to determine the preventive factors for specific cancers that are aligned with the most significant risk factors. Therefore we propose a research plan to attain these objectives within this chapter. The possible solutions of the above objectives are: new heartbeat identification techniques show promising association with the heartbeat patterns and diseases, sensitivity based feature selection methods will be applied to early cancer patient classification, meta learning approaches will be adopted in clustering algorithms to select an automatic distance function, and Apriori algorithm will be applied to discover the significant risks and preventive factors for specific cancers. We expect this research will add significant contributions to the medical professional to enable more accurate diagnosis and better patient care. It will also contribute in other area such as biomedical modeling, medical image analysis and early diseases warning.
Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features
Authors: Júnior Geraldo Braz , de Oliveira Martins Leonardo , Silva Aristófanes Corrêa , de Paiva Anselmo Cardoso
Published: October 2011

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).
Machine Learning for Automated Polyp Detection in Computed Tomography Colonography
Authors: Miranda Abhilash Alexander , Caelen Olivier , Bontempi Gianluca
Published: October 2011

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.
A Simulation of Temporally Variant Agent Interaction via Passive Inquiry
Authors: Conover Adam J.
Published: October 2011

This chapter presents a description of ongoing experimental research into 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. Part I of this two-part series focuses on an exploration of the effects of incremental migration of John Conway’s “Game of Life” form a simple cellular automata simulation to a framework for the exploration of spatially embedded agents.
Application of Uncertain Variables to Knowledge-Based Resource Distribution
Authors: Orski Donat
Published: October 2011

The chapter concerns a class of systems composed of operations performed with the use of resources allocated to them. In such operation systems, each operation is characterized by its execution time depending on the amount of a resource allocated to the operation. The decision problem consists in distributing a limited amount of a resource among operations in an optimal way, that is, in finding an optimal resource allocation. Classical mathematical models of operation systems are widely used in computer supported projects or production management, allowing optimal decision making in deterministic, well-investigated environments. In the knowledge-based approach considered in this chapter, the execution time of each operation is described in a nondeterministic way, by an inequality containing an unknown parameter, and all the unknown parameters are assumed to be values of uncertain variables characterized by experts. Mathematical models comprising such two-level uncertainty are useful in designing knowledge-based decision support systems for uncertain environments. The purpose of this chapter is to present a review of problems and algorithms developed in recent years, and to show new results, possible extensions and challenges, thus providing a description of a state-of-the-art in the field of resource distribution based on the uncertain variables.
Empirical Evaluation of Ensemble Learning for Credit Scoring
Authors: Wang Gang , Hao Jin-xing , Ma Jian , Huang Li-hua
Published: October 2011

Credit scoring is an important finance activity. Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for this topic. But different techniques have different advantages and disadvantages on different datasets. Recent studies draw no consistent conclusions to show that one technique is superior to the other, while they suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this study, we conduct an empirical evaluation of the performance of three popular ensemble methods, i.e., bagging, boosting, and stacking, based on four base learners, i.e., Logistic Regression Analysis (LRA), Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The experiment uses the credit dataset including 239 companies’ financial records from China, collected by the Industrial and Commercial Bank of China. Results reveal that ensemble learning can substantially improve individual base learners. Stacking, in our experiments, gets the best performance in terms of all six performance indicators, i.e., type I error, type II error, average accuracy, precision, recall, and F-value.
Conservation of Information (COI)
Authors: Stachura Max E. , Astapova Elena V. , Tung Hui-Lien , Sofge Donald A. , Grayson James , Bergman Margo , Wood Joseph
Published: October 2011

The authors review telemedicine and e-health from an organizational perspective. To evaluate their effectiveness, they review organizational and system theory along with field and laboratory results. Theory of the conservation of information (COI) provides the means to study tradeoffs across space and over time as telemedicine and e-health management make operational decisions for virtual communities users. With the authors’ three case studies, they evaluate COI for telemedicine and e-health networks operating in the state of Georgia. After analyzing the case studies with COI, the authors close with a review of future trends that includes an interaction rate equation, an agent-based model (ABM) using natural selection (machine learning), and a Monte Carlo simulation of return on investments (ROI).
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
Authors: Lucinska Malgorzata , Wierzchon Slawomir T.
Published: October 2011

Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.
Artificial Intelligence in Software Engineering
Authors: Meziane Farid , Vadera Sunil
Published: October 2011

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.
Emotional Memory and Adaptive Personalities
Authors: Francis Jr. Anthony G. , Mehta Manish , Ram Ashwin
Published: October 2011

Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while maintaining a consistent personality. For short-term interactions in restricted environments, scripting and state machine techniques can create agents with emotion and personality, but these methods are labor intensive, hard to extend, and brittle in new environments. Fortunately, research in memory, emotion and personality in humans and animals points to a solution to this problem. Emotions focus an animal’s attention on things it needs to care about, and strong emotions trigger enhanced formation of memory, enabling the animal to adapt its emotional response to the objects and situations in its environment. In humans this process becomes reflective: emotional stress or frustration can trigger re-evaluating past behavior with respect to personal standards, which in turn can lead to setting new strategies or goals. To aid the authoring of adaptive agents, we present an artificial intelligence model inspired by these psychological results in which an emotion model triggers case-based emotional preference learning and behavioral adaptation guided by personality models. Our tests of this model on robot pets and embodied characters show that emotional adaptation can extend the range and increase the behavioral sophistication of an agent without the need for authoring additional hand-crafted behaviors.
Translation of Biomedical Terms by Inferring Rewriting Rules
Authors: Claveau Vincent
Published: October 2011

This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This approach relies on a machine learning process able to infer rewriting rules from examples, that is, from a list of paired terms in two studied languages. Any new term is then simply translated by applying the rewriting rules to it. When different translations are produced by conflicting rewriting rules, we use language modeling to single out the best candidate. The experiments reported here show that this technique yields very good results for different language pairs (including Czech, English, French, Italian, Portuguese, Spanish and even Russian). The author also shows how this translation technique could be used in a cross-language information retrieval task and thus complete the dictionary-based existing approaches.
An Intelligent Algorithm for Home Sleep Apnoea Test Device
Authors: Khandoker Ahsan H.
Published: October 2011

In this chapter authors try to develop a system for Sleep apnea with the help of machine learning algorithms using ECG signals.  The application of an intelligent machine learning technique (Support Vector Machines, SVM) to diagnose the patients with sleep apnea syndrome using Electrocardiogram (ECG) signal.  Sleep apnea syndrome is a medical condition caused by sleep apnea which is defined as the cessation of breathing for short periods during sleep.
Balance Modelling and Implementation of Flow Balance for Application in Air Traffic Management
Authors: Borges de Souza Bueno , Weigang Li , Ferreira Crespo Antonio Marcio , Rezende Celestino Victor Rafael
Published: October 2011

This work describes a decision making support system with Graph Theory and Artificial Intelligence methodologies applied to the Brazilian Air Traffic Flow Management. It consists of a flow management model based on graphs with heuristic adaptations for the dynamic regulation of the air traffic flow. The model lays the foundation of the architecture of the Flow Balancing Model (FBM) which integrates the Distributed Decision Support System applied to the Tactical Management of the Traffic Flow (SISCONFLUX), under development, and has the objective of improving the national airspace management. The FBM was proposed to give support to the system in operation at the First Air Defence and Air Traffic Control Integrated Centre (CINDACTA I), by providing additional information to the process applied by the controllers, in order to mitigate the workload and improve the results of their actions. Using flow maximization techniques adapted from Graph Theory, FBM was developed as a model of analysis which determines the separation time between departures from terminals integrating the Brasilia Flight Information Region (FIR-BS), and distributes the slack capacity along the controlled airspace, in order to prevent or reduce traffic congestion in various sectors of FIR-BS. The FBM gives support to traffic flow regulation, assisting the controllers and other units within the SISCONFLUX.
Computational Intelligence for Information Technology Project Management
Authors: Hammell Robert J. , Hoksbergen Julie , Wood James , Christensen Mark
Published: October 2011

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.
Forecasting Supply Chain Demand Using Machine Learning Algorithms
Authors: Carbonneau Réal , Vahidov Rustam , Laframboise Kevin
Published: October 2011

Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses. One of the crucial determinants of effective supply chain management is the ability to recognize customer demand patterns and react accordingly to the changes in face of intense competition. Thus the ability to adequately predict demand by the participants in a supply chain is vital to the survival of businesses. Demand prediction is aggravated by the fact that communication patterns between participants that emerge in a supply chain tend to distort the original consumer’s demand and create high levels of noise. Distortion and noise negatively impact forecast quality of the participants. This work investigates the applicability of machine learning (ML) techniques and compares their performances with the more traditional methods in order to improve demand forecast accuracy in supply chains. To this end we used two data sets from particular companies (chocolate manufacturer and toner cartridge manufacturer), as well as data from the Statistics Canada manufacturing survey. A representative set of traditional and ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As a group, Machine Learning techniques outperformed traditional techniques in terms of overall average, but not in terms of overall ranking. We also found that a support vector machine (SVM) trained on multiple demand series produced the most accurate forecasts.
Granular Computing and Human-Centricity in Computational Intelligence
Authors: Pedrycz Witold
Published: October 2011

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.
Artificial Moral Agency in Technoethics
Authors: Sullins John P.
Published: October 2011

This chapter will argue that artificial agents created or synthesized by technologies such as artificial life (ALife), artificial intelligence (AI), and in robotics present unique challenges to the traditional notion of moral agency and that any successful technoethics must seriously consider that these artificial agents may indeed be artificial moral agents (AMA), worthy of moral concern. This purpose will be realized by briefly describing a taxonomy of the artificial agents that these technologies are capable of producing. I will then describe how these artificial entities conflict with our standard notions of moral agency. I argue that traditional notions of moral agency are too strict even in the case of recognizably human agents and then expand the notion of moral agency such that it can sensibly include artificial agents.
Noble Ape’s Cognitive Simulation
Authors: Barbalet Thomas S.
Published: October 2011

Inspired by observing bacterial growth in agar and by the transfer of information through simple agar simulations, the cognitive simulation of Noble Ape (originally developed in 1996) has defined itself as both a philosophical simulation tool and a processor metric. The Noble Ape cognitive simulation was originally developed based on diverse philosophical texts and in methodological objection to the neural network paradigm of artificial intelligence. This chapter explores the movement from biological observation to agar simulation through information transfer into a coherent cognitive simulation. The cognitive simulation had to be tuned to produce meaningful results. The cognitive simulation was adopted as processor metrics for tuning performance. This “brain cycles per second” metric was first used by Apple in 2003 and then Intel in 2005. Through this development, both the legacy of primitive agar information-transfer and the use of this as a cognitive simulation method raised novel computational and philosophical issues.
A Survey of Optimized Learning Pathway Planning and Assessment Paper Generation with Swarm Intelligence
Authors: Wong Lung-Hsiang , Looi Chee-Kit
Published: October 2011

One major direction in research on technology-enabled learning systems revolves round the notion of generating optimal learning pathways. Two examples of the application areas that could be presented as search and optimization problem in the context of Artificial Intelligence are:- (1) Adaptive selection and sequencing of learning objects based on the learning profiles, preferences and abilities of individual learners; (2) Automatic composition of assessment or examination papers based on instructors‘ specifications. In this chapter, we present a critical discussion of the research which is concerned with the application of the paradigm of “swarm intelligence” in these two areas. The main aim of this survey is to highlight the new trends and key research achievements that have been realised in the last few years. We will also outline a range of relevant research issues and challenges that have been generated by this body of work.
Facial Expression Analysis by Machine Learning
Authors: Cho Siu-Yeung , Teoh Teik-Toe , Nguwi Yok-Yen
Published: October 2011

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.
Application of Machine Learning Techniques for Railway Health Monitoring
Authors: Shafiullah G.M. , Thompson Adam , Wolfs Peter J. , Ali A.B.M. Shawkat
Published: October 2011

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.
Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach
Authors: Maffezzoli A. , Wanke E.
Published: October 2011

In the present chapter authors want to expose new insights in the field of Computational Neuroscience at regard to the study of neuronal networks grown in vitro. Such kind of analyses can exploit the availability of a huge amount of data thanks to the use of Multi Electrode Arrays (MEA), a multi-channel technology which allows capturing the activity of several different neuronal cells for long time recordings. Given the possibility of simultaneous targeting of various sites, neuroscientists are so applying such recent technology for various researches. The chapter begins by giving a brief presentation of MEA technology and of the data produced in output, punctuating some of the pros and cons of MEA recordings. Then we present an overview of the analytical techniques applied in order to extrapolate the hidden information from available data. Then we shall explain the approach we developed and applied on MEAs prepared in our cell culture laboratory, consisting of statistical methods capturing the main features of the spiking, in particular bursting, activity of various neuron, and performing data dimensionality reduction and clustering, in order to classify neurons according to their spiking properties having showed correlated features. Finally the chapter wants to furnish to neuroscientists an overview about the quantitative analysis of in-vitro spiking activity data recorded via MEA technology and to give an example of explorative analysis applied on MEA data. Such study is based on methods from Statistics and Machine Learning or Computer Science but at the same time strictly related to neurophysiological interpretations of the putative pharmacological manipulation of synaptic connections and mode of firing, with the final aim to extract new information and knowledge about neuronal networks behavior and organization.
Modeling the Ecological Footprint of Nations via Evolutionary Computation and Machine Learning Models
Authors: Mostafa Mohamed M.
Published: October 2011

The per capita Ecological Footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth’s biological capacity required to support human activity. This study uses gene expression programming and Self-organizing Maps (SOM) to predict, classify and cluster the EF of 140 nations. A Bayesian approach was used to formally test the research hypotheses. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific simulation method, i.e., Markov Chain Monte Carlo (MCMC), is utilized to estimate the model parameters. Bayesian MCMC methods allow a richer and more complete representation of complex EF data. It also provides a computationally attractive and straightforward method to develop a full and complete description of the inherent uncertainty in parameters, quantiles and performance metrics.
Adaptive Interaction for Mass Customisation
Authors: Uchyigit Gulden
Published: October 2011

The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.
Multiculturality and Multimodal Languages
Authors: Caschera Maria Chiara , D’Ulizia Arianna , Ferri Fernando , Grifoni Patrizia
Published: October 2011

The way by which people communicate each other changes in the different cultures due to the different communicative expectations and depending on their cultural backgrounds. The development of the Internet has caused an increasing use of computer systems by people from different cultures, highlighting the need for interaction systems that adapt the interaction according to the cultural background of the user. This is one of the reasons of the growing research activity that explores how to consider cultural issues during the design of multimodal interaction systems. This chapter is focused on such a challenging topic, proposing a grammatical approach representing multicultural issues in multimodal languages. The approach is based on a grammar, that is able to produce a set of structured sentences, composed of gestural, vocal, audio, graphical symbols, and so on, along with the meaning that these symbols have in the different cultures. This work provides a contribution to the area of mulsemedia research, as it deals with the integration of input produced by multiple human senses and acquired through multiple sensorial media.
Humanitarian Partnerships—Drivers, Facilitators, and Components
Authors: Tomasini Rolando M.
Published: October 2011

Through the use of a case study, this chapter discusses the design of a partnership between humanitarian organizations to understand what the drivers, facilitators, and components of the partnership are. This research has been designed using a topical literature review and a case study. The practical implications include a discussion and guidelines for designing partnerships under high uncertainty and limited resources.
Humanitarian Aid Logistics
Authors: Beresford Anthony , Pettit Stephen
Published: October 2011

This chapter contrasts the response to the Wenchuan earthquake (May 2008) which took place in a landlocked region of China with that of the January 2010 earthquake in Haiti, which as an island nation, was theoretically easily accessible to external aid provision via air or sea. In the initial period following the Wenchuan earthquake, the response was wholly internal as a detailed needs assessment was carried out. Once the Chinese authorities had established the scale of response required, international assistance was quickly allowed into the country. Several multimodal solutions were devised to minimize the risk of supply breakdown. Haiti required substantial external aid and logistics support, but severe organizational and infrastructural weaknesses rendered the supply chain extremely vulnerable locally. This translated to a mismatch between the volume of aid supplied and logistics capability, highlighting the importance of “last-mile” distribution management. The two earthquakes posed extreme challenges to the logistics operations, though both required a mix of military and non-military input into the logistics response. Nonetheless, in each case the non-standard logistics solutions which were devised broadly met the requirements for effective aid distribution in extreme environments.
The Application of Value Chain Analysis for the Evaluation of Alternative Supply Chain Strategies for the Provision of Humanitarian Aid to Africa
Authors: Taylor David H.
Published: October 2011

The chapter also gives a more general consideration to the potential of value chain analysis concepts and techniques to the measurement, evaluation, and improvement of humanitarian supply chain operations in locations and scenarios beyond that described in the current case study.
Local Sourcing in Peacekeeping
Authors: Skoglund Per , Hertz Susanne
Published: October 2011

This case study explores the Swedish armed forces’ sourcing from local suppliers in the area of the peacekeeping operation in Liberia. The chapter discusses why, what, and how the Swedish armed forces develop local sourcing. For the study, a theoretical framework was developed with an industrial network perspective based on three cornerstones: supplier buyer relation development, internationalisation, and finally, souring and business development in a war-torn country. The results of the study show that both implicit and explicit reasons to source locally exist. Every operation is unique, and therefore the sourcing needs to be tailored for each operation. Local sourcing was developed in the country based on existing needs and when opportunities arise. Theoretically, new insights of differences between business relations in military operations and normal business to business relations were gained. Practically, this study illustrates the importance to develop and diversify sourcing in international operations.
Military Involvement in Humanitarian Supply Chains
Authors: Barber Elizabeth
Published: October 2011

The purpose of this chapter is to demonstrate the multitude of activities that military logisticians can provide throughout the various stages in relief supply chains. Most military joint doctrine identifies humanitarian assistance (HA) as one of the “Military Operations Other Than War” (MOOTW) that military personnel are trained to undertake. Part of this HA involves contributing to humanitarian supply chains and logistics management. The supply chain management processes, physical flows, as well as associated information and financial systems form part of the military contributions that add to other aid in the relief supply chain. The main roles of the military to relief supply chains include security and protection, distribution, and engineering. Examples of these key contributions will be provided in this chapter.