Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

A multi-attribute decision making procedure using fuzzy numbers and hybrid aggregators

The classical Analytical Hierarchy Process (AHP) has two limitations. Firstly, it disregards the aspect of uncertainty that usually embedded in the data or information expressed by human. Secondly, it ignores the aspect of interdependencies among attributes during aggregation. The application of fuzzy numbers aids in confronting the former issue whereas, the usage of Choquet Integral operator helps in dealing with the later issue. However, the application of fuzzy numbers into multi-attribute decision making (MADM) demands some additional steps and inputs from decision maker(s). Similarly, identification of monotone measure weights prior to employing Choquet Integral requires huge number of computational steps and amount of inputs from decision makers, especially with the increasing number of attributes. Therefore, this research proposed a MADM procedure which able to reduce the number of computational steps and amount of information required from the decision makers when dealing with these two aspects simultaneously. To attain primary goal of this research, five phases were executed. First, the concept of fuzzy set theory and its application in AHP were investigated. Second, an analysis on the aggregation operators was conducted. Third, the investigation was narrowed on Choquet Integral and its associate monotone measure. Subsequently, the proposed procedure was developed with the convergence of five major components namely Factor Analysis, Fuzzy-Linguistic Estimator, Choquet Integral, Mikhailov‘s Fuzzy AHP, and Simple Weighted Average. Finally, the feasibility of the proposed procedure was verified by solving a real MADM problem where the image of three stores located in Sabak Bernam, Selangor, Malaysia was analysed from the homemakers‘ perspective. This research has a potential in motivating more decision makers to simultaneously include uncertainties in human‘s data and interdependencies among attributes when solving any MADM problems

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

Blueprint: descrição da complexidade da regulação metabólica através da reconstrução de modelos metabólicos e regulatórios integrados

Tese de doutoramento em Biomedical EngineeringUm modelo metabólico consegue prever o fenótipo de um organismo. No entanto, estes modelos podem obter previsões incorretas, pois alguns processos metabólicos são controlados por mecanismos reguladores. Assim, várias metodologias foram desenvolvidas para melhorar os modelos metabólicos através da integração de redes regulatórias. Todavia, a reconstrução de modelos regulatórios e metabólicos à escala genómica para diversos organismos apresenta diversos desafios. Neste trabalho, propõe-se o desenvolvimento de diversas ferramentas para a reconstrução e análise de modelos metabólicos e regulatórios à escala genómica. Em primeiro lugar, descreve-se o Biological networks constraint-based In Silico Optimization (BioISO), uma nova ferramenta para auxiliar a curação manual de modelos metabólicos. O BioISO usa um algoritmo de relação recursiva para orientar as previsões de fenótipo. Assim, esta ferramenta pode reduzir o número de artefatos em modelos metabólicos, diminuindo a possibilidade de obter erros durante a fase de curação. Na segunda parte deste trabalho, desenvolveu-se um repositório de redes regulatórias para procariontes que permite suportar a sua integração em modelos metabólicos. O Prokaryotic Transcriptional Regulatory Network Database (ProTReND) inclui diversas ferramentas para extrair e processar informação regulatória de recursos externos. Esta ferramenta contém um sistema de integração de dados que converte dados dispersos de regulação em redes regulatórias integradas. Além disso, o ProTReND dispõe de uma aplicação que permite o acesso total aos dados regulatórios. Finalmente, desenvolveu-se uma ferramenta computacional no MEWpy para simular e analisar modelos regulatórios e metabólicos. Esta ferramenta permite ler um modelo metabólico e/ou rede regulatória, em diversos formatos. Esta estrutura consegue construir um modelo regulatório e metabólico integrado usando as interações regulatórias e as ligações entre genes e proteínas codificadas no modelo metabólico e na rede regulatória. Além disso, esta estrutura suporta vários métodos de previsão de fenótipo implementados especificamente para a análise de modelos regulatórios-metabólicos.Genome-Scale Metabolic (GEM) models can predict the phenotypic behavior of organisms. However, these models can lead to incorrect predictions, as certain metabolic processes are controlled by regulatory mechanisms. Accordingly, many methodologies have been developed to extend the reconstruction and analysis of GEM models via the integration of Transcriptional Regulatory Network (TRN)s. Nevertheless, the perspective of reconstructing integrated genome-scale regulatory and metabolic models for diverse prokaryotes is still an open challenge. In this work, we propose several tools to assist the reconstruction and analysis of regulatory and metabolic models. We start by describing BioISO, a novel tool to assist the manual curation of GEM models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype predictions. Hence, this tool can reduce the number of artifacts in GEM models, decreasing the burdens of model refinement and curation. A state-of-the-art repository of TRNs for prokaryotes was implemented to support the reconstruction and integration of TRNs into GEM models. The ProTReND repository comprehends several tools to extract and process regulatory information available in several resources. More importantly, this repository contains a data integration system to unify the regulatory data into standardized TRNs at the genome scale. In addition, ProTReND contains a web application with full access to the regulatory data. Finally, we have developed a new modeling framework to define, simulate and analyze GEnome-scale Regulatory and Metabolic (GERM) models in MEWpy. The GERM model framework can read a GEM model, as well as a TRN from different file formats. This framework assembles a GERM model using the regulatory interactions and Genes-Proteins-Reactions (GPR) rules encoded into the GEM model and TRN. In addition, this modeling framework supports several methods of phenotype prediction designed for regulatory-metabolic models.I would like to thank Fundação para a Ciência e Tecnologia for the Ph.D. studentship I was awarded with (SFRH/BD/139198/2018)

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models