New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Expert Weighting Based Dynamic Eco-efficiency Assessment of World Consumption

Optimizing the consumption of natural resources and ensuring the availability of resources for both current and future generations has been the target for sustainability research. This paper aims to assess the eco-efficiency of global resource consumption through the environmental footprint perspective. The study effectively utilized EXIOBASE 3.41, a multi-region input-output (MRIO) database, for collecting data and Multi-criteria decision making (MCDM) approach for eco-efficiency assessment. Besides, the present paper utilizes expert weighting strategies such as EPP, SAB, Harvard, and EQUAL for assigning relative significance to various environmental indicators. Primarily, the data sample represents the influence of environmental stressors like GHG emission, land use, energy use, material consumption, water consumption. The study expands through three major scenarios in terms of importance to the economic and environmental outcomes. As such, with three scenarios and four weighting strategies, twelve situations are considered for the purpose of the study. The study findings indicate that the eco-efficiency score for given weighting strategies concerning economic and environmental impact demonstrates a significant statistical difference. The countries like China, India, Russia, Mexico, and Turkey are worst performing while Switzerland, Japan, UK, Germany, and France are best performing in terms of eco-efficiency score. Finally, k-mean clustering algorithm has applied to rank the countries centered on eco-efficiency score and weighing strategie

Multiple Criteria Decision Analysis: Classification Problems and Solutions

Multiple criteria decision analysis (MCDA) techniques are developed to address challenging classification problems arising in engineering management and elsewhere. MCDA consists of a set of principles and tools to assist a decision maker (DM) to solve a decision problem with a finite set of alternatives compared according to two or more criteria, which are usually conflicting. The three types of classification problems to which original research contributions are made are Screening: Reduce a large set of alternatives to a smaller set that most likely contains the best choice. Sorting: Arrange the alternatives into a few groups in preference order, so that the DM can manage them more effectively. Nominal classification: Assign alternatives to nominal groups structured by the DM, so that the number of groups, and the characteristics of each group, seem appropriate to the DM. Research on screening is divided into two parts: the design of a sequential screening procedure that is then applied to water resource planning in the Region of Waterloo, Ontario, Canada; and the development of a case-based distance method for screening that is then demonstrated using a numerical example. Sorting problems are studied extensively under three headings. Case-based distance sorting is carried out with Model I, which is optimized for use with cardinal criteria only, and Model II, which is designed for both cardinal and ordinal criteria; both sorting approaches are applied to a case study in Canadian municipal water usage analysis. Sorting in inventory management is studied using a case-based distance method designed for multiple criteria ABC analysis, and then applied to a case study involving hospital inventory management. Finally sorting is applied to bilateral negotiation using a case-based distance model to assist negotiators that is then demonstrated on a negotiation regarding the supply of bicycle components. A new kind of decision analysis problem, called multiple criteria nominal classification (MCNC), is addressed. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCNC is the classification of alternatives into nominal groups, structured by the DM, who specifies multiple characteristics for each group. The features, definitions and structures of MCNC are presented, emphasizing criterion and alternative flexibility. An analysis procedure is proposed to solve MCNC problems systematically and applied to a water resources planning problem

An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic & Plithogenic Optimizations

This Special Issue puts forward for discussion state-of-the-art papers on new topics related to neutrosophic theories, such as neutrosophic algebraic structures, neutrosophic triplet algebraic structures, neutrosophic extended triplet algebraic structures, neutrosophic algebraic hyperstructures, neutrosophic triplet algebraic hyperstructures, neutrosophic n-ary algebraic structures, neutrosophic n-ary algebraic hyperstructures, refined neutrosophic algebraic structures, refined neutrosophic algebraic hyperstructures, quadruple neutrosophic algebraic structures, refined quadruple neutrosophic algebraic structures, neutrosophic image processing, neutrosophic image classification, neutrosophic computer vision, neutrosophic machine learning, neutrosophic artificial intelligence, neutrosophic data analytics, neutrosophic deep learning, neutrosophic symmetry, and their applications in the real world. This book leads to the further advancement of the neutrosophic and plithogenic theories of NeutroAlgebra and AntiAlgebra, NeutroGeometry and AntiGeometry, Neutrosophic n-SuperHyperGraph (the most general form of graph of today), Neutrosophic Statistics, Plithogenic Logic as a generalization of MultiVariate Logic, Plithogenic Probability and Plithogenic Statistics as a generalization of MultiVariate Probability and Statistics, respectively, and presents their countless applications in our every-day world

Ny forståelse av gasshydratfenomener og naturlige inhibitorer i råoljesystemer gjennom massespektrometri og maskinlæring

Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some crude oils form hydrates that do not agglomerate or deposit, but remain as transportable dispersions. This is commonly believed to be due to naturally occurring components present in the crude oil, however, despite decades of research, their exact structures have not yet been determined. Some studies have suggested that these components are present in the acid fractions of the oils or are related to the asphaltene content of the oils. Crude oils are among the worlds most complex organic mixtures and can contain up to 100 000 different constituents, making them difficult to characterise using traditional mass spectrometers. The high mass accuracy of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) yields a resolution greater than traditional techniques, making FT-ICR MS able to characterise crude oils to a greater extent, and possibly identify hydrate active components. FT-ICR MS spectra usually contain tens of thousands of peaks, and data treatment methods able to find underlying relationships in big data sets are required. Machine learning and multivariate statistics include many methods suitable for big data. A literature review identified a number of promising methods, and the current status for the use of machine learning for analysis of gas hydrates and FT-ICR MS data was analysed. The literature study revealed that although many studies have used machine learning to predict thermodynamic properties of gas hydrates, very little work have been done in analysing gas hydrate related samples measured by FT-ICR MS. In order to aid their identification, a successive accumulation procedure for increasing the concentrations of hydrate active components was developed by SINTEF. Comparison of the mass spectra from spiked and unspiked samples revealed some peaks that increased in intensity over the spiking levels. Several classification methods were used in combination with variable selection, and peaks related to hydrate formation were identified. The corresponding molecular formulas were determined, and the peaks were assumed to be related to asphaltenes, naphthenes and polyethylene glycol. To aid the characterisation of the oils, infrared spectroscopy (both Fourier Transform infrared and near infrared) was combined with FT-ICR MS in a multiblock analysis to predict the density of crude oils. Two different strategies for data fusion were attempted, and sequential fusion of the blocks achieved the highest prediction accuracy both before and after reducing the dimensions of the data sets by variable selection. As crude oils have such complex matrixes, samples are often very different, and many methods are not able to handle high degrees of variations or non-linearities between the samples. Hierarchical cluster-based partial least squares regression (HC-PLSR) clusters the data and builds local models within each cluster. HC-PLSR can thus handle non-linearities between clusters, but as PLSR is a linear model the data is still required to be locally linear. HC-PLSR was therefore expanded into deep learning (HC-CNN and HC-RNN) and SVR (HC-SVR). The deep learning-based models outperformed HC-PLSR for a data set predicting average molecular weights from hydrolysed raw materials. The analysis of the FT-ICR MS spectra revealed that the large amounts of information contained in the data (due to the high resolution) can disturb the predictive models, but the use of variable selection counteracts this effect. Several methods from machine learning and multivariate statistics were proven valuable for prediction of various parameters from FT-ICR MS using both classification and regression methods.Gasshydrater er et av hovedproblemene for Flow assurance i olje- og gassnæringen ettersom at de kan forårsake blokkeringer i oljerørledninger og prosessutstyr som krever at systemet må stenges ned. Tidligere studier har vist at noen råoljer danner hydrater som ikke agglomererer eller avsetter, men som forblir som transporterbare dispersjoner. Dette antas å være på grunn av naturlig forekommende komponenter til stede i råoljen, men til tross for årevis med forskning er deres nøyaktige strukturer enda ikke bestemt i detalj. Noen studier har indikert at disse komponentene kan stamme fra syrefraksjonene i oljen eller være relatert til asfalteninnholdet i oljene. Råoljer er blant verdens mest komplekse organiske blandinger og kan inneholde opptil 100 000 forskjellige bestanddeler, som gjør dem vanskelig å karakterisere ved bruk av tradisjonelle massespektrometre. Den høye masseoppløsningen Fourier-transform ion syklotron resonans massespektrometri (FT-ICR MS) gir en høyere oppløsning enn tradisjonelle teknikker, som gjør FT-ICR MS i stand til å karakterisere råoljer i større grad og muligens identifisere hydrataktive komponenter. FT-ICR MS spektre inneholder vanligvis titusenvis av topper, og det er nødvendig å bruke databehandlingsmetoder i stand til å håndtere store datasett, med muligheter til å finne underliggende forhold for å analysere spektrene. Maskinlæring og multivariat statistikk har mange metoder som er passende for store datasett. En litteratur studie identifiserte flere metoder og den nåværende statusen for bruken av maskinlæring for analyse av gasshydrater og FT-ICR MS data. Litteraturstudien viste at selv om mange studier har brukt maskinlæring til å predikere termodynamiske egenskaper for gasshydrater, har lite arbeid blitt gjort med å analysere gasshydrat relaterte prøver målt med FT-ICR MS. For å bistå identifikasjonen ble en suksessiv akkumuleringsprosedyre for å øke konsentrasjonene av hydrataktive komponenter utviklet av SINTEF. Sammenligninger av massespektrene fra spikede og uspikede prøver viste at noen topper økte sammen med spikingnivåene. Flere klassifikasjonsmetoder ble brukt i kombinasjon med ariabelseleksjon for å identifisere topper relatert til hydratformasjon. Molekylformler ble bestemt og toppene ble antatt å være relatert til asfaltener, naftener og polyetylenglykol. For å bistå karakteriseringen av oljene ble infrarød spektroskopi inkludert med FT-ICR MS i en multiblokk analyse for å predikere tettheten til råoljene. To forskjellige strategier for datafusjonering ble testet og sekvensiell fusjonering av blokkene oppnådde den høyeste prediksjonsnøyaktigheten både før og etter reduksjon av datasettene med bruk av variabelseleksjon. Ettersom råoljer har så kompleks sammensetning, er prøvene ofte veldig forskjellige og mange metoder er ikke egnet for å håndtere store variasjoner eller ikke-lineariteter mellom prøvene. Hierarchical cluster-based partial least squares regression (HCPLSR) grupperer dataene og lager lokale modeller for hver gruppe. HC-PLSR kan dermed håndtere ikke-lineariteter mellom gruppene, men siden PLSR er en lokal modell må dataene fortsatt være lokalt lineære. HC-PLSR ble derfor utvidet til convolutional neural networks (HC-CNN) og recurrent neural networks (HC-RNN) og support vector regression (HC-SVR). Disse dyp læring metodene utkonkurrerte HC-PLSR for et datasett som predikerte gjennomsnittlig molekylvekt fra hydrolyserte råmaterialer. Analysen av FT-ICR MS spektre viste at spektrene inneholder veldig mye informasjon. Disse store mengdene med data kan forstyrre prediksjonsmodeller, men bruken av variabelseleksjon motvirket denne effekten. Flere metoder fra maskinlæring og multivariat statistikk har blitt vist å være nyttige for prediksjon av flere parametere from FT-ICR MS data ved bruk av både klassifisering og regresjon

Supply Chain Performance Appraisement and Benchmarking for Manufacturing Industries: Emphasis on Traditional, Green, Flexible and Resilient Supply Chain along with Supplier Selection

Supply chain represents a network of interconnected activities starting from raw material extraction to delivery of the finished product to the end-user. The main constituents of supply chain are supplying/purchasing, inbound logistics, manufacturing, outbound logistics, marketing and sales. In recent times, the traditional supply chain construct is being modified to embrace various challenges of present business needs. Today’s global market has become highly volatile; customers’ expectations are ever-changing. Fierce competition amongst business sectors necessitates adapting modern supply chain management philosophies. Agility, greenness, flexibility as well as resilience have become the key success factors in satisfying global business needs. In order to remain competitive in the turbulent marketplace, industries should focus on improving overall performance of the supply chain network. In this dissertation, supply chain performance assessment has been considered as a decision making problem involving various measures and metrics (performance indicators). Since most of the performance indices are subjective in nature; decisionmaking relies on active participation of a group of decision-makers (DMs). Subjective human judgment often bears some sort of ambiguity as well as vagueness in the decision making; to overcome uncertainty in decision making, adaptation of grey/fuzzy set theory seems to be fruitful. To this end, present work deals with a variety of decision support tools to facilitate supply chain performance appraisement as well as benchmarking in fuzzy/grey context. Starting from the traditional supply chain, this work extends appraisement and benchmarking of green supply chain performance for a set of candidate case companies (under the same industry) operating under similar supply chain construct. Exploration of grey-MOORA, fuzzy-MOORA, IVFN-TOPSIS, fuzzy-grey relation method has been illustrated in this part of work. Apart from aforementioned empirical studies, two real case studies have been reported in order to estimate a quantitative performance metric reflecting the extent of supply chain flexibility and resilience, respectively, in relation to the case company under consideration. Performance benchmarking helps in identifying best practices in perspectives of supply chain networking; it can easily be transmitted to other industries. Organizations can follow their peers in order to improve overall performance of the supply chain. vi Supplier selection is considered as an important aspect in supply chain management. Effective supplier selection must be a key strategic consideration towards improving supply chain performance. However, the task of supplier selection seems difficult due to subjectivity of supplier performance indices. Apart from considering traditional supplier selection criteria (cost, quality and service); global business scenario encourages emphasizing various issues like environmental performance (green concerns), resiliency etc. into evaluation and selection of an appropriate supplier. In this context, the present work also attempts to explore fuzzy based decision support systems towards evaluation and selection of potential suppliers in green supply chain as well as resilient supply chain, respectively. Fuzzy based Multi-Level Multi-Criteria Decision Making (MLMCDM) approach, fuzzy-TOPSIS and fuzzy-VIKOR have been utilized to facilitate the said decision making

Supply Chain Performance Appraisement and Benchmarking for Manufacturing Industries: Emphasis on Traditional, Green, Flexible and Resilient Supply Chain along with Supplier Selection

Supply chain represents a network of interconnected activities starting from raw material extraction to delivery of the finished product to the end-user. The main constituents of supply chain are supplying/purchasing, inbound logistics, manufacturing, outbound logistics, marketing and sales. In recent times, the traditional supply chain construct is being modified to embrace various challenges of present business needs. Today’s global market has become highly volatile; customers’ expectations are ever-changing. Fierce competition amongst business sectors necessitates adapting modern supply chain management philosophies. Agility, greenness, flexibility as well as resilience have become the key success factors in satisfying global business needs. In order to remain competitive in the turbulent marketplace, industries should focus on improving overall performance of the supply chain network. In this dissertation, supply chain performance assessment has been considered as a decision making problem involving various measures and metrics (performance indicators). Since most of the performance indices are subjective in nature; decisionmaking relies on active participation of a group of decision-makers (DMs). Subjective human judgment often bears some sort of ambiguity as well as vagueness in the decision making; to overcome uncertainty in decision making, adaptation of grey/fuzzy set theory seems to be fruitful. To this end, present work deals with a variety of decision support tools to facilitate supply chain performance appraisement as well as benchmarking in fuzzy/grey context. Starting from the traditional supply chain, this work extends appraisement and benchmarking of green supply chain performance for a set of candidate case companies (under the same industry) operating under similar supply chain construct. Exploration of grey-MOORA, fuzzy-MOORA, IVFN-TOPSIS, fuzzy-grey relation method has been illustrated in this part of work. Apart from aforementioned empirical studies, two real case studies have been reported in order to estimate a quantitative performance metric reflecting the extent of supply chain flexibility and resilience, respectively, in relation to the case company under consideration. Performance benchmarking helps in identifying best practices in perspectives of supply chain networking; it can easily be transmitted to other industries. Organizations can follow their peers in order to improve overall performance of the supply chain. vi Supplier selection is considered as an important aspect in supply chain management. Effective supplier selection must be a key strategic consideration towards improving supply chain performance. However, the task of supplier selection seems difficult due to subjectivity of supplier performance indices. Apart from considering traditional supplier selection criteria (cost, quality and service); global business scenario encourages emphasizing various issues like environmental performance (green concerns), resiliency etc. into evaluation and selection of an appropriate supplier. In this context, the present work also attempts to explore fuzzy based decision support systems towards evaluation and selection of potential suppliers in green supply chain as well as resilient supply chain, respectively. Fuzzy based Multi-Level Multi-Criteria Decision Making (MLMCDM) approach, fuzzy-TOPSIS and fuzzy-VIKOR have been utilized to facilitate the said decision making