A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

TOWARDS A HOLISTIC EFFICIENT STACKING ENSEMBLE INTRUSION DETECTION SYSTEM USING NEWLY GENERATED HETEROGENEOUS DATASETS

With the exponential growth of network-based applications globally, there has been a transformation in organizations\u27 business models. Furthermore, cost reduction of both computational devices and the internet have led people to become more technology dependent. Consequently, due to inordinate use of computer networks, new risks have emerged. Therefore, the process of improving the speed and accuracy of security mechanisms has become crucial.Although abundant new security tools have been developed, the rapid-growth of malicious activities continues to be a pressing issue, as their ever-evolving attacks continue to create severe threats to network security. Classical security techniquesfor instance, firewallsare used as a first line of defense against security problems but remain unable to detect internal intrusions or adequately provide security countermeasures. Thus, network administrators tend to rely predominantly on Intrusion Detection Systems to detect such network intrusive activities. Machine Learning is one of the practical approaches to intrusion detection that learns from data to differentiate between normal and malicious traffic. Although Machine Learning approaches are used frequently, an in-depth analysis of Machine Learning algorithms in the context of intrusion detection has received less attention in the literature.Moreover, adequate datasets are necessary to train and evaluate anomaly-based network intrusion detection systems. There exist a number of such datasetsas DARPA, KDDCUP, and NSL-KDDthat have been widely adopted by researchers to train and evaluate the performance of their proposed intrusion detection approaches. Based on several studies, many such datasets are outworn and unreliable to use. Furthermore, some of these datasets suffer from a lack of traffic diversity and volumes, do not cover the variety of attacks, have anonymized packet information and payload that cannot reflect the current trends, or lack feature set and metadata.This thesis provides a comprehensive analysis of some of the existing Machine Learning approaches for identifying network intrusions. Specifically, it analyzes the algorithms along various dimensionsnamely, feature selection, sensitivity to the hyper-parameter selection, and class imbalance problemsthat are inherent to intrusion detection. It also produces a new reliable dataset labeled Game Theory and Cyber Security (GTCS) that matches real-world criteria, contains normal and different classes of attacks, and reflects the current network traffic trends. The GTCS dataset is used to evaluate the performance of the different approaches, and a detailed experimental evaluation to summarize the effectiveness of each approach is presented. Finally, the thesis proposes an ensemble classifier model composed of multiple classifiers with different learning paradigms to address the issue of detection accuracy and false alarm rate in intrusion detection systems

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Choice modelling embedded in marketing relationships

Continuation versus defection decisions are rarely a neutral process for consumers. Although previous research has recognized the key role of emotions in such process, it has not directly formulated models of choice based on affective constructs. This dissertation develops and empirically tests such model using discrete choice experimental data. The conceptual model incorporates sellers’ extra effort and work (e.g., adaptation of policies, provision of small favors or considerations) into a traditional choice model, and shows how such efforts unfold in consumer black box as well as influences behavioral outcomes. Specifically, I propose that benefits received throughout interpersonal interactions create a self-other trade-off, which is processed by a specific social-emotional process. Across three studies, the dissertation demonstrates that the social-emotional process mediates the effect of seller efforts on defection utility, and such process is activated only when benefits are received thought interpersonal interactions. Also, the current results demonstrate that the role of emotions on preferences is highly malleable, that is, the same social emotion can both promote or inhibit defection behaviors. Overall, the work offers theoretical insights and methodological guidelines for research on the role of emotions on choice situations. In particular, marketing research should avoid claiming fixed relations between discrete emotions and consumer preferences or prioritize more valenced-based approaches in discrete choice frameworks.Decisões que envolvem continuação versus deserção raramente são um processo neutro para os consumidores. Embora pesquisas anteriores tenham reconhecido o papel fundamental das emoções em tal processo, elas não formularam diretamente modelos de escolha baseados em construtos afetivos. Esta tese desenvolve e testa empiricamente tal modelo usando dados experimentais de escolha discreta. O modelo conceitual incorpora o esforço e trabalho extra dos vendedores (p.ex., adaptação de políticas, provisão de pequenos favores ou considerações) em um modelo de escolha tradicional, e mostra como tais esforços se desdobram dentro da caixa preta do consumidor, bem como influenciam os resultados comportamentais. Especificamente, é proposto que os benefícios recebidos ao longo das interações interpessoais criam um trade-off entre “Eu” e “Outro”, o qual é avaliado por um processo socioemocional específico. Através de três estudos, a dissertação demonstra que o processo socioemocional medeia o efeito dos esforços do vendedor na utilidade da deserção, e tal processo é ativado apenas quando os benefícios são recebidos por meio de interações interpessoais. Além disso, os resultados demonstram que o papel das emoções nas preferências em situações de escolha é altamente maleável, ou seja, a mesma emoção social pode promover ou inibir comportamentos de deserção. No geral, o trabalho oferece insights teóricos e diretrizes metodológicas para a pesquisa sobre o papel das emoções em situações de escolha. Em particular, a pesquisa em marketing deve evitar alegar relações fixas entre emoções específicas e preferências do consumidor ou priorizar abordagens mais baseadas em valência em análises de escolha discreta

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Design principles for data quality tools

Data quality is an essential aspect of organizational data management and can facilitate accurate decision-making and building competitive advantages. Nu-merous data quality tools aim to support data quality work by offering automa-tion for different activities, such as data profiling or validation. However, de-spite a long history of tools and research, a lack of data quality remains an issue for many organizations. Data quality tools face changes in the organizational (e.g., evolving data architectures) and technical (e.g., big data) environment. Established tools cannot fully comprehend these changes, and limited prescrip-tive design knowledge on creating adequate tools is available. In this cumula-tive dissertation, we summarize the findings of nine individual studies on the objectives and design of data quality tools. Most importantly, we conducted four case studies on implementing data quality tools in real-world scenarios. In each case, we designed and implemented a separate data quality tool and abstracted the essential design elements. A subsequent cross-case analysis helped us accu-mulate the available design knowledge, resulting in the proposal of 13 general-ized design principles. With the proposal of empirically grounded design knowledge, the dissertation contributes to the managerial and scientific commu-nities. Managers can use our results to create customized data quality tools and assess offerings at the market. Scientifically, we address the lack of prescriptive design knowledge for data quality tools and offer many opportunities to extend our research in multiple directions. The continuous work on data quality tools will help them become more successful in ensuring data fulfills high-quality standards for the benefit of businesses and society