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Intro -- Preface -- Contents -- 1 Machine Learning Application in Food Safety, Production, and Quality -- 1.1 Introduction -- 1.2 An Introduction to Food Supply Chain -- 1.2.1 Food Safety -- 1.2.1.1 Foodborne Illness -- 1.2.1.2 Foodborne Disease Outbreaks -- 1.2.2 Food Spoilage and Quality -- 1.2.2.1 Food Authenticity -- 1.2.2.2 Food Post-harvesting -- 1.2.3 Food Production Process -- 1.2.3.1 Food Harvesting -- 1.2.3.2 Food Packaging -- 1.2.3.3 Food Traceability -- 1.2.3.4 Food Distribution -- 1.2.3.5 Food Storage -- 1.3 An Introduction to Machine Learning -- 1.3.1 Machine Learning Applications in Food Safety -- 1.3.2 Machine Learning Applications in Food Quality -- 1.3.3 Machine Learning Applications in Food Production -- 1.4 Conclusion -- References -- 2 Foodborne Bacterial PathogenBig Data - Genomic Analysis -- 2.1 Introduction -- 2.2 Whole Genome Sequencing -- 2.2.1 WGS in Source Attribution -- 2.2.2 WGS in Disease Surveillance -- 2.2.3 Antimicrobial Resistance, Virulence Potential, and Risk Analysis -- 2.2.4 WGS Technologies -- 2.2.4.1 First-Generation Sequencing: Sanger Shotgun Approach -- 2.2.4.2 Second-Generation Sequencing: The Massively Parallel Approach -- 2.2.4.3 Third-Generation Sequencing: The Long-Read Approach -- 2.3 Bioinformatics: Algorithms and Databases -- 2.4 Future Opportunities and Challenges for WGS -- 2.4.1 Predicting Emerging Treats -- 2.4.2 Low- and Middle-Income Countries -- 2.4.3 Culture-Independent Diagnostic Tests -- 2.4.3.1 Metagenomic Sequencing -- References -- 3 Foodborne Viral Pathogen Big Data: Genomic Analysis -- 3.1 Introduction -- 3.1.1 Norovirus -- 3.1.2 HAV -- 3.1.3 HEV -- 3.1.4 SARS-CoV-2 -- 3.1.5 WGS -- 3.2 Applications -- 3.2.1 Surveillance and Source Attribution -- 3.2.2 Analysis of Variants and Viral Evolution -- 3.2.3 Predictive Analytics -- 3.3 Conclusion and Future Perspectives -- References.

With the advent of Big Data (BD) in the social sciences, vast amounts of data (and the tools to analyze them) have become available faster than ethical and legal standards could develop regarding the use of such data. At the same time, data collectors and analysts face new moral dilemmas as the proliferation of personal and impersonal data clearly poses new challenges to traditional assumptions about privacy and autonomy. The discussion of such ethical challenges seems to lag behind and the literature specifically dealing with the research ethics of BD is still scarce. This article asks which ethical and legal aspects need to be considered when collecting and analyzing data on individuals from the web and combining them to gain an enriched picture of human activities. It proceeds to provide a brief overview of existing research ethics regulations and outlines areas of particular relevance to the challenges that come with the use of BD, such as the delineation of human subject research, the (im)possibility of informed consent for these new kinds of data, the sources and public availability of data and questions of risk and risk assessment. It also formulates some generic recommendations in order to stimulate further debate, one of which posits that social scientists must address and discuss the challenges that emerge in research applications of BD more widely than it is currently the case.

Introduction: New legal challenges of big data /Joe Cannataci, Valeria Falce and Oreste Pollicino --Big data and big databases between privacy and competition /Sofia Oliveira Pais --Competition challenges of big data : algorithmic collusion, personalised pricing and privacy /Antonio Capobianco and Pedro Gonzaga --Antitrust enforcement and privacy standards /Renato Nazzini --Mergers, data markets and competition /Damiano Canapa --Platform role and intermediary responsibility /Vicente Bagnoli --Global perspectives on big data and consumer law /Mateja Durovic and Franciszek Lech --Data as an input in competition law cases : standards, difficulties and biases in EU merger control /Rupprecht Podszun and Sarah Langenstein --Breaking down information silos with big data : a legal analysis of data sharing /Giovanni De Gregorio and Sofia Ranchordás --The relationship between freedom of expression and big data /Oleg Soldatov --Big data and children's rights : new legal challenges alongside new opportunities /Shulamit Almog and Liat Franco --Artificial intelligence in the big data era : risks and opportunities /Francesca Lagioia and Giovanni Sartor.

The government is liable to help every citizen for employability, minimum facilitation of livelihood and almost all countries have shown their concern for. There are several ways of helping the poor and weaker section of society as well as all resident of a community/area or whole population and one of them subsidy. All subsidy schemes have two objectives, to help the common man and to upgrade their economic and living status. These have several success and failure stories and need up-gradation at every actionable day to protect the frauds, minimize the financial burden to government and maximize the impact of schemes. The main objective of the present study is to assess the issues associated with government subsidies and proposing the solution from Big data, most accepted source of information in current information era. The study review the source of information which may be usable for the subsidy mechanism and scaled it through data driven technique which will be more transparent, democratic and recommend the more responsible governance system after assigning the stepwise monitoring and verification from the data.

Abstract The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

"Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or 'Big Data'. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government."--Provided by publisher.

Prologue: Why ask the question? / Natasha Lushetich -- Big data and/versus people knowledge : on the ambiguities of humanistic research / Ingrid Hoofd -- Simulated replicants forever? Big data, engendered determinism and the end of prophecy / Franco 'Bifo' Berardi -- "Visual hallucination of probable events" : on environments of images, data, and machine learning / Abelardo Gil-Fournier and Jussi Parikka -- Biometric datafication in governmental and personal spheres / Btihaj Ajana -- Digital biopolitics and the problem of fatigue in platform capitalism / Tim Christaens -- Appreciating machine-generated artwork through deep learning mechanisms / Lonce Wyse -- Data to the Nth degree : zooming in on the smart set / Craig J. Saper -- Intellectual autonomy after artificial intelligence : the future of memory institutions and historical research / Nicola Horsley -- BeHere : prosthetic memory in the age of digital frottage / Natasha Lushetich and Masaki Fujihat -- Surfaces and depths : an aesthetics of big data / Dominic Smith -- POV data doubles, the dividual and the drive to visibility / Mitra Azar -- Reading big data as the heterogenous subject / Simon Biggs -- Epilogue: Telepathic exaptation in late cognitive capitalism : a speculative approach to the effects of digitality / Warren Neidich.

One way to study the politics of big data is the inspection of their assemblages. By opening up the "black boxes" of data assemblages, it is possible to reconstruct the choices, compromises, conflicts and agree-ments that contributed to the construction of a given datum. Leaning on in-depth interviews and focus groups with experts and specialists who work within three European data centers, we unveil the interde-pendence between social and technical aspects and between a series of internal and external actors, which all contribute to the data assemblage. Results show that communities of experts, technologies, stakeholders and end-users are entwined components that interact amongst them in a contingent and complex web of negotiations and constraints and frame what is possible, desirable and expected by data.

"The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter "--

This paper proposes to tackle the problem of digital inequality by introducing digital technologies of knowledge generation and decision-making to a feminist critique of rationality that is informed by discourse theory and intersectional perspectives on gender and gendered relations of inequality. Therefore, it takes a closer look at the epistemological foundations of Big Data as one prominent representation of digital technologies. While Big Data and Big Data-based results and decisions are generally believed to be objective and neutral, numeral cases of algorithmic discrimination have lately begged to differ. This paper argues that algorithmic discrimination is neither random nor accidental; on the contrary, it is - amongst others - the result of the epistemological foundation of Big Data - namely: data fundamentalism, post-explanatory anticipatory pragmatics, and anti-political solutionism. As a consequence, a critical engagement with the concepts and premises that become materialized in the design of digital technologies is needed, if they are not to silently (re)produce social inequalities.