Databaser

Smart Food Industry: The Blockchain for Sustainable Engineering, Volume II - Current Status, Future Foods, and Global Issues reviews the literature and scientific frameworks to present a kind of sustainability compass.

Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.

In the rapidly evolving landscape of the digital age, two technologies stand out for their transformative potential: Artificial Intelligence (AI) and Blockchain. This book offers an incisive exploration of the confluence between these technological titans.

The ever-changing world of wearable technologies makes it difficult for experts and practitioners to keep up with the most recent developments. This handbook provides a solid understanding of the significant role that AI plays in the design and development of wearable technologies along with applications and case studies.

This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.

In the rapidly evolving landscape of the digital age, two technologies stand out for their transformative potential: Artificial Intelligence (AI) and Blockchain. This book offers an incisive exploration of the confluence between these technological titans.

This book will cover many helpful analysis tools and processes to help energy managers administer their energy program through their meter management system.

Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital.

The book highlights important advancements in health science research and development by applying the distinctive capabilities inherent to distributed ledger systems.

The book teases out nuances in how NFTs can and should operate, while also pointing to the potential that these non-fungible assets wield in the digital sphere, along with countervailing arguments against their conceptual and functional salience, and perhaps some dangers that they pose.

This text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes¿ theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques.Introduces machine learning/deep learning methods in detail based on examples and data from materials science;Covers all theoretical foundations in an accessible manner, tailored to materials scientists and engineers;Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.

Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making.

In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities.This textbook aims to live up to the now broad diversity of computer science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages and visualisation, the textbook is framed by history and an outlook. Students with a connection to sports science are given a comprehensive insight into computer science in sport, supported by a didactically sophisticated concept that makes it easy to convey the learning content. Numerous questions for self-testing underpin the learning effect and ensure optimal exam preparation. For advanced students, the in-depth discussion of time series data mining, artificial neural networks, convolution kernels, transfer learning and random forests offers additional value.

This books brings readers a holistic understanding of blockchain adoption in healthcare by not only considering the technical fundamentals of use cases, but also the regulatory, informational and organizational challenges and solutions. The book also provides frameworks and toolkits to manage the entire life cycle of adoption, including analysing the environment and feasibility, application design from a user-centred perspective, and implementation strategies that would overcome organizational and informational barriers. Specific issues addressed include but are not limited to: How to analyse the value propositions in healthcare and which distributed actors should be engaged to fulfil these propositions? What policies and practices need to be reviewed to ensure security and privacy of the information shared? How to design blockchain systems that seamlessly integrate with other stakeholder applications, while only the needed information is in the distributed architecture? How can blockchain implementation be managed from governance and risk mitigation perspectives, especially when multiple actors are involved?By reading this book, blockchain enthusiasts, health informatics professionals and healthcare executives will be better prepared to leverage the transformative potential of blockchain for healthcare.

This fully revised and updated new edition of the definitive text/reference on computer network and information security presents a comprehensive guide to the repertoire of security tools, algorithms and best practices mandated by the technology we depend on.

Data has become an indispensable success factor for every company. However, the road towards a data-driven organization is paved with numerous challenges. This book presents a process model for the path to a data-driven company and provides recommendations for the design of all relevant fields of action: Which structures need to be created? Which systems and processes have proven beneficial? How can the quality of the data be ensured and what requirements exist for a data-driven organization in the areas of governance and communication? And last but not least: How can employees be brought along on the journey and what implications does the data-driven organization have for our corporate culture? The book presents an orientation and action framework for the strategic and operational design of a data-driven organization and is valuable for managers who are involved in data management in companies and organizations.

If you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Excel suite of features along with other powerful tools for analytics. George Mount of Stringfest Analytics shows business analysts, data analysts, and business intelligence specialists how to make bigger gains right from your spreadsheets by using Excel's latest features. You'll learn how to build repeatable data cleaning workflows with Power Query, and design relational data models straight from your workbook with Power Pivot. You'll also explore other exciting new features for analytics, such as dynamic array functions, AI-powered insights, and Python integration. Learn how to build reports and analyses that were previously difficult or impossible to do in Excel. This book shows you how to: Build repeatable data cleaning processes for Excel with Power Query Create relational data models and analysis measures with Power Pivot Pull data quickly with dynamic arrays Use AI to uncover patterns and trends from inside Excel Integrate Python functionality with Excel for automated analysis and reporting

This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.

Within this book, the reader will learn how to implement IoT devices, IoT-equipped machines, and AI-equipped IoT applications using models and methodologies along with an array of case studies.

Machine Learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine.

Metaphor and Corpus Linguistics: Building and Investigating an English as a Medium of Instruction Corpus offers a model for building a corpus of oral EMI seminars. It demonstrates how incorporating metaphor to the process of corpus building affords a more comprehensive description of the role of metaphor in discourse.

Technological trends in banking and fintech are disrupting the traditional finance industry. Emerging technologies like blockchain change the industry's structure and new business strategies are needed. This book explores practical and theoretical aspects of digital transformation.

Technological trends in banking and fintech are disrupting the traditional finance industry. Emerging technologies like blockchain change the industry's structure and new business strategies are needed. This book explores practical and theoretical aspects of digital transformation.

Green Computing for Sustainable Smart Cities: A Data Analytics Applications Perspective covers the need for smart green computing from various engineering disciplines and offers diversified applications for smart green computing with use cases.