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Data-Centric Business and Applications: Modern Trends in Financial and Innovation Data Processes 2024 (Lecture Notes on Data Engineering and Communications Technologies #240)
by Andriy Semenov Iryna Yepifanova Jana KajanováThe combination of the latest developments in economic theory with contemporary information technologies may be considered as a powerful instrument for the processing of commercial data. This book employs the Ukrainian economy as a case study to examine the multifaceted aspects of financial and investment processes, as well as the utilization of information technology mechanisms in company and industrial management. The range of industrial sectors that have been investigated facilitates application of effective business analysis in enterprises. Furthermore, the book provides detailed insights into transdisciplinary ideas, practices, and examples that may be beneficial when examining evolutional developments in this area. Additionally, this book presents analytical techniques for decision-making in business, finance, and innovation management.
Data-Centric Business and Applications: Towards Software Development (Volume 4) (Lecture Notes on Data Engineering and Communications Technologies #40)
by Lech Madeyski Natalia Kryvinska Aneta Poniszewska-Marańda Stanisław JarząbekThis book explores various aspects of software creation and development as well as data and information processing. It covers relevant topics such as business analysis, business rules, requirements engineering, software development processes, software defect prediction, information management systems, and knowledge management solutions. Lastly, the book presents lessons learned in information and data management processes and procedures.
Data-Centric Security in Software Defined Networks (Studies in Big Data #149)
by Marek Amanowicz Sebastian Szwaczyk Konrad WronaThe book focuses on applying the data-centric security (DCS) concept and leveraging the unique capabilities of software-defined networks (SDN) to improve the security and resilience of corporate and government information systems used to process critical information and implement business processes requiring special protection. As organisations increasingly rely on information technology, cyber threats to data and infrastructure can significantly affect their operations and adversely impact critical business processes. Appropriate authentication, authorisation, monitoring, and response measures must be implemented within the perimeter of the system to protect against adversaries. However, sophisticated attackers can compromise the perimeter defences and even remain in the system for a prolonged time without the owner being aware of these facts. Therefore, new security paradigms such as Zero Trust and DCS aimto provide defence under the assumption that the boundary protections will be breached. Based on experience and lessons learned from research on the application of DCS to defence systems, the authors present an approach to integrating the DCS concept with SDN. They introduce a risk-aware approach to routing in SDN, enabling defence-in-depth and enhanced security for data in transit. The book describes possible paths for an organisation to transition towards DCS, indicating some open and challenging issues requiring further investigation. To allow interested readers to conduct detailed studies and evaluate the exemplary implementation of DCS over SDN, the text includes a short tutorial on using the emulation environment and links to the websites from which the software can be downloaded.
Data-Driven Alexa Skills: Voice Access to Rich Data Sources for Enterprise Applications
by Simon A. KingabyDesign and build innovative, custom, data-driven Alexa skills for home or business. Working through several projects, this book teaches you how to build Alexa skills and integrate them with online APIs. If you have basic Python skills, this book will show you how to build data-driven Alexa skills. You will learn to use data to give your Alexa skills dynamic intelligence, in-depth knowledge, and the ability to remember. Data-Driven Alexa Skills takes a step-by-step approach to skill development. You will begin by configuring simple skills in the Alexa Skill Builder Console. Then you will develop advanced custom skills that use several Alexa Skill Development Kit features to integrate with lambda functions, Amazon Web Services (AWS), and Internet data feeds. These advanced skills enable you to link user accounts, query and store data using a NoSQL database, and access real estate listings and stock prices via web APIs.What You Will LearnSet up and configure your development environment properly the first timeBuild Alexa skills quickly and efficiently using Agile tools and techniquesCreate a variety of data-driven Alexa skills for home and businessAccess data from web applications and Internet data sources via their APIsTest with unit-testing frameworks throughout the development life cycleManage and query your data using the DynamoDb NoSQL database enginesWho This Book Is ForDevelopers who wish to go beyond Hello World and build complex, data-driven applications on Amazon's Alexa platform; developers who want to learn how to use Lambda functions, the Alexa Skills SDK, Alexa Presentation Language, and Alexa Conversations; developers interested in integrating with public APIs such as real estate listings and stock market prices. Readers will need to have basic Python skills.
Data-Driven Applications for Emerging Technologies
by Nazmul Siddique Mohammad Shamsul Arefin Ahmed Wasif Reza Aminul HaqueData-Driven Applications for Emerging Technologies explores the practical use of data science in AI, healthcare, sustainability, and security. It covers key topics like predictive modelling, deep learning, and natural language processing, offering a mix of theory and hands-on applications. The book highlights how data-driven techniques can improve decision-making, optimize processes, and solve real-world problems.Each chapter includes research contributions from academics and industry professionals, making the content both relevant and accessible. Readers will find practical insights into applying machine learning frameworks, data preprocessing techniques, and emerging technologies across different domains.Designed for researchers, professionals, and students, this book provides a solid foundation in data-driven methods without being overly technical. Whether you’re looking to enhance your understanding of AI and machine learning or apply data science in real-world scenarios, this book serves as a useful and practical resource.
Data-Driven Approach for Bio-medical and Healthcare (Data-Intensive Research)
by Nilanjan DeyThe book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.
Data-Driven Clinical Decision-Making Using Deep Learning in Imaging (Studies in Big Data #152)
by Nilanjan Dey M. F. MridhaThis book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
Data-Driven Company: Moderne und integrierte Ansätze, um datengetrieben zu werden
by Sven-Erik WillrichDaten werden für Unternehmen immer wichtiger. Gleichzeitig mangelt es an Best Practices und Leitfäden, wie klassische mit modernen Ansätzen wie Data Mesh oder Data Fabric zu einem anwendbaren Framework integriert werden können. Hierzu werden die Themen Organisationsdesign, Datenstrategie / -management und Enterprise Architecture auf theoretische und pragmatische Weise verbunden. Das Buch präsentiert Ziele, ein Data Operating Model sowie datenstrategische Ansätze für eine Data-Driven Company. Hervorzuheben sind dabei die zahlreichen Abbildungen aus diesem Buch, die die komplexen Zusammenhänge anschaulich machen und das Lesen unterstützen. Zielgruppe Mit diesen Inhalten richtet sich das Buch an Führungskräfte, Experten, Berater und weitere Personen, die einen Bezug zur IT und Daten haben beziehungsweise diesen entwickeln möchten. Durch den niedrigschwelligen Einstieg und gleichzeitigen Tiefgang in die ausgewählten Themen adressiert es sowohl Einsteiger als auch erfahrene Datenexperten. Autor Dr. Sven-Erik Willrich ist ein erfahrener Experte im Bereich IT und Datenmanagement. Mit seinem Hintergrund in Wirtschaftsinformatik und langjähriger Beratungserfahrung bringt er sowohl theoretisches Wissen als auch praxisorientierte Lösungsansätze ein. Als Dozent und Redner im Bereich Digitalisierung teilt er regelmäßig seine Expertise.
Data-Driven Customer Engagement: Mastering MarTech Strategies for Success
by Ralf StraussEmbark on a journey through the rapidly evolving landscape of Marketing Technology (MarTech) with this comprehensive guide. From understanding the strategic imperatives driving MarTech adoption to navigating the intricacies of data-driven customer interaction, this book provides invaluable insights and practical strategies. Explore topics ranging from budget allocation and market potential to data readiness and GDPR compliance, gaining a deep understanding of key concepts and best practices. Whether you're grappling with the complexities of AI integration or seeking to optimize measurement and KPIs, this book equips you with the knowledge and tools needed to thrive in today's digital marketing environment. With decades of industry experience, Ralf Strauss offers in this book a roadmap for success, empowering marketers to navigate the challenges and seize the opportunities presented by MarTech innovation.
Data-Driven Customer Experience Transformation: Optimize Your Omnichannel Approach
by Mohamed ZakiWe are living in an experience-driven economy, where the customer's experience is paramount and even beloved brands risk losing market share due to a single negative customer experience.In our technology-led, omnichannel environment, one of the biggest risks for brands is a lack of consistency in their customer experience across digital, physical and social channels. Data-driven Customer Experience Transformation provides insights and frameworks for creating delightful customer experiences across all three channels, by leveraging data and the latest technologies. Using cutting-edge research from the Cambridge Service Alliance, at the University of Cambridge, this book explores the importance of omnichannel customer-centricity across all sectors and takes you on a journey from setting your strategy, through designing and managing your customer experiences in real-time. It explores how AI can be used to identify opportunities and predict engagement, as well as how to use data to understand customer loyalty, forge stronger customer relationships and drive growth.By combining academic rigour with real-world examples from leading companies such as Microsoft, KFC and Emirates Airline, this book is the ultimate guide to designing and implementing an exceptional data-driven customer experience across all channels, whether you work in B2B, B2C or public services.
Data-Driven Cyber Physical Systems
by Xiaolong Wu Fangyu Li Honggui HanThis book shows the exploration and integration of data-driven approaches within cyber-physical systems (CPS), focusing on how these systems leverage data science, artificial intelligence, and machine learning to enhance performance, optimize real-time decision-making, and improve the interaction between physical and digital components. Readers will be interested in the areas of data acquisition, integration, storage, modeling, simulation, fault detection, predictive maintenance, and cybersecurity. Because these topics highlight how data-driven approaches and advanced technologies can be applied to optimize system performance, enhance real-time decision-making, and ensure the safety and reliability of DDCPS. Additionally, practical applications across various industries demonstrate the real-world impact. The inclusion of real-world examples and practical applications helps bridge the gap between theory and practice, making the content highly relevant for professionals and researchers. Additionally, the book covers emerging trends and technologies, offering readers insights into the future of DDCPS. Readers will gain a comprehensive understanding of how to leverage data-driven approaches to enhance the performance and reliability of DDCPS.
Data-Driven Cybersecurity: Reducing risk with proven metrics
by Mariano MatteiMeasure, improve, and communicate the value of your security program.Every business decision should be driven by data—and cyber security is no exception. In Data-Driven Cybersecurity, you'll master the art and science of quantifiable cybersecurity, learning to harness data for enhanced threat detection, response, and mitigation. You&’ll turn raw data into meaningful intelligence, better evaluate the performance of your security teams, and proactively address the vulnerabilities revealed by the numbers. Data-Driven Cybersecurity will teach you how to: • Align a metrics program with organizational goals • Design real-time threat detection dashboards • Predictive cybersecurity using AI and machine learning • Data-driven incident response • Apply the ATLAS methodology to reduce alert fatigue • Create compelling metric visualizations Data-Driven Cybersecurity teaches you to implement effective, data-driven cybersecurity practices—including utilizing AI and machine learning for detection and prediction. Throughout, the book presents security as a core part of organizational strategy, helping you align cyber security with broader business objectives. If you&’re a CISO or security manager, you&’ll find the methods for communicating metrics to non-technical stakeholders invaluable. Foreword by Joseph Steinberg. About the technology A data-focused approach to cybersecurity uses metrics, analytics, and automation to detect threats earlier, respond faster, and align security with business goals. About the book Data-Driven Cybersecurity shows you how to turn complex security metrics into evidence-based security practices. You&’ll learn to define meaningful KPIs, communicate risk to stakeholders, and turn complex data into clear action. You&’ll begin by answering the important questions: what makes a &“good&” security metric? How can I align security with broader business objectives? What makes a robust data-driven security management program? Python scripts and Jupyter notebooks make collecting security data easy and help build a real-time threat detection dashboards. You&’ll even see how AI and machine learning can proactively predict cybersecurity incidents! What's inside • Improve your alert system using the ATLAS framework • Elevate your organization&’s security posture • Statistical and ML techniques for threat detection • Executive buy-in and strategic investment About the reader For readers familiar with the basics of cybersecurity and data analysis. About the author Mariano Mattei is a professor at Temple University and an information security professional with over 30 years of experience in cybersecurity and AI innovation. Table of Contents Part 1 Building the foundation 1 Introducing cybersecurity metrics 2 Cybersecurity analytics toolkit 3 Implementing a security metrics program 4 Integrating metrics into business strategy Part 2 The metrics that matter 5 Establishing the foundation 6 Foundations of cyber risk 7 Protecting your assets 8 Continuous threat detection 9 Incident management and recovery Part 3 Beyond the basics: Advanced analytics, machine learning and AI 10 Advanced cybersecurity metrics 11 Advanced statistical analysis 12 Advanced machine learning analysis 13 Generative AI in cybersecurity metrics
Data-Driven Decision Making
by Vinod Sharma Chandan Maheshkar Jeanne PouloseThis book delves into contemporary business analytics techniques across sectors for critical decision-making. It combines data, mathematical and statistical models, and information technology to present alternatives for decision evaluation. Offering systematic mechanisms, it explores business contexts, factors, and relationships to foster competitiveness. Beyond managerial perspectives, it includes contributions from professionals, academics, and scholars worldwide, delivering comprehensive knowledge and skills through diverse viewpoints, cases, and applications of analytical tools. As an international business science reference, it targets professionals, academics, researchers, doctoral scholars, postgraduate students, and research organizations seeking a nuanced understanding of modern business analytics.
Data-Driven Decision Making for Sustainable Business Growth (Studies in Systems, Decision and Control #607)
by Ismail QasemThis book provides a comprehensive guide to data-driven decision-making for sustainable business growth. It offers practical insights and methodologies for leveraging data analytics to enhance sustainability initiatives and drive business success. Designed for business professionals, data scientists, and sustainability practitioners, the book bridges the gap between data science and sustainable business practices. By integrating data-driven strategies with sustainability goals, it addresses the critical need for informed decision-making in today's data-centric business environment. The main topics covered in this book include data analytics for sustainability, predictive modelling, big data applications, and the role of artificial intelligence in decision-making. These topics are crucial as they enable businesses to make informed decisions, optimize resource use, and improve overall sustainability performance. The book also explores case studies of successful data-driven sustainability initiatives, providing real-world examples and actionable insights. The relevance of these topics is significant in the modern business landscape, where data is a valuable asset. This book not only highlights the importance of data-driven decision-making for sustainability but also provides practical tools and techniques to implement these strategies effectively. It aims to empower businesses to harness the power of data for sustainable growth, enhancing their competitive advantage and contributing to a more sustainable future. The problem this book sets out to solve is the lack of clear guidance on integrating data analytics with sustainability initiatives. Many businesses struggle to utilize data effectively to achieve their sustainability goals. This book offers a roadmap for leveraging data to drive sustainable business practices, showcasing best practices and innovative approaches. The target audience for this book includes business leaders, data analysts, sustainability officers, researchers, and students in business and data science fields. By offering a blend of theoretical knowledge and practical applications, this book aims to equip readers with the skills needed to make data-driven decisions for sustainable business growth.
Data-Driven Decision Making in Entrepreneurship: Tools for Maximizing Human Capital
by Nikki Blacksmith Maureen E. McCuskerSince 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.
Data-Driven Decision Support System in Intelligent HealthCare
by Yu-Chen Hu Debnath BhattacharyyaMachine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models.Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field.This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.
Data-Driven Decision-Making for Business
by Claus Grand BangResearch shows that companies that employ data-driven decision-making are more productive, have a higher market value, and deliver higher returns for their shareholders. In this book, the reader will discover the history, theory, and practice of data-driven decision-making, learning how organizations and individual managers alike can utilize its methods to avoid cognitive biases and improve confidence in their decisions. It argues that value does not come from data, but from acting on data.Throughout the book, the reader will examine how to convert data to value through data-driven decision-making, as well as how to create a strong foundation for such decision-making within organizations. Covering topics such as strategy, culture, analysis, and ethics, the text uses a collection of diverse and up-to-date case studies to convey insights which can be developed into future action. Simultaneously, the text works to bridge the gap between data specialists and businesspeople. Clear learning outcomes and chapter summaries ensure that key points are highlighted, enabling lecturers to easily align the text to their curriculums.Data-Driven Decision-Making for Business provides important reading for undergraduate and postgraduate students of business and data analytics programs, as well as wider MBA classes. Chapters can also be used on a standalone basis, turning the book into a key reference work for students graduating into practitioners. The book is supported by online resources, including PowerPoint slides for each chapter.
Data-Driven Design for Computer-Supported Collaborative Learning: Design Matters (Lecture Notes in Educational Technology)
by Lanqin ZhengThis book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research.The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.
Data-Driven Energy Management and Tariff Optimization in Power Systems: Shaping the Future of Electricity Distribution through Analytics
by Pierluigi Siano Josep M. Guerrero Hamidreza Arasteh Niki MoslemiPresents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning. Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book’s accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges. Integrating rigorous analysis with application-oriented strategies, this book: Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff designProvides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrationsHighlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy dataAddresses the integration of renewable energy sources into existing infrastructures through data-driven optimization Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
Data-Driven Engineering Design
by Yuchen Wang Ang Liu Xingzhi WangThis book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design.Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation.Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.
Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science (Studies in Computational Intelligence #975)
by Yaochu Jin Handing Wang Chaoli SunIntended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture
by Syed Nisar Hussain BukhariIn the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
Data-Driven Global Optimization Methods and Applications
by Peng Wang Huachao Dong Jinglu LiThis book presents recent advances in data-driven global optimization methods, combining theoretical foundations with real-world applications to address complex engineering optimization challenges.The book begins with an overview of the state of the art, key technologies and standard benchmark problems in the field. It then delves into several innovative approaches: space reduction-based, hybrid surrogate model-based and multi-surrogate model-based global optimization, followed by surrogate-assisted constrained global optimization, discrete global optimization and high-dimensional global optimization. These methods represent a variety of optimization techniques that excel in both optimization capability and efficiency, making them ideal choices for complex engineering optimization problems. Through benchmark test problems and real-world engineering applications, the book illustrates the practical implementation of these methods, linking established theories with cutting-edge research in industrial and engineering optimization.Both a professional book and an academic reference, this title will provide valuable insights for researchers, students, engineers and practitioners in a variety of fields, including optimization methods and algorithms, engineering design and manufacturing and artificial intelligence and machine learning.
Data-Driven HR: How to Use Analytics and Metrics to Drive Performance
by Bernard MarrTraditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-Driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-Driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.
Data-Driven Innovation for Intelligent Technology: Perspectives and Applications in ICT (Studies in Big Data #148)
by Jorge Brieva Lourdes Martínez-Villaseñor Hiram Ponce Ernesto Moya-Albor Octavio Lozada-FloresThis book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value.It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others.This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems.