The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience. Features The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners

Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it's important to find a resource you connect with to guide your learning. Luckily, there's no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. * Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools * Offers practical advice for each major area of development with both Python 3.x and Python 2.x * Based on the latest research in cognitive science and learning theory * Helps the reader learn how to write effective, idiomatic Python code by leveraging its best-and possibly most neglected-features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars.

Exploratory data analysis helps to recognize natural patterns hidden in the data. This book describes the tools for hypothesis generation by visualizing data through graphical representation and provides insight into advanced analytics concepts in an easy way. The book addresses the complete data visualization technologies workflow, explores basic and high-level concepts of computer science and engineering in medical science, and provides an overview of the clinical scientific research areas that enables smart diagnosis equipment. It will discuss techniques and tools used to explore large volumes of medical data and offers case studies that focus on the innovative technological upgradation and challenges faced today. The primary audience for the book includes specialists, researchers, graduates, designers, experts, physicians, and engineers who are doing research in this domain.

Convergence of Blockchain, AI and IoT: A Digital Platform discusses the convergence of three powerful technologies that play into the digital revolution and blur the lines between biological, digital, and physical objects. This book covers novel algorithms, solutions for addressing issues in applications, security, authentication, and privacy. Discusses innovative technological upgradation and significant challenges in the current era Gives an overview of clinical scientific research that enables smart diagnosis through artificial intelligence Provides an insight into how disruptive technology enabled with the self-running devices and protection mechanism is involved in an augmented reality with blockchain mechanism Talks about neural science being capable of enhancing deep brain waves to predict an overall improvement in human thoughts and behaviours Covers the digital currency mechanism in detail Enhances the knowledge of the readers about smart contract and ledger mechanism with artificial intelligence and blockchain mechanism Targeted audiences range from those interested in the technical revolution of blockchain, big data and the Internet of Things, to research scholars and the professional market.

Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management. The traditional framework of Human Resource Management (HRM) primarily consists of four functions: Human Resource Planning, Recruitment and Selection, Training and Development and Performance Management Systems which help businesses in hiring, motivating and retaining employees. The introduction of Artificial Intelligence (AI) has changed all of these aspects. This second volume in The Adoption and Effect of Artificial Intelligence on Human Resources Management Series reflects upon: the accuracy of using AI to nominate candidates; the change readiness of employees; impacts and challenges on HRM practices; effects on upskilling; and the effects of the pandemic; concluding with an overall state of industry trends. This book is ideal for HR managers, senior managers, researchers, and students planning a career in this field.

Big data - unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling and incentivize risk reduction - is a relatively recent development in the insurance industry, the data sets previously being too impossibly great to analyse through traditional methods. However, with the global capacity to collect and store data growing alongside advancements in AI and machine learning technology, insurers need to seriously evaluate their technology stacks to ensure they can remain competitive and respond to growing customer demand. Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional. Providing high quality academic research, Emerald Studies in Finance, Insurance, and Risk Management provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale. Subjects of interest may include banking, accounting, auditing, compliance, sustainability, behaviour, management, and business economics.

Big Data Analytics in the Insurance Market is an industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. This book will be a 'must' for people seeking to broaden their knowledge of big data concepts and their real-world applications, particularly in the field of insurance. The insurance industry is largely dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential. Yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. This book an invaluable resource for any insurance professional from practitioners and policymakers working at insurance companies, to undergraduate and graduate students of economics management, and finance. Providing high quality academic research, ESFIRM provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale.

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book: Converges Blockchain, Big Data and Machine learning in one volume. Connects Blockchain technologies with the data centric applications such Big data and E-Health. Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries. Covers big data analytics examples using R. Includes lllustrative examples in python for blockchain creation.

This book explores potentially disruptive and transformative healthcare-specific use cases made possible by the latest developments in Internet of Things (IoT) technology and Cyber-Physical Systems (CPS). Healthcare data can be subjected to a range of different investigations in order to extract highly useful and usable intelligence for the automation of traditionally manual tasks. In addition, next-generation healthcare applications can be enhanced by integrating the latest knowledge discovery and dissemination tools. These sophisticated, smart healthcare applications are possible thanks to a growing ecosystem of healthcare sensors and actuators, new ad hoc and application-specific sensor and actuator networks, and advances in data capture, processing, storage, and mining. Such applications also take advantage of state-of-the-art machine and deep learning algorithms, major strides in artificial and ambient intelligence, and rapid improvements in the stability and maturity of mobile, social, and edge computing models.

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book: Converges Blockchain, Big Data and Machine learning in one volume. Connects Blockchain technologies with the data centric applications such Big data and E-Health. Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries. Covers big data analytics examples using R. Includes lllustrative examples in python for blockchain creation.

This book explores potentially disruptive and transformative healthcare-specific use cases made possible by the latest developments in Internet of Things (IoT) technology and Cyber-Physical Systems (CPS). Healthcare data can be subjected to a range of different investigations in order to extract highly useful and usable intelligence for the automation of traditionally manual tasks. In addition, next-generation healthcare applications can be enhanced by integrating the latest knowledge discovery and dissemination tools. These sophisticated, smart healthcare applications are possible thanks to a growing ecosystem of healthcare sensors and actuators, new ad hoc and application-specific sensor and actuator networks, and advances in data capture, processing, storage, and mining. Such applications also take advantage of state-of-the-art machine and deep learning algorithms, major strides in artificial and ambient intelligence, and rapid improvements in the stability and maturity of mobile, social, and edge computing models.

The Covid 19 pandemic has created chaos in the business world and forced leaders to rethink their operational status quo. Balancing the physical and virtual spaces of the global digital economy wherein economic, commercial, and professional transactions are enabled by information and communication technologies has called for additional support from data-driven technologies like smart analytics and artificial intelligence. Opportunities created within digital economies to leverage technologies to execute tasks better, faster, and often differently have found the desired prominence in the recent past. Though the benefits outweigh the risks, the challenges in digitalised economies are as sophisticated as the solutions they offer. Contemporary Studies in Economic and Financial Analysis publishes a series of current and relevant themed volumes within the fields of economics and finance. Both disciplinary and interdisciplinary studies are welcome.

The Covid 19 pandemic has created chaos in the business world and forced leaders to rethink their operational status quo. Balancing the physical and virtual spaces of the global digital economy wherein economic, commercial, and professional transactions are enabled by information and communication technologies has called for additional support from data-driven technologies like smart analytics and artificial intelligence. Opportunities created within digital economies to leverage technologies to execute tasks better, faster, and often differently have found the desired prominence in the recent past. Though the benefits outweigh the risks, the challenges in digitalised economies are as sophisticated as the solutions they offer. Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalized Economy presents various viewpoints on topics like artificial intelligence, blockchain technology, digitalisation in various sectors, technology issues like cybersecurity and financial inclusion, and technology-enabled banking issues like money laundering. The theme of sustainability forms the core of the book.

Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management. The traditional framework of Human Resource Management (HRM) primarily consists of four functions: Human Resource Planning, Recruitment and Selection, Training and Development and Performance Management Systems which help businesses in hiring, motivating and retaining employees. The introduction of Artificial Intelligence (AI) has changed all of these aspects. The Adoption and Effect of Artificial Intelligence on Human Resources Management Series sets out a blended approach towards different functions of HRM and AI, reflecting upon: HR data privacy; data security; diversity and inclusion using AI; and sustainable AI-HR practices. This book is ideal for HR managers, senior managers, researchers, and students planning a career in this field.

Increased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making. This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19. Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed. Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.