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The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics for performing so many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how the proper decision depends on a number of factors, the book examines how AI and ML can be used to model equations and define constraints to solve more easily problems and discover proper and valid solutions. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies look at how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI enabled technologies.

The book provides a comprehensive examination of the integration of IoT technology into various industries and its impact on daily life, with a focus on the most recent advancements in the field. The technical aspects of IoT are thoroughly discussed, including the implementation of cutting-edge sensors, data communication protocols, and network topologies. The book also covers the latest advancements in areas such as edge computing, 5G networks, and AI-powered IoT devices. Emphasis is placed on the examination of IoT in real-world applications, including healthcare, agriculture, transportation, and home automation. Other highlights of the book include: IoT-based systems for monitoring air and water quality. Wearable devices for continuous monitoring of vital signs and other health metrics. IoT-based systems for monitoring and optimizing crop growth and yields. Connected vehicles for improved safety, efficiency, and traffic management. Monitoring of goods and resources in transit to optimize delivery times. With case studies and real-world examples, readers gain a comprehensive understanding of how IoT is revolutionizing various industries and enhancing daily life. This book is a comprehensive guide to the exciting world of IoT and its practical application.

Practical Project Research is a collection of essays from key researchers in the field of project management who describe what they feel are the most impactful findings from research. In the challenging and competitive world of project management, project managers need all the insight they can get. Leading researchers share what they believe are the most important findings from the research being done today. These cover such pressing topics confronting project managers as hybrid methodologies, schedule overrun and schedule estimation, project efficiency, and managing local stakeholders. Highlights include: Jeff Pinto and Kate Davis explore the "Normalization of Deviance" (NoD) phenomenon within various organizational settings, focusing on projects. NoD involves the gradual acceptance of deviant practices, diverging from established norms and often leading to detrimental outcomes. Francesco Di Maddaloni investigates how local communities' stakeholders are perceived, identified, and categorized by project managers in major public infrastructure and construction projects (MPIC). His chapter helps project managers to have a better understanding of a more inclusive and holistic approach to engage with a broader range of stakeholders. Lavagnon Ika, Peter Love, and Jeff Pinto suggest that error and bias combine to exact a toll on major projects and offers theoretical insights and outlines practical recommendations for project managers. Jonas Söderlund offers managerial guidelines for leveraging deadlines as powerful tools for generating project success. Pedro Serrado looks at empirical studies that link planning quality to project success, emphasizing its importance. He also discusses the downsides of excessive planning, particularly in dynamic environments and research and development projects.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and machine learning as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing so many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how the proper decision depends on a number of factors, the book examines how AI and ML can be used to model equations and define constraints to solve more easily problems and discover proper and valid solutions. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

They say that repeating the same thing and expecting a different result is insanity. This book aims to analyze the reasons for failure in project management. It is filled with stories, anecdotes and satires that highlight how organizations and project managers fall into an "insanity spiral". It provides seven Sanity Checks designed to keep project managers from repeating the same mistakes and to help them become project champions: The first sanity check is how and when to appoint a project manager. This first sanity check may be familiar and may well bring back memories of starting a career in project management. The second sanity check is the comprehension of why a project is needed. It helps to overcome the misunderstanding that many have on the nature of projects and its management. The third sanity check is the understanding of the unknown and emphasizes the importance of risk management. The fourth sanity check is capturing who needs what. It is about the constant pursuit to satisfy a host of individuals and at times the, sometimes seemingly, unsurmountable quest to secure resources for a project. The fifth sanity check is who does what. It also deals with satisfying stakeholders and obtaining resources. The sixth sanity check is outside assistance. It is all about breaking the us versus them syndrome when outsourcing in a project. The seventh and most important sanity check is engaging the efforts of others as it deals with people--the lifeblood of any organization.

A large number of healthcare employees, whether they are on the frontlines or in management, work in complex, fluid environments. They must perform a diverse number of sometimes intricate tasks on a daily basis as well as excel in handling ad hoc interactions with patients, residents, coworkers, or other stakeholders. In the coming years, these workers also will have to adjust to disruptions in their workplaces that are brought about by the introduction of new Artificial Intelligence (AI) systems and health information technologies (HITs) into their offices and clinics. Many of these workers will find themselves competing for jobs not only with other humans but also with machines.Preparing Healthcare Workers for an AI-Driven Workplace helps healthcare professionals to develop their core critical thinking skills while also enabling them to develop methodologies for successfully completing complex projects by themselves, dealing with ad hoc interactions, and taking advantage of the coming AI- and IT-driven changes in their workplaces. The book begins with explaining why healthcare workers, whether they work on the frontlines or in management, need to be strong critical thinkers. It breaks down "critical thinking" into its key elements and provides methods that readers can use to help them to master critical thinking and grow to become elite critical thinkers. The book also provides tips on how to handle ad hoc conversations with supervisors, coworkers, patients, residents, and other stakeholders. Examining how AI- and IT-related developments will transform the healthcare ecosystem in the coming years, the book identifies key mindsets and strategies for thriving in technologically rich healthcare environments.

The concept of customer relationship management (CRM) has grown from the loosely defined methodology of using customer transactions for developing profiles on customers to the well-defined business process of using sophisticated tools and analytical processes for managing each customer on an individual basis. CRM integrates e-mail and the PDA with the day planner, electronic scheduler, client database, and a number of other business management tools so you can create a single point from which to manage customer relationships. The Customer Relationship Management Systems Handbook provides a complete and detailed analysis of CRM, its origins, rationale, implementation strategies, core technologies, and benefits. The author takes readers through the evolution of CRM- from its early beginning as a tool for better managing and utilizing vast amounts of customer transaction data acquired in day-to-day transactions to today's sophisticated data warehouse-based systems. The text was researched, formatted, and written for IS professionals who need a full understanding of what is involved in the successful development and implementation of a CRM. To highlight the significant benefits of implementing CRM strategies, the book provides examples of successful CRM implementations from a broad range of business sectors. These implementations, presented in a case study format, demonstrate implementation processes, appropriate technologies, and vendor solutions that work. Wherever possible, illustrations are used to enhance the textual presentation. The complete analysis of CRM provided in the Customer Relationship Management Systems Handbook will enable you to accomplish what many businesses fail to do-put the customer first.

This book focuses on data analytics with machine learning using IoT and blockchain technology. Integrating these three fields by examining their interconnections, it examines the opportunities and challenges of developing systems and applications exploiting these technologies.

With the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them.Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of big data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussion about the important sectors where big data analytics has proven to be very effective in improving performance and helping industries to remain competitive. This book provides insight into the novel areas of big data analytics and the research directions for the scholars working in the domain. Highlights include: The advantages, disadvantages and challenges of big data analytics State-of-the-art technologies for big data analytics such as Hadoop, NoSQL databases, data lakes, deep learning and blockchain The application of big data analytic in healthcare, business, social media analytics, fraud detection and prevention and governance Exploring the concepts and technologies behind big data analytics, the book is an ideal resource for researchers, students, data scientists, data analysts and business analysts who need insight into big data analytics

This book explores the concept and applications of AI-based analytics related to marketing and business. It also discusses future research directions in this domain. It covers both aspects of artificial intelligence-based marketing and business that are helpful for business management to succeed in the marketplace by creating value.

The book emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry.