From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

This book constitutes the refereed proceedings of the 11th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage. The 45 revised full papers were carefully reviewed and selected from 120 submissions. The papers cover many dimensions including security algorithms and architectures, privacy-aware policies, regulations and techniques, anonymous computation and communication, encompassing fundamental theoretical approaches, practical experimental projects, and commercial application systems for computation, communication and storage.

Smart cities emanate from a smart renewable-energy-aided power grid. The smart grid technologies offer an array of benefits like reliability, availability, and resiliency. Smart grids phenomenally contribute to facilitating cities reaching those sustainability goals over time. Digital technologies, such as the Internet of Things (IoT), automation, artificial intelligence (AI) and machine learning (ML) significantly contribute to the two-way communication between utilities and customers in smart cities. Five salient features of this book are as follows: Smart grid to the smart customer Intelligent computing for smart grid applications Novel designs of IoT systems such as smart healthcare, smart transportation, smart home, smart agriculture, smart manufacturing, smart grid, smart education, smart government, smart traffic management systems Innovations in using IoT and AI in improving resilience of smart energy infrastructure Challenges and future research directions of smart city applications

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

In the face of rapid digitalization and environmental challenges, the world stands at a critical juncture. The relentless pace of technological advancement has brought unparalleled convenience and efficiency but has also contributed to unsustainable consumption patterns, resource depletion, and environmental degradation. Despite growing awareness, many industries need help integrating sustainable practices into their operations, hindered by a lack of understanding, resources, and clear guidelines. Moreover, the complexity of the circular economy and the ethical dimensions of digitalization pose significant challenges, requiring innovative solutions and comprehensive guidance. Navigating the Circular Age of a Sustainable Digital Revolution offers a timely and comprehensive solution to these pressing challenges. By exploring the intricate relationship between technology and sustainability, this book provides a roadmap for businesses, policymakers, and individuals to embrace sustainable practices in the digital era. Researchers and scholars gain profound insights from this book into the dynamics between digitalization and sustainable practices while policymakers find nuanced analyses to shape regulatory frameworks. Business leaders and professionals discover practical guidance for sustainable business models and digital transformation, and technology practitioners align their fields with sustainable advancements. Ultimately, the book empowers individuals and organizations to shape a future where technology and sustainability coexist, fostering a more sustainable and prosperous world.

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

INTERMITTENT DEMAND FORECASTING The first text to focus on the methods and approaches of intermittent, rather than fast, demand forecasting Intermittent Demand Forecasting is for anyone who is interested in improving forecasts of intermittent demand products, and enhancing the management of inventories. Whether you are a practitioner, at the sharp end of demand planning, a software designer, a student, an academic teaching operational research or operations management courses, or a researcher in this field, we hope that the book will inspire you to rethink demand forecasting. If you do so, then you can contribute towards significant economic and environmental benefits. No prior knowledge of intermittent demand forecasting or inventory management is assumed in this book. The key formulae are accompanied by worked examples to show how they can be implemented in practice. For those wishing to understand the theory in more depth, technical notes are provided at the end of each chapter, as well as an extensive and up-to-date collection of references for further study. Software developments are reviewed, to give an appreciation of the current state of the art in commercial and open source software. “Intermittent demand forecasting may seem like a specialized area but actually is at the center of sustainability efforts to consume less and to waste less. Boylan and Syntetos have done a superb job in showing how improvements in inventory management are pivotal in achieving this. Their book covers both the theory and practice of intermittent demand forecasting and my prediction is that it will fast become the bible of the field.” —Spyros Makridakis, Professor, University of Nicosia, and Director, Institute for the Future and the Makridakis Open Forecasting Center (MOFC). “We have been able to support our clients by adopting many of the ideas discussed in this excellent book, and implementing them in our software. I am sure that these ideas will be equally helpful for other supply chain software vendors and for companies wanting to update and upgrade their capabilities in forecasting and inventory management.” —Suresh Acharya, VP, Research and Development, Blue Yonder. “As product variants proliferate and the pace of business quickens, more and more items have intermittent demand. Boylan and Syntetos have long been leaders in extending forecasting and inventory methods to accommodate this new reality. Their book gathers and clarifies decades of research in this area, and explains how practitioners can exploit this knowledge to make their operations more efficient and effective.” —Thomas R. Willemain, Professor Emeritus, Rensselaer Polytechnic Institute.