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Wiley-VCH - Computer Science
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Machine Learning for Business Analytics
Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second Python edition of Machine Learning for Business Analytics. This edition also includes: * A new chapter on generative AI (large language models or LLMs, and image generation) * An expanded chapter on deep learning * A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning * A new chapter on responsible data science * Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students * A full chapter of cases demonstrating applications for the machine learning techniques * End-of-chapter exercises with data * A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology [720 Pages, Hardcover]
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Fundamentals of Robust Machine Learning
An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: * A blend of robust statistics and machine learning principles * Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection * Python code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers. [416 Pages, Hardcover]
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Optimizing Biofuel Production with Artificial Intelligence
Optimizing Biofuel Production with Artificial Intelligence will help readers discover how integrating artificial intelligence with biotechnological advancements can revolutionize biofuel production, ensuring a sustainable energy future in response to pressing global challenges like pollution and climate change. This book presents artificial intelligence as a technique to aid the production of biofuels. Recently, tremendous developments have been made in energy and environmental biotechnologies, spurred by societal issues like pollution control, energy security, and climate change. Energy can be obtained from a variety of sources, including coal, oil, natural gas, solar, wind, and nuclear energy. The need to transition to new energy results from finite resources and economic sustainability. Biotechnological process optimization is crucial for ensuring a quality final product and boosting bioconversion performance efficiency. When combined with traditional simulation and modeling methods, artificial intelligence and computer technology can help define ideal process parameters and save total process costs. The energy sector can benefit from artificial intelligence in several ways, including increased asset efficiency, early detection and assessment of wildfire risks, assistance with vegetation management and storm recovery, and optimized energy use. The new frontier for energy is biomass. [384 Pages, Hardcover]
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Cloud Computing in Smart Energy Meter Management
Cloud Computing in Smart Energy Meter Management equips you with essential insights and practical solutions for effectively managing smart meter data through cutting-edge technologies like artificial intelligence and cloud computing, making it an invaluable resource for anyone looking to enhance their understanding of modern energy management. Cloud Computing in Smart Energy Meter Management presents a structured review of the current research on smart energy meters with artificial intelligence and cloud computing solutions. This book will help provide solutions for processing and analyzing the massive amounts of data involved in smart meters through cloud computing. Readers will learn about data storage, processing, and dynamic pricing of smart energy data in the cloud, as well as smart metering concepts dealing with the flow of power consumption from consumer to utility center. It offers an in-depth explanation of advanced metering infrastructure (AMI) which includes meter installation, meter advising, commissioning, integration, master data synchronization, billing, customer interface, complaints, and resolution. In smart cities, components in household energy meters are fitted with sensors and can interconnect with the Internet of Things to measure power consumption with an automated meter reading. This book also acts as a new resource describing new technologies involved in the integration of smart metering with existing cellular networks. Cloud Computing in Smart Energy Meter Management provides knowledge on the vital role played by artificial intelligence and cloud computing in smart energy meter reading with precise evaluations. [544 Pages, Hardcover]
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Distributed Time-Sensitive Systems
The book provides invaluable insights into cutting-edge advancements across multiple sectors of Society 5.0, where contemporary concepts and interdisciplinary applications empower you to understand and engage with the transformative technologies shaping our future. Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices. The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape. Audience Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0 [400 Pages, Hardcover]
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