Ali Soofastaei – författare

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

The evolution of modern technology has affected all the industry dimensions. Mother industries play a critical role in providing the precursor materials for other industries, and a small improvement in these can make a big change in others. This book covers the analytics revolution in Industry 4.0 for the mother industries, such as mining, oil and gas, and steel. It focuses on the use of advanced analytics and artificial intelligence to improve the business decisions aimed at increasing the quality and quantity of mother industries' products. It helps to design and implement their digital transformation strategies in these industries.Key Features:Provides a concise overview of state of the art for mother industries' executives and managers.Highlights and describes critical opportunity areas for industry operations optimization.Explains how to implement advanced data analytics through case studies and examples.Provides approaches and methods to improve data-driven decision-making.Brings experience and learning in digital transformation from adjacent sectors.This book is aimed at researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering.

Advanced analytics is revolutionizing the industrial sector, empowering technology enterprises to harness online data sources for actionable insights. These insights drive advancements in everything from operational process automation to enhancing customer experiences. This volume explores the transformative benefits of advanced analytics across technology-driven industries such as computing, telecommunications, aerospace, electronics, robotics, and e-commerce. It delves into how reduced costs, democratized data, and faster management capabilities enable dynamic forecasting, complete supply chain visibility, and enhanced security measures to ensure privacy compliance – even in remote work environments.Key features:A concise overview of cutting-edge analytics tailored for executives and managers in advanced industriesIdentification of critical opportunities for optimizing industrial operationsPractical guidance on implementing advanced data analytics through real-world case studies and examplesProven approaches and methodologies to enhance data-driven decision-makingInsights and lessons from digital transformation efforts in adjacent sectorsThis volume is an essential resource for researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering.

Digital solutions are needed to develop advanced analytics applications in different industries. The main objectives of this two volume set are presenting the scientific concepts and providing industrial case studies for different applications of advanced analytics, which can be grouped into three main areas namely descriptive, predictive and prescriptive analytics. Main prerogatives include improving understanding of the business value and applicability of different analytic approaches, and business framework to assess the value, cost, and risk of potential analytic solutions. It covers pertinent aspects of data analytics for mother and technology industries.Provides a concise overview of state of the art for mother and technology industries executives and managersHighlights and describes critical opportunity areas for industries operations optimizationExplains how to implement advanced data analytics through case studies and examplesProvides approaches and methods to improve data-driven decision makingBrings experience and learning in digital transformation from adjacent sectorsThis two volume set aims at researchers, professionals, graduate students in data science, manufacturing, automation and computer engineering

This book explains what artificial intelligence is, explores its use cases and applications, and helps readers understand key AI concepts and terms such as machine learning, deep learning, and neural networks. It also examines various issues and concerns surrounding AI, including ethics, bias, and the impact on jobs. In addition, it guides readers on learning AI and starting a career in the field. It is designed for both technical and non‑technical audiences, and it introduces the fundamentals of AI in an accessible and practical way.Key Features:Explains what AI is, its applications and use cases, and how it is transforming our lives.Clarifies common AI terminology, including neural networks, machine learning, deep learning, and data science.Navigates ethical and societal discussions surrounding AI.Helps readers build machine learning and data science projects.Supports professionals in working with AI teams and developing AI strategies within their organizations.This book is intended for professionals in applied data analytics, artificial intelligence, and related academic fields.

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time.Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results.From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing.Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in yourcompany’s data.The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.