This two-volume set (CCIS 1367-1368) constitutes reviewed and selected papers from the 10th International Advanced Computing Conference, IACC 2020, held in December 2020. The 65 full papers and 2 short papers presented in two volumes were thorougly reviewed and selected from 286 submissions. The papers are organized in the following topical sections: Application of Artificial Intelligence and Machine Learning in Healthcare; Using Natural Language Processing for Solving Text and Language related Applications; Using Different Neural Network Architectures for Interesting applications; ​Using AI for Plant and Animal related Applications.- Applications of Blockchain and IoT.- Use of Data Science for Building Intelligence Applications; Innovations in Advanced Network Systems; Advanced Algorithms for Miscellaneous Domains; New Approaches in Software Engineering.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

The massive increase in energy demand and the related rapid development of unconventional reservoirs has opened up exciting new energy supply opportunities along with new, seemingly intractable engineering and research challenges. The energy industry has primarily depended on a heuristic approach—rather than a systematic approach—to optimize and tackle the various challenges when developing new and improving the performance of existing unconventional reservoirs. Industry needs accurate estimations of well production performance and of the cumulative estimated ultimate reserves, accounting for uncertainty. This Special Issue presents 10 original and high-quality research articles related to the modeling of unconventional reservoirs, which showcase advanced methods for fractured reservoir simulation, and improved production forecasting techniques.

Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.

This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.

Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

This book aims at bringing together global researchers to generate thought on how this transition from Industry 4.0 to Industry 5.0 could make a difference to the globe for larger good. The collaboration and interaction between man and machine has given rise to Industry 5.0. With the prime objective of Industry 5.0 to create a benefit for the human beings while tapping on to the advantage of Industry 4.0, in no case, does it replace what has already been achieved. In fact, it brings to light what can be done in order to make life better. While Industry 4.0 offered extraordinary technological advancement, Industry 5.0 reasons out that technology alone is not sufficient to answer everything or provide a solution, but it is an amalgamation of both machine and human interaction to create that difference. In fact, with the impact of widespread digitalization that has led to dehumanization of the industrial makeup, the interest of global researchers has increased toward mapping how the human creativity and brainpower can be reconciled with the intelligent systems that can enhance process efficiency. Industry 5.0 has touched upon some of those key domains which are of much concern and debate globally including resilience (both business and cyber), environment and sustainability, diversity and inclusion, values and ethics, vision and purpose, circular economy, understanding the human–machine collaboration and the ‘human-touch’ in the production process. This transition that has taken place in moving from Industry 4.0 to Industry 5.0 has essentially created a need to pay cognizance to the role of ‘human’ in the process which creates an enhanced focus toward the right kind of skills and competencies, identification of training and developmental needs, talent acquisition and management, safety and wellbeing, future of work as well as hybrid working models. Undeniably, the pace with which Industry 4.0 has been accelerating has bypassed the first three industrial revolutions, which is definitely a consequence of the fast introduction of new and cutting-edge technologies. While organizations are already in analyzing the context, mapping this transition and the flow of activities from Industry 4.0 to 5.0 is gaining attention as Industry 4.0 lacked personalization and customization. This co-existence of man and machine creates a pathway for newer prospects and opportunities to emerge and expand possibilities of personalization with the empowerment of ‘human’ in the production process. This lays the foundation for this book. This book adopts a forward-looking approach by bringing in research and contributions that facilitate in mapping the consereasons, consequences and solutions for ‘man+machine’ across industries. This book serves as a guide not just to academia but also to the industry to adopt suitable strategies that offer insights into global best practices as well as the innovations in the domain.