Accelerating Discoveries In Data Science And Artificial Intelligence Ii

This edited volume on machine learning and big data analytics (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, International Association of Academicians (IAASSE), and Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and Data Science. With the fascinating development of technologies in several industries, there are numerous opportunities to develop innovative intelligence technologies to solve a wide range of uncertainties in various real-life problems. Researchers and academics have been drawn to building creative AI strategies by combining data science with classic mathematical methodologies. The book brings together leading researchers who wish to continue to advance the field and create a broad knowledge about the most recent research.

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML

This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.

This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of a computer to learn, from data, without being explicitly programmed end-to-end. As ML and AI learn they acquire the ability to carry out cognitive functions, such as perceiving, learning, reasoning and automatically digging deeper to identify important insights or new novel discovery. With the advance in machine learning, in particular its Deep Learning (DL) subset, ML is rapidly spreading across sectors and will continue to do so at an even higher rate with the ever increasing growth of Big Data. Gartner predicts that companies will combine Big Data and Machine Learning to carry out some or most of their service processes by 40% in 2022, up from 5% in 2017.ML is used to accelerate data-driven discovery in research and development. Recently, it has enabled scientists to discover largely unknown diversity of viruses, amounting to thousands of previously unknown viruses. The book refers to previous as well recent research work, with colleagues, where ML was used to capture subtle variation and to discover rare items, such as rare genes which researchers have so long sought for in vain. Such processes to identify genes or medicine can be daunting, as it may take years and can be expensive and the outcome can be uncertain. ML is used today to shorten the time and even help to identify medicine that can be more effective for people with a particular gene, which will help in turn in personalized medicine. ML is a critical ingredient for intelligent applications and provides the opportunity to further accelerate discovery processes as well as enhancing decision making processes. These trends promise that every sector will be data-driven and will be using machine learning in the cloud to incorporate artificial intelligence applications and to ultimately supplement existing analytical and decision making tools. The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language's machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.The book contains 10 chapters, the first chapter highlights ML quests, chapter 2 provides a detailed historical perspective, chapter 3 shows how ML works by introducing conceptual frameworks of ML, chapter 4 lists some of the metrics used to assess the performance of ML types. Chapter 5, 6 and 7 focus on different types of ML including supervised, unsupervised and reinforced learning. Chapter 8 and chapter 9 introduce the ML implementation platforms of R and Spark with their different libraries including Spark MLlib. Chapter 10 provides different walk-through ML examples using both R and Spark ML techniques.