This book explains Central Asia's different perceptive, especially in the economic, security, and energy fields. The book also clarifies the influence of America, Russia, Europe, and China on Central Asian countries. Central Asia and international players' current association depends on geographic, political, economic, and security factors. Central Asia sits at the center of the Asian continent, a region rich in history and culture. This region benefits from a mixture of national identities that have been developed carefully for many decades. Central Asia consists of five former Soviet nations, as it is currently defined: Uzbekistan, Turkmenistan, Tajikistan, Kyrgyzstan, and Kazakhstan. This book discusses several issues involves in Central Asia.
Data analysis forms the basis of many modes of research ranging from scientific discoveries to governmental findings. With the advent of machine intelligence and neural networks, extracting and modeling, approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other. Predictive Analysis on Large Data for Actionable Knowledge: Emerging Research and Opportunities provides emerging information on extraction and prediction patterns in data mining along with knowledge discovery. While highlighting the current issues in data extraction, readers will learn new methodologies comprising of different algorithms that automate the multidimensional schema that remove the manual processes. This book is a vital resource for researchers, academics, and those seeking new information on data mining techniques and trends.
In Emerging Innovation: Business Transformation in the New Normal, 111 Compact Case Studies, readers are taken on an enlightening journey through the rapidly changing business landscape. This comprehensive collection of compact case studies offers a unique perspective on how companies across various industries have adapted and thrived in the face of unprecedented challenges brought on by the global pandemic. With a sharp focus on innovation, adaptability, and resilience, each case study provides valuable insights into the strategies and tactics employed by businesses to not only survive but also excel in this new normal. The book's 111 compact case studies have been meticulously curated to present a diverse range of sectors, geographic locations, and company sizes, ensuring that readers can relate to and learn from the experiences of others. From leveraging cutting-edge technology and redefining business models, to fostering employee engagement and implementing robust risk management practices, this book is an essential guide intended for business leaders, entrepreneurs, and professionals looking for ways and routes to traverse these uncharted waters and emerge more robust than ever. Whether you are a seasoned executive, an aspiring entrepreneur, or one who is simply curious about the intricacies of business transformation in challenging times, this book is a must-read. Get ready to be inspired, informed, and equipped with the knowledge that will take your organization to soaring heights even amid the uncertainties of the new normal.
This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms. The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book. This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
This book is a comprehensive collection of chemical engineering terms in a single volume. The book is a useful reference material for the people both at the schools and the industry. Our experience of teaching and research over the years has made us to realize a must book of this kind. Better understanding of the terms helps in better understanding the relevant literature and in communicating with more assurance and less use of words. The book is easy to use as the terms are written in an alphabetical order. Where a term deserves more elaboration, a rather detailed description is provided. The book also contains a number of labeled diagrams which are extremely helpful in comprehending some important terms.
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