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Academics and researchers currently grapple with a pressing issue; the demand for precise and insightful geographical information has surged across various fields, encompassing urban planning, environmental monitoring, agriculture, and disaster management. This surge has revealed a substantial knowledge gap, underscoring the need for effective applications that can bridge the gap between cutting-edge technologies and practical usage. Geospatial Application Development Using Python Programming emerges as the definitive solution to this challenge. This comprehensive book equips academics, researchers, and professionals with the essential tools and insights required to leverage the capabilities of Python programming in the realm of spatial analysis. It goes beyond merely connecting these two realms; it actively fosters their collaboration. By advancing knowledge in spatial sciences and highlighting Python's pivotal role in data analysis and application development, this book plays a crucial part in addressing the challenge of effectively harnessing geographical data.

In today's landscape of leadership and management, a pressing issue confronts professionals at all levels. Traditional leadership paradigms, including emotional intelligence, are proving insufficient in meeting the demands of the dynamic professional environment. Leaders, both aspiring and experienced, grapple with the challenge of establishing deeper, more meaningful connections in both personal and professional spheres. What exacerbates this issue is the lack of awareness regarding the untapped potential residing at the intersection of neuroscience, cognitive psychology, and social sciences. Building Organizational Resilience With Neuroleadership serves as a beacon of knowledge and a solution to this enduring challenge. This thought-provoking book embarks on an illuminating journey through the emerging field of neuroleadership, seamlessly integrating insights from neuroscience, cognitive psychology, and leadership studies. It offers a comprehensive solution, meticulously crafted for academic scholars, researchers, management students, and seasoned professionals who aspire to transcend their leadership abilities. This groundbreaking book propels emotional intelligence to new heights, empowering leaders to forge more profound connections within their teams and organizations. By unraveling the neural underpinnings of effective leadership, it equips readers with the tools to recognize and manage emotions, thereby fostering authenticity in their interactions. It also reveals the profound influence of neurons, encouraging both budding and seasoned leaders to embrace the extraordinary role of brain functions in shaping magnetic organizational cultures and teams. By bringing together the collaborative efforts of pioneering researchers, social scientists, and behavioral experts, a wholistic solution is prepared within the pages of this text.

"This unique edited book provides a competitive edge in the financial sector by offering a platform for research. It explores the applications of AI-based algorithms for evolving market trends, with a focus on algorithm-based financial solutions for the stock market, portfolio optimization, and financial fraud detection"--

Higher education institutions must urgently overcome the difficulty of negotiating the complex web of international accreditation standards in a rapidly globalized world. Academic researchers, teachers, and administrators struggle with the intricacy of making sure their programs adhere to strict standards while still attempting to maintain their competitiveness on a global level. These organizations run the risk of stagnation and missing out on possibilities for advancement and recognition if there is no clear path forward. Evaluating Global Accreditation Standards for Higher Education , is a comprehensive guide for overcoming the modern accreditation conundrum. This invaluable resource equips academic scholars and professionals with the tools and knowledge they need to successfully navigate the accreditation process at both local and international levels. From program criteria and curriculum development to faculty professional development and alumni engagement, this book offers a roadmap to excellence. By following the expert guidance within these pages, institutions can unlock their potential, achieve accreditation, and gain the recognition they deserve.

Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches.

Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making.

Generative Artificial Intelligence (AI), an ever-evolving technology, holds immense promise across various industries, from healthcare to content generation. However, its rapid advancement has also given rise to profound ethical concerns. Illicit black-market industries exploit generative AI for counterfeit imagery, and in educational settings, biases and misinformation perpetuate. These issues underscore the need to grapple with the risks accompanying generative AI integration. Exploring the Ethical Implications of Generative AI emerges as a wellspring of insight for discerning academic scholars. It sets the stage by acknowledging generative AI's multifaceted potential and its capacity to reshape industries. The book addresses these complex ethical concerns, offering a comprehensive analysis and providing a roadmap for responsible AI development and usage. Its intended audience spans business leaders, policymakers, scholars, and individuals passionate about the ethical dimensions of AI.

A chasm grows between the currently established knowledge and the rapidly evolving landscape of healthcare. As the field of biomedical research hurtles forward with groundbreaking discoveries and transformative technologies, academic scholars find themselves grappling with a significant dilemma. There exists a disconnect between traditional educational resources and the need to keep pace with the latest innovations that are reshaping medicine, diagnosis, and treatment. This widening gap inhibits scholars from adequately preparing their students and hampers their ability to engage in relevant, cutting-edge research, ultimately impeding the advancement of healthcare as a whole. Biomedical Research Developments for Improved Healthcare serves as the ultimate solution to this academic challenge. This book offers a compelling bridge between the realm of academic theory and the dynamic world of practical, real-world biomedical research. Its primary objective is to equip scholars with the knowledge, insights, and materials needed to inspire the next generation of healthcare professionals. By presenting a comprehensive overview of the most recent and groundbreaking advancements in biomedical research, the book enables scholars to transcend the limitations of traditional academia and empower their students with up-to-date, practical knowledge.

The longstanding practice of keeping academic disciplines separate has been a barrier to effectively addressing the complex challenges in our world. The boundaries separating fields like healthcare, social sciences, and technology have obscured the potential for interdisciplinary collaboration, preventing us from unlocking innovative solutions to the most pressing issues of our time. As a result, the critical problems we face, from healthcare inequities to technological advancements with ethical dilemmas, have remained largely unresolved. This fragmented approach to academic inquiry has left a void in our quest to tackle these challenges effectively. The solution is found within the pages of Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology . This groundbreaking compendium illuminates the transformative potential of interdisciplinary collaboration, offering direction and support in the form of knowledge for scholars, researchers, practitioners, and students committed to solving real-world problems. By harnessing the collective wisdom of diverse disciplines, the book demonstrates how convergence across healthcare, social sciences, organizational behavior, and technology can lead to groundbreaking insights and solutions. It showcases success stories and innovative strategies that drive positive change within our societies, offering a roadmap towards a brighter, more interconnected future.

Smart watches, autonomous vehicles, and talking robots are now an everyday part of life for many regions of the world. The digital revolution has now permeated nearly every facet of our existence. This surge in technological advancement has ushered in what economists term an "innovation economy." In this era, the synergy between technology and business intelligence propels groundbreaking innovations, fostering entrepreneurial ventures across various sectors. These ventures encompass an array of industries, including agriculture, fast-moving consumer goods, hospitality, cultural and indigenous products, to name just a few. While these entrepreneurial endeavors bring forth creativity and contribute to circular economies within communities, not all manage to navigate the challenging environment successfully. This raises a pressing concern - how can entrepreneurs harness the power of business intelligence and innovation to achieve sustainability and a competitive edge in today's complex business landscape? Applying Business Intelligence and Innovation to Entrepreneurship brings a definitive solution to academicians, researchers, and students who seek a comprehensive understanding of the dynamic interplay between entrepreneurship, business intelligence, and innovation. By dissecting various types of organizations, from small and medium-sized enterprises (SMEs) to technology-based startups like Fintech, digital marketing, and community-driven initiatives, this book paints a vivid picture of the entrepreneurial ecosystem. It delves deep into the symbiotic relationship between local wisdom and innovation, shedding light on how entrepreneurs can seize opportunities presented by disruptive and unpredictable phenomena.

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists' ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics . This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.

"This book highlights the revolutionary potential of combining AI with digital twin technology. In today's data-driven and complex business world, organizations may achieve operational excellence, innovation, and competitive advantage by using the power of these technologies"--

In recent years, the advent of biosensors and bioelectronics has facilitated the swift and precise detection of numerous biomolecules or pathogens in animal samples, aquatic habitats, and plants. These technologies have emerged as potent assets for the agriculture and aquaculture industries. Many experts have raised the question; how might the integration of biosensors into portable devices or automated monitoring systems enable on-site and real-time detection of diverse biomolecules or pathogens? Agriculture and Aquaculture Applications of Biosensors and Bioelectronics presents the recent developments in biological recognition elements, transducer materials, and signal processing techniques for biosensors and bioelectronics used in agriculture and aquaculture applications. Sophisticated biosensors and bioelectronics operate through the immobilization of biological recognition components-such as enzymes or antibodies-on a transducer surface. This immobilization process allows for the targeted recognition and binding of biomolecules or pathogens. The resulting electrical, optical, or chemical changes triggered by the recognition event are quantifiable through various methods and are often enhanced by applications utilizing artificial intelligence (AI). The necessity for high sensitivity and selectivity, the optimization of biocompatibility and stability, and the integration of biosensors with AI-aided solutions are just some of the challenges and opportunities in developing biosensors for high-tech agriculture and aquaculture. This book targets a mixed audience of biotechnology engineers, biosensors scientists, bioelectronics researchers, high-tech agriculture analysts, Ph.D. scholars, researchers, academics, professionals, engineers, and students.

The chasm between the physical capabilities of Intelligent Robotics and Autonomous Systems (IRAS) and their cognitive potential presents a formidable challenge. While these machines exhibit astonishing strength, precision, and speed, their intelligence and adaptability lag far behind. This inherent limitation obstructs the realization of autonomous systems that could reshape industries, from self-driving vehicles to industrial automation. The solution to this dilemma is unveiled within the pages of Modeling, Simulation, and Control of AI Robotics and Autonomous Systems . Find within the pages of this book answers for the cognitive deficit within IRAS. While these systems boast remarkable physical capabilities, their potential for intelligent decision-making and adaptation remains stunted, thereby bringing innovation to a halt. Solving this issue would mean the re-acceleration of multiple industries that could utilize automation to prevent humans from needing to do work that is dangerous, and could revolutionize transportation, and more.

The Covid-19 pandemic has accelerated the adoption of Smart Working (SW) and Distance Learning (DL), leading to concerns about their overall effectiveness despite initial efficiency. Limited research on the positive and negative effects of these practices has left decision-makers ill-equipped to navigate their complexities. To address this knowledge gap, Innovation Capabilities and Entrepreneurial Opportunities of Smart Working , edited by Beatrice Orlando from the University of Ferrara, Italy, provides a comprehensive resource to explore the multifaceted effects of SW and DL. The book covers topics such as efficiency, effectiveness, knowledge management, individual impact, and broader social, economic, and business implications, equipping both academic scholars and practitioners with the necessary knowledge to understand and leverage SW and DL effectively. It empowers decision-makers to make informed choices, develop innovative strategies, and maximize benefits while mitigating potential negative effects. By offering a comprehensive examination of the present and future impact of SW and DL, Innovation Capabilities and Entrepreneurial Opportunities of Smart Working emerges as an indispensable solution. The book enables scholars to contribute groundbreaking research and assists practitioners in making informed decisions that will shape the future of work and learning in the digital age. With its fresh insights and evidence-based research, it equips readers to navigate the complexities of SW and DL, driving meaningful change and fostering innovation in organizations and educational institutions.

During the COVID-19 pandemic, many coronavirus-positive patients faced myriad health issues during treatment and post treatment. Many people who lost their lives due to COVID-19 infection were also prone to other, previously established health issues including heart diseases, diabetes, hypertension, and more. It is essential to explore these different health issues and complications that patients face during and after COVID-19 treatment with respect to a global treatment group and the methods employed by healthcare professionals to help them overcome these complications with medicine and other treatments and precautions. Clinical Practice and Post-Infection Care for COVID-19 Patients creates a platform for healthcare professionals to share their experiences and treatments while handling COVID-19 patients during the pandemic and post-pandemic periods. The case studies and issues with heart patients and related diseases during the pandemic mentioned in this book can help medical care providers guide patients to take appropriate precautions well in advance of any adverse conditions, preserving their health and ability to recover. This book is ideally designed for medical practitioners, medical teachers, medical students, researchers, hospital administrators, nurses, and heart patients.

The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by artificial intelligence (AI) and machine learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the integration of these advanced technologies becomes paramount. While AI and ML have individually showcased their capabilities in developing robust intelligent machine systems and services, their fusion holds the key to propelling intelligent machines to a new realm of transformation. By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths.

As societies strive for progress, the need to ensure equal access to education for all, irrespective of socioeconomic, ethnic, or gender backgrounds, emerges as a pivotal factor. In the thought-provoking book, Socio-Economic Implications of Global Educational Inequalities , the complex interplay between education and its profound impact on human capital, economic growth, and development is meticulously explored. The book delves into the multifaceted dimensions of education as a critical determinant of income and gender disparities, poverty levels, and financial sector advancement. Drawing on the expertise of professional researchers, this comprehensive work presents a panoramic view of the socio-economic implications of educational inequalities on a global scale. By examining the profound link between education and various societal aspects, the book opens avenues for insightful discussions and strategic policymaking. In addition to catering to scholars and researchers, this book addresses a broader audience, including individuals, managers, and governmental employees who recognize education's pivotal role in shaping economic, financial, and social landscapes.

Mathematical writing is essential for students' math learning, but it's often underutilized due to unclear guidelines. Mathematical writing is a mode of communication that provides teachers access to their students' thinking and, importantly, offers students an opportunity to deepen their mathematical understanding, engage in mathematical reasoning, and learn a fundamental way to communicate mathematically. Notably, one needs to be able to judiciously combine mathematical symbols, representations, and text. However, more research is needed to exemplify the qualities of mathematical writing, develop implementation methods, and support teachers. Illuminating and Advancing the Path for Mathematical Writing Research , is a necessary comprehensive resource designed to enhance mathematical writing and promote equitable learning. This research book provides a comprehensive understanding of the current state of mathematical writing and illuminates various perspectives on moving the teaching and learning of k-12 mathematical writing forward. Mathematical writing is an important yet underutilized component of mathematical discourse, and this book offers further insight into understanding what it means to write mathematically for mathematics educators and researchers. It informs with research-based implementation strategies and creates purposeful professional learning opportunities. Ultimately, k-12 students will benefit from a more informed field because they will have access to a vital mode of mathematical reasoning and communication.

Metaverse Applications for Intelligent Healthcare explores the exciting intersection of artificial intelligence (AI) and the metaverse in the field of healthcare. The use of AI technology in healthcare has already shown great potential in assisting with diagnosis, treatment, and patient care. The metaverse, with its immersive virtual environments, has the potential to revolutionize healthcare by making it more accessible, efficient, and personalized. This book introduces various applications of the metaverse in healthcare, including virtual consultations, remote patient monitoring, and virtual rehabilitation. The book discusses how the metaverse can be used to provide immersive experiences that empower patients and providers, while also offering unique learning opportunities. The book is ideal for researchers, practitioners, healthcare professionals, scholars, and students who are interested in exploring the cutting-edge technology of AI and the metaverse in healthcare. It offers insights into the future of healthcare, and how these technologies can be used to provide better care to patients. By combining the latest research in AI and the metaverse, this book provides a comprehensive overview of the potential applications of these technologies in healthcare.

Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.

Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.

Never has the world been so heavily marked by the escalating challenges of climate change, dwindling natural resources, and escalating environmental crises. Finding viable solutions has become increasingly imperative as a result of these challenges. The daunting issue at hand is the dire need for financing sustainable initiatives that can effectively combat these global challenges. This financial gap often poses a significant roadblock, hindering progress towards a more environmentally conscious and sustainable future. Sustainable Investments in Green Finance serves as a resource to examine this issue and provide solutions. This meticulously researched book offers real-world case studies that not only highlight the gravity of these challenges but also present tangible solutions. Readers will explore a myriad of sustainable finance mechanisms, collaborative stakeholder efforts, and visionary project objectives that have successfully navigated these obstacles. The book also delves into topics such as the renewable energy revolution, sustainable infrastructure transformations, green bonds, climate finance, nature-based solutions, circular economy innovations, and socially responsible investing.