The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.

Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications, experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.

There is an increasing demand to develop intelligent robotics and autonomous systems to deal with dynamically changing and complex, unstructured, and unpredictable environments. Such robots should be able to handle task varieties, environment dynamics and goal variations, and their complexity. This also highlights the need for having intelligent robotics and autonomous systems with capabilities assuring reliable and robust functions resolving real-time complex problems that are associated with many applications across diverse domains. This requires unconventional ways to develop creative and innovative, energy-efficient, and eco- and environmentally friendly solutions that consider new ways of creative thinking while drawing inspiration from nature as a model leading to creating new designs, intelligent systems, intelligent structures/mechanisms, reconfigurability, and more. Global Perspectives on Robotics and Autonomous Systems: Development and Applications describes the evolution of robotics and autonomous systems, their development, their technologies, and their applications. This book discusses the concept of autonomy, requirements, and its role in shaping the behavior of these robots so that they can make their own effective and safe decisions and act on them reliably while assuring real-life requirements. Covering topics such as digital transformation, fused deposition modeling (FDM), and organizational unbundling process, this premier reference source is an essential resource for engineers, computer scientists, industry professionals, manufacturers, smart systems developers, data analysts, students and educators of higher educations, researchers, and academicians.

Emerging technologies have become both crucibles and showrooms for the practical application of artificial intelligence, the internet of things, and cloud computing, and for integrating big data into everyday life. Is the digital world optimized and sustainable using intelligence systems, machine learning, and cyber security methods? This complex concoction of challenges requires new thinking of the synergistic utilization of intelligence systems, machine learning, deep learning and blockchain methods, data-driven decision-making with automation infrastructure, autonomous transportation, and connected buildings. Effective AI, Blockchain, and E-Governance Applications for Knowledge Discovery and Management provides a global perspective on current and future trends concerning the integration of intelligent systems with cybersecurity applications, including recent advances and challenges related to the concerns of security and privacy issues in deep learning with an emphasis on the current state-of-the-art methods, methodologies and implementation, attacks, and countermeasures. The book also discusses the challenges that need to be addressed for implementing DL-based security mechanisms that should have the capability of collecting or distributing data across several applications. Topics covered include skill development and tools for intelligence systems, deep learning, machine learning, blockchain, IoT, cloud computing, data ethics, and infrastructure. It is ideal for independent researchers, research scholars, scientists, libraries, industry experts, academic students, business associations, communication and marketing agencies, entrepreneurs, and all potential audiences with a specific interest in these topics.

Recently, several fog computing applications have been developed like IoT-based healthcare, 5G, blockchains, autonomous driving, and mobile wireless applications. They also address challenges such as data management, scalability, regulations, interoperability, device network human interfaces, security, and privacy. Further study on these applications is required to ensure this technology is utilized appropriately. Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time focuses on fog computing problems and solutions for various applications and covers the new approaches, architecture, and theoretical foundations in the fog paradigm of storage, communication, and computing. The book explores recent trends and challenges that lead to a potential course for the ideas, practices, norms, and strategies related to fog computing. Covering key topics such as data privacy, data analytics, and the internet of things, this reference work is ideal for computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.

Perspectives on Artificial Intelligence in Times of Turbulence: Theoretical Background to Applications offers a comprehensive exploration of the intricate relationship between artificial intelligence (AI) and the ever-changing landscape of our society. The book defines AI as machines capable of performing tasks that were once exclusive to human cognition. However, it emphasizes the current limitations of AI, dispelling the notion of sophisticated cyborgs depicted in popular culture. These machines lack self-awareness, struggle with understanding context—especially in language—and are constrained by historical data and predefined parameters. This distinction sets the stage for examining AI's impact on the job market and the evolving roles of humans and machines. Rather than portraying AI as a threat, this book highlights the symbiotic relationship between humans and machines. It recognizes that while certain jobs may become obsolete, new opportunities will emerge. The unique abilities of human beings—such as relational skills, emotional intelligence, adaptability, and understanding of differences—will continue to be indispensable in a rapidly transforming society. Its perspectives cover a wide range of topics such as business sustainability, change management, cybersecurity, digital economy and transformation, information systems management, management models and tools, and continuous improvement are comprehensively addressed. Additionally, the book delves into healthcare, telemedicine, Health 4.0, privacy and security, knowledge management, learning, and presents real-world case studies. Designed for researchers and professionals seeking to enhance their knowledge and research capabilities, this book offers a consistent theoretical and practical foundation. It serves as a springboard for further studies, supports change management initiatives within organizations, and facilitates knowledge sharing among experts. This book is an essential companion for colleges with master's and Ph.D. degree investigators, and researchers across a wide range of disciplines.

In the present era, technological developments are increasing the efficiency and potential of each stakeholder in a business. Robotic process automation is one of the key areas that can be applied in business organizations and corporate sectors to enhance productivity and show a path to success. Application and Adoption of Robotic Process Automation for Smart Cities provides relevant theoretical frameworks and various developments in the area of robotic process automation. Covering topics such as banking and financial services, public engagement, and smart cities, this premier reference source is a valuable resource for business leaders, IT managers, government officials, engineers, students and educators of higher education, researchers, and academicians.

Image processing integrates and extracts data from photos for a variety of uses. Applications for image processing are useful in many different disciplines. A few examples include remote sensing, space applications, industrial applications, medical imaging, and military applications. Imaging systems come in many different varieties, including those used for chemical, optical, thermal, medicinal, and molecular imaging. To extract the accurate picture values, scanning methods and statistical analysis must be used for image analysis. Thrust Technologies’ Effect on Image Processing provides insights into image processing and the technologies that can be used to enhance additional information within an image. The book is also a useful resource for researchers to grow their interest and understanding in the burgeoning fields of image processing. Covering key topics such as image augmentation, artificial intelligence, and cloud computing, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

The lack of comprehensive, innovative insights into the intricate world of pregnancy complication prediction is a pressing concern, as these complications can severely impact the health and wellbeing of pregnant patients. As the complexities of maternal healthcare continue to evolve, scholars grapple with the challenge of staying at the forefront of research and innovation in this critical field. The unpredictability of pregnancy complications poses significant risks to positive patient outcomes, demanding novel approaches to diagnosis and prevention. The academic community seeks a solution that can bridge the gap between traditional research and the transformative potential of technological advancements in healthcare. Technological Tools for Predicting Pregnancy Complications not only identify the problem but offer an authoritative solution. It serves as a beacon of knowledge for academic scholars, providing a holistic exploration of how Artificial Intelligence (AI) and Machine Learning (ML) technologies can revolutionize maternal healthcare. With a laser focus on predictive models, comprehensive health data analysis, and innovative algorithmic approaches, this book equips scholars with the tools they need to navigate the ever-evolving landscape of pregnancy complications. Academic scholars will find a treasure trove of insights, spanning from the fundamentals of AI and ML in healthcare to the application of IoT devices and wearable sensors for expectant mothers.

This book presents proceedings of the 14th Days of Bosnian-Herzegovinian American Academy of Arts and Sciences held in Tuzla, BIH, June 1–4, 2023. Delve into the intellectual tapestry that emerged from this event, as we unveil our highly anticipated Conference Proceedings Book. This groundbreaking publication captures the essence of seven captivating technical sessions spanning from Civil Engineering through Power Electronics all the way to Data Sciences and Artificial Intelligence, each exploring a distinct realm of innovation and discovery. Uniting diverse disciplines, this publication catalyzes interdisciplinary collaboration, forging connections that transcend traditional boundaries. Within these pages, readers find a compendium of knowledge, insights, and research findings from leading researchers in their respective fields. The editors would like to extend special gratitude to the chairs of all symposia for their dedicated work in the production of this volume.