Text analysis tools aid in extracting meaning from digital content. As digital text becomes more and more complex, new techniques are needed to understand conceptual structure. Concept Parsing Algorithms (CPA) for Textual Analysis and Discovery: Emerging Research and Opportunities provides an innovative perspective on the application of algorithmic tools to study unstructured digital content. Highlighting pertinent topics such as semantic tools, semiotic systems, and pattern detection, this book is ideally designed for researchers, academics, students, professionals, and practitioners interested in developing a better understanding of digital text analysis.

Research in neuroscience and brain imaging show that exposure of learners to multi-semiotic problems enhance cognitive control of inter-hemispheric attentional processing in the lateral brain and increase higher-order thinking. Multi-semiotic representations of conceptual meaning are found in most knowledge domains where issues of quantity, structure, space, and change play important roles, including applied sciences and social science. Teaching courses in History and Theory of Architecture to young architecture students with pedagogy for conceptual thinking allows them to connect analysis of historic artifact, identify pattern of design ideas extracted from the precedent, and transfer concepts of good design into their creative design process. Pedagogy for Conceptual Thinking and Meaning Equivalence: Emerging Research and Opportunities is a critical scholarly resource that demonstrates an instructional and assessment methodology that enhances higher-order thinking, deepens comprehension of conceptual content, and improves learning outcomes. Based on the rich literature on word meaning and concept formation in linguistics and semiotics, and in developmental and cognitive psychology, it shows how independent studies in these disciplines converge on the necessary clues for constructing a procedure for the demonstration of mastery of knowledge with equivalence-of-meaning across multiple representations. Featuring a wide range of topics such as curriculum design, learning outcomes, and STEM education, this book is essential for curriculum developers, instructional designers, teachers, administrators, education professionals, academicians, policymakers, and researchers.

This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications.

As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.

Biomimetic research is an emerging field that aims to draw inspiration and substances from natural sources and create biological systems in structure, mechanism, and function through robotics. The products have a wide array of application including surgical robots, prosthetics, neurosurgery, and biomedical image analysis. The Handbook of Research on Biomimetics and Biomedical Robotics provides emerging research on robotics, mechatronics, and the application of biomimetic design. While highlighting mechatronical challenges in today’s society, readers will find new opportunities and innovations in design capabilities in intelligent robotics and interdisciplinary biomedical products. This publication is a vital resource for senior and graduate students, researchers, and scientists in engineering seeking current research on best ways to globally expand online higher education.

The disconnect between technology and traditional practices poses a significant challenge. Many healthcare professionals and individuals struggle to navigate the influx of emerging technologies, hindering the full realization of their potential in revolutionizing health literacy and medical practice. The lack of cohesive understanding and integration of technologies like mobile applications, wearable devices, artificial intelligence, and telemedicine impedes the seamless delivery of healthcare services and obstructs individuals from actively managing their health. Transformative Approaches to Patient Literacy and Healthcare Innovation offers a comprehensive solution to bridge the gap between healthcare and technology. Delving into the dynamic fusion of these domains, it unravels the transformative power of technology applications, showcasing how they enhance health literacy and empower individuals to make informed decisions about their well-being. By providing insights into the integration of mobile health apps, electronic health records, extended reality, artificial intelligence, and more, the book equips readers with the knowledge needed to navigate the evolving healthcare landscape with confidence.

Artificial intelligence is a constantly advancing field that requires models in order to accurately create functional systems. The use of natural acumen to create artificial intelligence creates a field of research in which the natural and the artificial meet in a new and innovative way. Critical Developments and Applications of Swarm Intelligence is a critical academic publication that examines developing research, technologies, and function regarding natural and artificial acumen specifically, in regards to self-organized systems. Featuring coverage on a broad range of topics such as evolutionary algorithms, optimization techniques, and computational comparison, this book is geared toward academicians, students, researchers, and engineers seeking relevant and current research on the progressive research based on the implementation of swarm intelligence in self-organized systems.