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Advances in Analytics for Learning and Teaching

Computer-Based Diagnostics and
Systematic Analysis of Knowledge

Computer-Based Diagnostics and Systematic Analysis of Knowledge

Critical Reflections and Advancements

Part 1: Conceptual Perspectives Chapter 1: On The Process, Use And Methodological Challenges Of Assessing Knowledge.- Chapter 2: Framing Knowledge As Conceptual Structure.- Chapter 3: Knowledge In The Era Of Artificial Intelligence.- Chapter 4: A Framework For Data-Driven Computer-Based Diagnostics Of Competencies And Capabilities Across Contexts.- Part 2:Applied Perspectives.- Chapter 5: T-MITOCAR. An Epistemological Approach To Assessing Artifacts Of Knowledge.- Chapter 6: Designing Effective Technologies to Support Self-Regulated Strategies Development for Writing.- Chapter 7: Sequential Pattern Mining On Cyber Ranges For A Computer-Based Diagnostic Of Cybersecurity Skills.- Chapter 8: Tracking Competency Development In Highly Interactive Digital Learning Environments.- Chapter 9: Modeling Creativity In Education. Assessing Creativity In Students Scratch Projects: A Study On Human-AI Collaboration For Creativity Assessment.- Chapter 10: The Next Level In Personalized Learning: Adaptation Of Educational Chatbots To Students’ Individual Learning.-&nbsp;Chapter 11: Technology-Enhanced Feedback In K-12 Schools: Utilizing T-MITOCAR For Knowledge Artifact And Feedback.-&nbsp;Chapter 12: Bridging The Gap Between Math Formalism And Natural Language.

Human Machine Integration in Industry 5.0

Digital Twin and Digital Triplet Technologies

Medical Robots and Devices: New Developments and Advances

AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies. The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

AI and Data Science in Medical Research

Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI offers advanced insights into how AI can be used to optimize the design, manufacturing, and clinical application of these systems. It covers a wide range of topics, including the introduction of surgical robotic systems design and the presentation of a fully autonomous surgical robotic system for minimally invasive surgery. Advanced medical imaging techniques integrated with surgical robots are discussed to enhance surgical precision and safety. The book also highlights the application of machine learning and deep learning models in robotic-assisted surgery, with the development of novel algorithms for autonomous surgical robots. Various AI approaches for optimizing surgical robotic systems and the design considerations for manufacturing and assembly are examined. Additionally, the book includes a thorough analysis of the life cycle and economic aspects of surgical robotic systems, as well as risk assessment and evaluation methods. The final chapters address ethics considerations and explore future enhancements in the realm of fully autonomous surgical robotic systems. Overall, this book aims to contribute to the technical understanding, design aspects, and ethical dimensions of surgical robotics, promoting safer and more efficient surgical procedures.

Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI

Eeg Signal Classification Using Machine Learning

A Practical Approach

Applied Machine Learning in Chemical Process Engineering

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools-just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

Deep Learning in Computer Vision

Das Handbuch leistet eine erste Bestandsaufnahme der im Fach Medienwissenschaft genutzten Methoden zur Untersuchung digitaler Kulturen. Es vermittelt einen Überblick über medienkulturwissenschaftliche Forschungsdesigns und den Umgang mit Forschungsdaten. Dabei stellt das Handbuch sowohl methodische Ansätze zur Erhebung und Analyse digitaler Artefakte, Praktiken und Phänomene vor, wie auch spezifisch digitale Methoden der Datenanalyse und -visualisierung. Die Beiträge stellen konkrete Anwendungen vor und reflektieren den jeweiligen methodischen Ansatz in der Fachkultur und den Arbeitsgebieten der Medienwissenschaft.

Handbuch Digitale Medien und Methoden

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing. Furthermore, it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

Computer-Based Diagnostics and Systematic Analysis of Knowledge

Weitere Titel zum Thema