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Gesamtkatalog

Catalogue complet

Data Driven Applications for Emerging
Technologies

Data Driven Applications for Emerging Technologies

Handbooks in Communication and Media

"The Handbook of Artificial Intelligence and Journalism will be the first of its kind in seeking to unveil the algorithmic transformation of journalism and define this new research field by highlighting the interplay between journalism and technological advancements. The rise of AI and its pervasive reach has ushered in a new era of practice and research in the field of communication, particularly in the realm of journalism, where it is reshaping not only the organizational structure of the news but the way it is produced, distributed, and consumed. This Handbook delves into the AI's potential for the Journalism field, as well as possible outcomes of the integration of intelligent systems into news journalism. Incorporating studies from around the world, including the global south, chapters will provide an inclusive and holistic understanding of the algorithmic transformation in journalism across different cultural, economic, and societal contexts. Topics covered include AI and mis/disinformation, ethical implications, emerging business models in algorithm-driven journalism, and AI governance. For students and scholars of communication and media studies or journalism, this handbook offers a theoretical framework, key concepts, and new approaches to algorithm-driven journalism, aiding them in the comprehension of the potentials and impact of the use of intelligent technologies in journalism"--

The Handbook of Artificial Intelligence and Journalism

Powered by Hyperscale Computing and Next Generation Networks

AI-Driven Smart Healthcare

Energy Efficient Internet of Things-Based Wireless Sensor Network

Deep Learning in Computer Vision

Machine Learning For Dummies

The Intersection of CFIUS, Team Telecom, AI Controls, and Other Regulations

Navigating Technology and National Security

Artificial Intelligence and Machine Learning in Data Science and Analytics

Advanced Biotechnology

Enables researchers and engineers to gain insights into the capabilities of machine learning approaches to power applications in their fields Machine Learning and Big Data-enabled Biotechnology discusses how machine learning and big data can be used in biotechnology for a wide breadth of topics, providing tools essential to support efforts in process control, reactor performance evaluation, and research target identification. Topics explored in Machine Learning and Big Data-enabled Biotechnology include: <ul> <li>Deep learning approaches for synthetic biology part design and automated approaches for GSM development from DNA sequences</li> <li>De novo protein structure and design tools, pathway discovery and retrobiosynthesis, enzyme functional classifications, and proteomics machine learning approaches</li> <li>Metabolomics big data approaches, metabolic production, strain engineering, flux design, and use of generative AI and natural language processing for cell models</li> <li>Automated function and learning in biofoundries and strain designs</li> <li>Machine learning predictions of phenotype and bioreactor performance</li> </ul> Machine Learning and Big Data-enabled Biotechnology earns a well-deserved spot on the bookshelves of reaction, process, catalytic, and environmental engineers seeking to explore the vast opportunities presented by rapidly developing technologies.

Machine Learning and Big Data-enabled Biotechnology

Advances in Industrial and Systems Engineering

Advances in Human-AI Collaboration

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Methods, Applications, and Implications

Adaptive AI in Sensor Informatics

A Cultural History of Computer Graphics presents a fundamentally new approach to analyzing digital images aesthetically through the example of 3D computer graphics (CG). While numerous methods for creating digital imagery have long existed, the advent of AI-generated content is causing a rise in debates and conflict. It is becoming increasingly difficult to differentiate digital photographs, CG, and AI images, and yet, because these types of images carry different cultural or even political implications, it is becoming increasingly important to do so. In response to the need of new methods to culturally decode digital imagery, this book starts from the production process and describes computer graphics as an independent method of expression, containing a specific ideological concept of realism. Through this study, it becomes clear that a particular understanding of the world is inscribed in computer graphics software development and consequently the image creation process, and it becomes necessary to perceive production aesthetically, hence there is a focus on production aesthetics. In its own unique way, every digital imaging method embodies its own sense of the world. This book will be of great interest to researchers of computer graphics, 3D image generation, and the cultural history of computer-generated imagery.

A Cultural History of Computer Graphics

Data Driven Applications for Emerging Technologies

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