503 results for machine learning
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Book Chapter Full text
Chapter 5 Control from the ground up
…The contributions discuss hybrid warfare, cyber and influence activities, machine learning and artificial intelligence, the use of armed drones and air power, the implications of the counterinsurgency experiences in Iraq, Afghanistan and Syria, as well as the consequences for law(fare) and decision…
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Mapping the Media-Marketing Ecology
…It charts the rapid take- up of artificial intelligence (AI) and machine learning tools, the role of platforms, and the expansion of actor- types across the creator economy. The authors define and analyse key concepts including polycentric governance, governance- in- practice, and ethics, and map…
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Chapter 7 Best Practices in Single-Cell RNA-seq Data Analysis
…Discusses the translational applications of NGS methods Reviews machine learning heuristics for NGS data interpretation Discusses emerging genomic assay technologies and characterising mechanisms of disease prevalence The book is meant for researchers and industry experts in genomics, computational…
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Chapter 9 Metagenomic Analysis Pipelines for Microbiome Studies
…Discusses the translational applications of NGS methods Reviews machine learning heuristics for NGS data interpretation Discusses emerging genomic assay technologies and characterising mechanisms of disease prevalence The book is meant for researchers and industry experts in genomics, computational…
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Chapter Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review
…technologies such as video/images/audio analysis based on sensors and machine learning to reach out mass numbers. In conclusion, gaps and future applications of this innovative approach within the virtual environment have been identified by the authors
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Disrupting Buildings
…of deep renovation. Technologies such as sensors, big data analytics and machine learning, BIM, digital twinning, simulation, robots, cobots and UAVs, and additive manufacturing are transforming the deep renovation process, improving sustainability performance, and developing new services and…
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Relating to Things
…everything from the care work undertaken by objects, reciprocal human/machine learning, technological mediation as a form of control, and what it takes to reveal things that tend to be hidden and that often (by design) conceal the ways in which they use us. As a whole, Relating to Things is a…
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Next-Generation Sequencing
…Discusses the translational applications of NGS methods Reviews machine learning heuristics for NGS data interpretation Discusses emerging genomic assay technologies and characterising mechanisms of disease prevalence The book is meant for researchers and industry experts in genomics, computational…
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Book Full text
Mapping Crisis
The digital age has thrown questions of representation, participation and humanitarianism back to the fore, as machine learning, algorithms and big data centres take over the process of mapping the subjugated and subaltern. Since the rise of Google Earth in 2005, there has been an explosion in the…
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Geotechnical Modeling and Intelligent Systems
…soil stabilization, finite element simulations, centrifuge modeling, and machine learning applications. Topics include advanced material characterization, predictive modeling of tunnels and slopes, AI-enhanced monitoring systems, and risk mitigation strategies for deep excavations and mining…
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Brain Fingerprint Identification
…of insufficient model generalization. In this book, based on traditional machine learning methods and deep learning methods, the authors will carry out multi-task single-session, single-task multi-session, and multi-task multi-session brain fingerprint identification research respectively for the…
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Information and Knowledge Organisation in Digital Humanities
…heritage collections, the book also includes chapters that focus on machine learning, knowledge graphs, text analysis, text annotations and network analysis. Other topics covered include: semantic technologies, conceptual schemas and data augmentation, digital scholarly editing, metadata creation…
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Stephanie Dinkins
Stephanie Dinkins is renowned for her critical investigations into artificial intelligence and machine learning systems as they intersect race, gender, and our future histories. By using her training in documentary practices as a photo-based artist, she creates inclusive platforms for dialogue and…
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Chapter Beyond the Model
…it blends empirical surveys, design-science modeling, and applied machine-learning experiments, ensuring academic rigor and practical relevance. Designed for researchers, policymakers, leaders, and entrepreneurs seeking to move beyond AI hype and implement people-first strategies, this is an…
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Chapter Quality of Information within Internet of Things Data
…metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information…
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Grounding AI
You are seeing a map — but not of streets or cities. This is a map of two million scientific papers about artificial intelligence, machine learning, and algorithms. Each dot is a paper. Each cluster is a conversation happening in science, right now, about how algorithms are being put to work in the…
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AI and Resilient Organizations
…it blends empirical surveys, design-science modeling, and applied machine-learning experiments, ensuring academic rigor and practical relevance. Designed for researchers, policymakers, leaders, and entrepreneurs seeking to move beyond AI hype and implement people-first strategies, this is an…
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Sparsity Methods for Systems and Control
The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More…
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Computational Conflict Research
…book covers a variety of computational approaches from text mining and machine learning to agent-based modelling and social network analysis. Empirical cases range from migration policy framing in North America and street protests in Iran to violence against civilians in Congo and food riots…
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Forecasting and Assessing Risk of Individual Electricity Peaks
…A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging…