Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition - Hardcover
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Big data approaches and artificial intelligence are rapidly reshaping the way geoscientists analyze, model, and interpret the Earth. This Special Issue focuses on the practical application of big data mining, machine learning, and artificial intelligence in Earth science. It aims to explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methodologies within geoscientific contexts. Comprising 17 papers, the Reprint highlights transformative advances in areas such as mineral prospectivity prediction with metallogenic belt identification, geological data inversion, modeling and deep learning architectures, and combining geological databases with big data mining. It further introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and knowledge discovery. By presenting AI-driven geology as a forward-looking paradigm, the collection demonstrates how artificial intelligence can revolutionize traditional geoscience practices by providing improved accuracy and deeper insight. Through practical examples and case studies, the Reprint illustrates the application of these technologies to complex geoscientific problems. It equips researchers, practitioners, and students with cutting-edge knowledge and tools to harness big data and machine learning, fostering innovation and advancing understanding across the geoscience disciplines.
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Hardcover. Zustand: new. Hardcover. Big data approaches and artificial intelligence are rapidly reshaping the way geoscientists analyze, model, and interpret the Earth. This Special Issue focuses on the practical application of big data mining, machine learning, and artificial intelligence in Earth science. It aims to explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methodologies within geoscientific contexts. Comprising 17 papers, the Reprint highlights transformative advances in areas such as mineral prospectivity prediction with metallogenic belt identification, geological data inversion, modeling and deep learning architectures, and combining geological databases with big data mining. It further introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and knowledge discovery. By presenting AI-driven geology as a forward-looking paradigm, the collection demonstrates how artificial intelligence can revolutionize traditional geoscience practices by providing improved accuracy and deeper insight. Through practical examples and case studies, the Reprint illustrates the application of these technologies to complex geoscientific problems. It equips researchers, practitioners, and students with cutting-edge knowledge and tools to harness big data and machine learning, fostering innovation and advancing understanding across the geoscience disciplines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783725871407
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Hardcover. Zustand: new. Hardcover. Big data approaches and artificial intelligence are rapidly reshaping the way geoscientists analyze, model, and interpret the Earth. This Special Issue focuses on the practical application of big data mining, machine learning, and artificial intelligence in Earth science. It aims to explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methodologies within geoscientific contexts. Comprising 17 papers, the Reprint highlights transformative advances in areas such as mineral prospectivity prediction with metallogenic belt identification, geological data inversion, modeling and deep learning architectures, and combining geological databases with big data mining. It further introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and knowledge discovery. By presenting AI-driven geology as a forward-looking paradigm, the collection demonstrates how artificial intelligence can revolutionize traditional geoscience practices by providing improved accuracy and deeper insight. Through practical examples and case studies, the Reprint illustrates the application of these technologies to complex geoscientific problems. It equips researchers, practitioners, and students with cutting-edge knowledge and tools to harness big data and machine learning, fostering innovation and advancing understanding across the geoscience disciplines. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9783725871407
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Hardcover. Zustand: new. Hardcover. Big data approaches and artificial intelligence are rapidly reshaping the way geoscientists analyze, model, and interpret the Earth. This Special Issue focuses on the practical application of big data mining, machine learning, and artificial intelligence in Earth science. It aims to explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methodologies within geoscientific contexts. Comprising 17 papers, the Reprint highlights transformative advances in areas such as mineral prospectivity prediction with metallogenic belt identification, geological data inversion, modeling and deep learning architectures, and combining geological databases with big data mining. It further introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and knowledge discovery. By presenting AI-driven geology as a forward-looking paradigm, the collection demonstrates how artificial intelligence can revolutionize traditional geoscience practices by providing improved accuracy and deeper insight. Through practical examples and case studies, the Reprint illustrates the application of these technologies to complex geoscientific problems. It equips researchers, practitioners, and students with cutting-edge knowledge and tools to harness big data and machine learning, fostering innovation and advancing understanding across the geoscience disciplines. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9783725871407
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Big data approaches and artificial intelligence are rapidly reshaping the way geoscientists analyze, model, and interpret the Earth. This Special Issue focuses on the practical application of big data mining, machine learning, and artificial intelligence in Earth science. It aims to explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methodologies within geoscientific contexts. Comprising 17 papers, the Reprint highlights transformative advances in areas such as mineral prospectivity prediction with metallogenic belt identification, geological data inversion, modeling and deep learning architectures, and combining geological databases with big data mining. It further introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and knowledge discovery. By presenting AI-driven geology as a forward-looking paradigm, the collection demonstrates how artificial intelligence can revolutionize traditional geoscience practices by providing improved accuracy and deeper insight. Through practical examples and case studies, the Reprint illustrates the application of these technologies to complex geoscientific problems. It equips researchers, practitioners, and students with cutting-edge knowledge and tools to harness big data and machine learning, fostering innovation and advancing understanding across the geoscience disciplines. Bestandsnummer des Verkäufers 9783725871407
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