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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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: 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. Codice articolo 9783725871407
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Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9783725871407
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Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9783725871407
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Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9783725871407
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Da: CitiRetail, Stevenage, Regno Unito
Hardcover. Condizione: 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. Codice articolo 9783725871407
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Da: AussieBookSeller, Truganina, VIC, Australia
Hardcover. Condizione: 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. Codice articolo 9783725871407
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Codice articolo 26406562935
Quantità: 4 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. Condizione: 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. Codice articolo 9783725871407
Quantità: 2 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand. Codice articolo 407639976
Quantità: 4 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND. Codice articolo 18406562941
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