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Add to basketPaperback. Condition: New. The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields.
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Published by Auerbach Publications, 2024
ISBN 10: 1032686723 ISBN 13: 9781032686721
Language: English
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Paperback. Condition: new. Paperback. The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields. This novel book provides comprehensive approach for graduate students to non-linear optimization techniques, fuzzy logic, and rough sets to model real-world situations. The book contains exercises of various level for students interested in combinat Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Add to basketCondition: New. Dr. Ramakant Bhardwaj is currently working as Professor, Department of Mathematics, AMITY University Kolkata (W.B.). He also worked as deputy director research and development about five years in Technocrats TIT group Bhopal. He has published more than 1.
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Add to basketTaschenbuch. Condition: Neu. Neuware - The book provides comprehensive and cognitive approach to building and deploying sophisticated information systems. The book utilizes non-linear optimization techniques, fuzzy logic, and rough sets to model various real-world use cases for the digital era. The hybrid information system modeling handles both qualitative and quantitative data and can effectively handle uncertainty and imprecision in the data. The combination of non-linear optimization mechanisms, fuzzy logic, and rough sets provides a robust foundation for next-generation information systems that can fulfill the demands of adaptive, aware, and adroit software applications for the knowledge era. The book emphasizes the importance of the hybrid approach, which combines the strengths of both mathematical and AI techniques, to achieve a more comprehensive and effective modeling process. Hybrid information system modeling techniques combine different approaches, such as fuzzy logic, rough sets, and neural networks, to create models that can handle the complexity and uncertainty of real-world problems. These techniques provide a powerful tool for modeling and analyzing complex systems, and the applications of hybrid information system modeling demonstrate their potential for solving real-world problems in various fields.
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Published by Elsevier Science & Technology, San Francisco, 2025
ISBN 10: 0443296545 ISBN 13: 9780443296543
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Paperback. Condition: new. Paperback. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032785063 ISBN 13: 9781032785066
Language: English
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