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Autor(en): 
  • Ramchandra Mangrulkar
  • Pallavi Vijay Chavan
  • Nisha Balani
  • Sangita Santosh Chaudhari
  • Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juli 2025  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / COMPUTERS / Artificial Intelligence / Expert Systems / COMPUTERS / Artificial Intelligence / General / COMPUTERS / Data Science / Neural Networks / Expert systems / knowledge-based systems / machine learning / Neural networks & fuzzy systems / Neural networks and fuzzy systems
    ISBN:  9780443337284 
    EAN-Code: 
    9780443337284 
    Verlag:  Elsevier 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 191 mm / D  
    Gewicht:  450 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Decision-making is a fundamental process that influences outcomes across a wide range of domains, including business, healthcare, scientific research, and automation. With the increasing availability of data and the growing computational power of modern systems, decision-making models have become more sophisticated and capable of providing highly accurate and efficient solutions. The ability to develop, analyze, and implement these models has become crucial for professionals and researchers working in fields that rely on data-driven decision-making.
    This book explores the evolution and significance of decision systems, covering both foundational theories and advanced methodologies. It introduces readers to the essential principles of decision-making models, illustrating their applications through practical case studies and real-world scenarios. The discussion begins with a focus on traditional decision-making techniques and gradually progresses to more advanced topics, including machine learning-based approaches, the integration of artificial intelligence, and the role of fuzzy logic in decision support systems. Furthermore, ethical considerations in decision-making and strategies for mitigating bias are examined, ensuring that models remain fair and transparent.
    Throughout this book, each chapter builds on the previous one, providing a structured and comprehensive learning experience. By the time readers complete this book, they will have gained an in-depth understanding of decision-making frameworks, their applications, and the future directions of research in this dynamic field. Whether one is a student, a researcher, or an industry professional, this book serves as a valuable guide to mastering the complexities of decision systems and applying them effectively in various domains.

      



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