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Record Nr. |
UNINA9910484030803321 |
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Autore |
Akram Muhammad |
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Titolo |
Hybrid Soft Computing Models Applied to Graph Theory / / by Muhammad Akram, Fariha Zafar |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XXV, 434 p. 279 illus., 1 illus. in color.) |
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Collana |
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Studies in Fuzziness and Soft Computing, , 1860-0808 ; ; 380 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Operations research |
Management science |
Data mining |
Computational Intelligence |
Operations Research, Management Science |
Data Mining and Knowledge Discovery |
Operations Research and Decision Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Rough Fuzzy Graphs -- Fuzzy Rough Graphs -- Intuitionistic Fuzzy Rough Graphs -- Fuzzy Soft Graphs -- Intuitionistic Fuzzy Soft Graphs -- Soft Rough Fuzzy Graphs -- Bipolar Fuzzy Soft Graphs -- Soft Rough Neutrosophic Influence Graphs. |
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Sommario/riassunto |
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This book describes a set of hybrid fuzzy models showing how to use them to deal with incomplete and/or vague information in different kind of decision-making problems. Based on the authors’ research, it offers a concise introduction to important models, ranging from rough fuzzy digraphs and intuitionistic fuzzy rough models to bipolar fuzzy soft graphs and neutrosophic graphs, explaining how to construct them. For each method, applications to different multi-attribute, multi-criteria decision-making problems, are presented and discussed. The book, which addresses computer scientists, mathematicians, and social scientists, is intended as concise yet complete guide to basic tools for constructing hybrid intelligent models for dealing with some interesting |
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real-world problems. It is also expected to stimulate readers’ creativity thus offering a source of inspiration for future research. |
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