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Record Nr. |
UNINA9910861098403321 |
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Autore |
Das Sharma Kaushik |
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Titolo |
Intelligent Computing in Carcinogenic Disease Detection / / by Kaushik Das Sharma, Subhajit Kar, Madhubanti Maitra |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (189 pages) |
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Collana |
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Computational Intelligence Methods and Applications, , 2510-1773 |
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Altri autori (Persone) |
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KarSubhajit |
MaitraMadhubanti |
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Disciplina |
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Soggetti |
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Artificial intelligence - Data processing |
Computer science |
Engineering - Data processing |
Data Science |
Theory and Algorithms for Application Domains |
Data Engineering |
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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 contenuto |
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Chapter 1. Introduction -- Chapter 2. Biological Background of Benchmark Carcinogenic Data Sets -- Chapter 3. Intelligent Computing Approaches for Carcinogenic Disease Detection: A Review -- Chapter 4. Classical Approaches in Gene Evaluation for Carcinogenic Disease Detection -- Chapter 5. Intelligent Computing Approach in Gene Evaluation for Carcinogenic Disease Detection -- Chapter 6. Intelligent Computing Approach for Leukocyte Identification -- Chapter 7. Intelligent Computing Approach for Lung Nodule Detection -- Chapter 8. Conclusion -- Index. |
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Sommario/riassunto |
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This book draws on a range of intelligent computing methodologies to effectively detect and classify various carcinogenic diseases. These methodologies, which have been developed on a sound foundation of gene-level, cell-level and tissue-level carcinogenic datasets, are discussed in Chapters 1 and 2. Chapters 3, 4 and 5 elaborate on several intelligent gene selection methodologies such as filter methodologies and wrapper methodologies. In addition, various gene selection philosophies for identifying relevant carcinogenic genes are |
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described in detail. In turn, Chapters 6 and 7 tackle the issues of using cell-level and tissue-level datasets to effectively detect carcinogenic diseases. The performance of different intelligent feature selection techniques is evaluated on cell-level and tissue-level datasets to validate their effectiveness in the context of carcinogenic disease detection. In closing, the book presents illustrative case studies that demonstrate the value of intelligent computing strategies. |
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