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Intelligent Computing in Carcinogenic Disease Detection [electronic resource] / by Kaushik Das Sharma, Subhajit Kar, Madhubanti Maitra.

By: Das Sharma, Kaushik [author.].
Contributor(s): Kar, Subhajit [author.] | Maitra, Madhubanti [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Computational Intelligence Methods and Applications: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024.Description: XIV, 180 p. 88 illus., 35 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789819724246.Subject(s): Artificial intelligence -- Data processing | Computer science | Engineering -- Data processing | Data Science | Theory and Algorithms for Application Domains | Data EngineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 005.7 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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 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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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.

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 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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