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Intelligent Computing in Carcinogenic Disease Detection de Kaushik Das Sharma

Intelligent Computing in Carcinogenic Disease Detection: Computational Intelligence Methods and Applications

Autor Kaushik Das Sharma, Subhajit Kar, Madhubanti Maitra
en Limba Engleză Hardback – 17 mai 2024
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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Specificații

ISBN-13: 9789819724239
ISBN-10: 9819724236
Pagini: 196
Ilustrații: XIV, 180 p. 88 illus., 35 illus. in color.
Dimensiuni: 160 x 241 x 17 mm
Greutate: 0.41 kg
Ediția:2024
Editura: Springer
Colecția Computational Intelligence Methods and Applications
Seria Computational Intelligence Methods and Applications

Locul publicării:Singapore, Singapore

Cuprins

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.

Notă biografică

Dr. Kaushik Das Sharma received the B.Tech. and M.Tech. degrees in Electrical Engineering from Department of Applied Physics, University of Calcutta, Kolkata, India, in 2001, 2004 respectively and Ph.D. degree from Jadavpur University, Kolkata, India, in 2012. He is currently serving as an Associate Professor in Electrical Engineering Section of Department of Applied Physics, University of Calcutta, Kolkata, India. He is a recipient of the Kanodia Research Scholarship in 2002 and University Gold Medal in 2004 from University of Calcutta. Dr. Das Sharma's key research interests include fuzzy control, stochastic optimization, machine learning, robotics and systems biology. He has authored/coauthored about 45 technical articles, including 20 international journal papers. Dr. Das Sharma is a Senior Member of the IEEE (USA), Member of the IET (UK) and a Life Member of Indian Science Congress Association. He has served/is serving in important positions in several International Conference Committees all over the world. Presently he also serves as Secretary of IEEE Joint CSS-IMS Kolkata Chapter, an Executive Committee Members of IEEE Kolkata Section and IET (UK) Kolkata Local Network. Amitava Chatterjee did his BEE, MEE and PhD from Electrical Engineering Department of Jadavpur University, Kolkata, in 1991, 1994 and 2002, respectively. He is currently serving as a Professor in Electrical Engineering Department of Jadavpur University, Kolkata, India. He is a recipient of the Japanese Government (Monbukagakusho) Scholarship in 2003 and a recipient of the Japan Society for the Promotion of Science (JSPS) Post-Doctoral Fellowship in 2004. In 2004, and in 2009 he visited University of Paris XII, Val de Marne, France, and in 2017 he visited University of Paris-Est, France as an Invited Teacher. Dr. Chatterjee's key research interests includenonlinear control, intelligent instrumentation, signal processing, computer vision, robotics, image processing and pattern recognition. Dr. Chatterjee has co-authored a book and co-edited two books all published by Springer-Verlag, Germany. He presently serves as an Editor of IEEE Transactions on Vehicular Technology and Engineering Applications of Artificial Intelligence Journal (Elsevier) and as an Associate Editor of IEEE Transactions on Instrumentation and Measurement, and IEEE Sensors Journal. He has authored/coauthored more than 145 technical articles, including 90 international journal papers. Dr. Chatterjee is a member of the Technical Committee on "Imaging Measurements and Systems" of IEEE Instrumentation & Measurement Society, USA. He is a Fellow of the IETE (India), a Fellow of the Institution of Engineers (India), and a Senior Member of the IEEE (USA). He has served/is serving in important positions in several International Conference Committees all over the world, including several flagship IEEE Conferences. Anjan Rakshit received the ME and PhD degrees in Electrical Engineering from Jadavpur University, Kolkata, India, in 1977 and 1987, respectively. He is a retired Professor from the Department of Electrical Engineering, Jadavpur University. His research interests include digital signal and image processing, Internet-based instrumentation, smart instrumentation, intelligent control, and design of real-time systems. He has published about 75 technical papers in his areas of research interest.

Textul de pe ultima copertă

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.

Caracteristici

Comprehensively presents intelligent methodologies for detecting and classifying carcinogenic diseases Blends theory and practice, presenting state-of-the-art methodologies Present case studies involving carcinogenic datasets at the gene level, cell level and tissue level