Applications of Machine Learning and Deep Learning on Biological Data

Herausgeber: Masoodi, Faheem; Bukhari, Syed; Quasim, Mohammad

• Gebundenes Buch

Produktbeschreibung

This book provides readers a comprehensive understanding of the application of machine Learning and deep Learning in proteomics, genomics, microarrays, text mining and related fields. The key objective is to provide machine learning applications to biological science problems, focusing on problems related to bioinformatics.

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Produktdetails

• Verlag: CRC Press
• Seitenzahl: 200
• Erscheinungstermin: 13. März 2023
• Englisch
• Abmessung: 234mm x 156mm x 13mm
• Gewicht: 476g
• ISBN-13: 9781032214375
• ISBN-10: 1032214376
• Artikelnr.: 66268446

Autorenporträt

Dr. Faheem Syeed Masoodi is Assistant Professor in the Department of Computer Science, University of Kashmir, India. Dr. Mohammad Tabrez Quasim is Assistant Professor at University of Bisha, Saudi Arabia. Dr. Syed Nisar Hussain Bukhari is a Scientist-C at the National Institute of Electronics and Information Technology (NIELIT) J&K, Srinagar, India. Prof. Dr. Sarvottam Dixit holds the post of Advisor to The Chairperson, Mewar University, Chittorgarh, India. Dr. Shadab Alam is currently Assistant Professor in the Department of Computer Science, Jazan University, Jazan, Saudi Arabia.

Inhaltsangabe

1. Deep Learning Approaches, Algorithms, and Applications in
Bioinformatics. 2. Role of Artificial Intelligence and Machine Learning in
Schizophrenia - A Survey. 3. Understanding Financial Impact of Machine
Learning and Deep Learning in Healthcare: An Analysis 4. Face Mask
Detection Alert System for COVID Prevention Using Deep Learning. 5. An
XGBoost-Based Classification Method to Classify Breast Cancer. 6.
Prediction of Erythemato-Squamous Diseases Using Machine Learning. 7.
Grouping of Mushroom 5.8s rRNA Sequences by Implementing Hierarchical
Clustering Algorithm. 8. Applications of Machine Learning and Deep Learning
in Genomics and Proteomics. 9. Artificial Intelligence: For Biological
Data. 10. Application of ML and DL on Biological Data. 11. Deep Learning
for Bioinformatics.