Artificial Intelligence In Spectroscopy
Download Artificial Intelligence In Spectroscopy PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Artificial Intelligence In Spectroscopy book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Artificial Intelligence in Spectroscopy
Author: Joseph Dubrovkin
language: en
Publisher: Cambridge Scholars Publishing
Release Date: 2025-07-17
This textbook summarizes studies and significant materials on Artificial Intelligence in spectroscopy into a fundamental monograph. Its rigorous mathematical basis, in-depth detailed description, and numerous examples of applications in chemistry and physics make it valuable for theorists, practitioners, and students specializing in data processing in spectroscopy, chemometrics, and analytical chemistry. The bibliography part briefly describes hundreds of data analytics applications for solving Artificial Intelligence-based spectroscopic tasks in industrial and research laboratories. This book differs from existing brief reviews and articles on this topic in that it forms, for the first time, the big picture of all kinds of Artificial Intelligence methods in spectroscopy. Also, the book provides quickly reproducible computer calculations, illustrating its significant theoretical statements. As such, it can also serve as a practical guide to lecturers in Artificial Intelligence in spectroscopy, including chemometrics and analytical chemistry.
Spectroscopic Techniques & Artificial Intelligence for Food and Beverage Analysis
Author: Ashutosh Kumar Shukla
language: en
Publisher: Springer Nature
Release Date: 2020-08-20
This informative book discusses the various spectroscopic techniques applied in the analysis of food and beverages. The respective chapters cover techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), FTIR spectroscopy, Electron Spin Resonance (ESR) spectroscopy and Thermoluminescence. The book also presents artificial intelligence applications that can be used to enhance the spectral data analysis experience in food safety and quality analysis. Given its scope, the book will appeal to novice researchers and students in the area of food science. It offers an equally exciting read for food scientists and engineers working in the food industry.
Artificial Intelligence (AI) in Cell and Genetic Engineering
This volume focuses on how different artificial intelligence (AI) techniques like Artificial Neural Network, Support Vector Machine, Random Forest, k-means Clustering, Rough Set Theory, and Convolutional Neural Network models are used in areas of cell and genetic engineering. The chapters this book cover a variety of topics such as molecular modelling in drug discovery, design of precision medicine, protein structure prediction, and analysis using AI. Readers can also learn about AI-based biomolecular spectroscopy, cell culture-system, AI-based drug discovery, and next generation sequencing. The book also discusses the application of AI in analysis of genetic diseases such as finding genetic insights of oral and maxillofacial cancer, early screening and diagnosis of autism, and classification of breast cancer microarray data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Artificial Intelligence (AI) in Cell and Genetic Engineering is a valuable resource for readers in various research communities who want to learn more about the real-life application of artificial intelligence and machine learning in systems biology, biotechnology, bioinformatics, and health-informatics especially in the field of cell and genetic engineering.