Normalization Of Multidimensional Data For Multi Criteria Decision Making Problems
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Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems
Author: Irik Z. Mukhametzyanov
language: en
Publisher: Springer Nature
Release Date: 2023-07-25
This book presents a systematic review of multidimensional normalization methods and addresses problems frequently encountered when using various methods and ways to eliminate them. The invariant properties of the linear normalization methods presented here can be used to eliminate simple problems and avoid obvious errors when choosing a normalization method. The book introduces valuable, novel techniques for the multistep normalization of multidimensional data. One of these methods involves inverting the normalized values of cost attributes into profit attributes based on the reverse sorting algorithm (ReS algorithm). Another approach presented is the IZ method, which addresses the issue of shift in normalized attribute values. Additionally, a new method for normalizing the decision matrix is proposed, called the MS method, which ensures the equalization of average values and variances of attributes. Featuring numerous illustrative examples throughout, the book helps readers to understand what difficulties can arise in multidimensional normalization, what to expect from such problems, and how to solve them. It is intended for academics and professionals in various areas of data science, computing in mathematics, and statistics, as well as decision-making and operations.
Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems
This book presents a systematic review of multidimensional normalization methods and addresses problems frequently encountered when using various methods and ways to eliminate them. The invariant properties of the linear normalization methods presented here can be used to eliminate simple problems and avoid obvious errors when choosing a normalization method. The book introduces valuable, novel techniques for the multistep normalization of multidimensional data. One of these methods involves inverting the normalized values of cost attributes into profit attributes based on the reverse sorting algorithm (ReS algorithm). Another approach presented is the IZ method, which addresses the issue of shift in normalized attribute values. Additionally, a new method for normalizing the decision matrix is proposed, called the MS method, which ensures the equalization of average values and variances of attributes. Featuring numerous illustrative examples throughout, the book helps readers to understand what difficulties can arise in multidimensional normalization, what to expect from such problems, and how to solve them. It is intended for academics and professionals in various areas of data science, computing in mathematics, and statistics, as well as decision-making and operations.
Application of Fluorescence Spectroscopy in Food Quality and Control
Though fluorescence is a long-established analytical method, it has only recently gained prominence as a valuable tool in food technology. As a particularly sensitive analytical technique, fluorescence spectroscopy allows for the precise identification of various components (functional, compositional and nutritional) of food products, including contaminants and additives. The introduction of new commercially available instruments for fluorescence analysis, coupled with improvements in time resolution and in the instrumentation of both its hardware and software, have given risen to a sharp increase in the technique’s popularity. Presently, it is a rapidly evolving analytical tool used in determining food product quality and authenticity across the industry. While typically discussed alongside other analytical techniques such as mid infrared, near infrared and Raman, the use of fluorescence spectroscopy in food quality control has not been covered in a dedicated, up-to-date volume in several decades. Application of Fluorescence Spectroscopy in Food Quality is a long overdue and unprecedented guide to fluorescence spectroscopy’s modern application in food quality and safety control. This book covers the fundamentals of the technique, before delving into its application to the quality control of various food products, ranging from vegetable and animal foods to cereals, honey and coffee. Multivariate descriptive and predictive methods for qualitative and quantitative analysis, respectively, will also be discussed. Experts from across the globe provide thorough explanations of fluorescence spectroscopy’s uses, while offering comment on the technique’s main advantages for the industry, as well as its limitations. This book will be invaluable to both those looking for an introduction to fluorescence spectroscopy, as well as those who are familiar with the technique and interested learn more about recent advances in the technology and its individual applications.