Statistical Models for Soil Attributes| Book Publisher International

This book explores applied methodologies for assessing soil attributes through diverse multivariate modeling techniques. Specifically, the feasibility of employing different multivariate models to uncover insights from the soil attribute data is examined. The dataset comprises soil attribute measurements collected from various locations and periods, constituting a multivariate dataset. To address this complexity, advanced statistical techniques were applied to reduce dimensionality and extract meaningful patterns. Multivariate statistics aids in understanding the different aims and backgrounds, elucidating how variables are related to each other. The practical implementation of multivariate statistics may involve various univariate and multivariate analyses to comprehend the relationship among variables and their relevance to the studied problems (Johnson and Wicheren, 1996). Various multivariate analysis techniques, including multivariate analysis of variance, Principal Component Analysis, Factor Analysis, Canonical Correlation Analysis, Discriminant Analysis, Cluster Analysis, and multivariate regression analysis, were employed. The multivariate techniques utilized in this research provide valuable tools for understanding complex relationships between soil attributes. By revealing essential latent factors and interdependencies among measured variables, these techniques contribute to a more comprehensive understanding of soil properties and their interactions. This knowledge holds significance for diverse applications such as agriculture, environmental studies, and land management practices. The diverse analyses undertaken provide valuable insights into soil parameter variations, relationships, and their impact on fertility, facilitating targeted soil improvement strategies for enhanced agricultural productivity.

Author(s) Details:

Rajarathinam, A.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, India.

Ramji, M.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, India.

Please see the link here: https://stm.bookpi.org/SMSA/article/view/12984

On Applications of Fuzzy Soft and Intuitionistic Fuzzy Soft Sets in Dimension Reduction and Medical Diagnosis | Chapter 7 | New Ideas Concerning Science and Technology Vol. 8

 In our everyday lives, we are often confronted with a variety of issues relating to data with a high dimensionality. Irrelevant and superfluous data, as well as valuable data, are present in such problems. As a result, dimensionality reduction has a broad range of applications in data processing and analysis. In recent years, the problem of dimensionality reduction in a fuzzy situation has gotten a lot of attention from researchers. They have developed different methods and hypotheses to solve these types of problems. Some of these approaches are based on a probabilistic approach, while others are not. Fuzzy soft set theory is gaining popularity for seeking coherent and logical solutions to a variety of real-world problems involving complexity, imprecision, and vagueness. A theoretical analysis of intuitionistic fuzzy soft has been developed recently. When ambiguity due to vagueness is more complicated, the combination of intuitionistic fuzzy set and intuitionistic fuzzy soft set is more useful from an application standpoint. The definition of fuzzy soft set is described in this chapter as a hybridization of fuzzy set and soft set theory. A new technique for converting a soft set table into a fuzzy soft set table is proposed, which can be used in big data dimension reduction. As a hybridization of fuzzy set and soft set theory, fuzzy soft set and intuitionistic fuzzy soft set are described. Following Sanchez's approach, new methods of applying fuzzy soft and intuitionistic soft sets are also defined in Medical Diagnosis.

Author (s) Details

Prof. D. S. Hooda
Former PVC, Kurukshetra University, Department of Mathematics, G. J. University of Science and Technology, Hisar- 125001, India.

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