The book showcases cutting-edge concepts and methods, and presents the principle of imprecise-information processing. It also proposes a new theory and technology for imprecise-information processing that differs from fuzzy technology, thus providing a platform for related applications and laying the theoretical basis for further research. Imprecise-information processing – a type of processing based on flexible linguistic values and quantifiable rigid linguistic values – is an important component of intelligence science and technology. This book offers an easy-to-understand overview of the basic principles and methods of imprecise-information processing, allowing readers to develop related applications or pursue further research.

This book consists of thirteen chapters covering many facts like psycho-social intervention on emotional disorders in individuals, impact of emotion and cognition on blended theory, theory and implication of information processing, effects of emotional self esteem in women, emotional dimension of women in workplace, effects of mental thinking in different age groups irrespective of the gender, negative emotions and its effect on information processing, role of emotions in education and lastly emotional analysis in multi perspective domain adopting machine learning approach. Most of the chapters having experimental studies, with each experiment having different constructs as well as different samples for each data collection. Most of the studies measure information processing within altered mood states, such as depression, anxiety, or positive emotional states, with mental ability tasks being conducted in addition to the experiments of quasi-experimental design.

In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books ́ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework but also systems and signal theory. The most important message of the book and authors is: brains are evolved as a whole and a description of parts although necessary lets one miss the wood for the trees.

This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; knowledge acquisition, representation and reasoning; logical structures of opposition and logical syllogisms; mathematical fuzzy logics; theoretical and applied aspects of imprecise probabilities; data science and machine learning; decision making modeling and applications; e-health; fuzzy methods in data mining and knowledge discovery; soft computing and artificia intelligence techniques in image processing; soft methods in statistics and data analysis; uncertainty, heterogeneity, reliability and explainability in AI; weak and cautious supervised learning.

"This book provides comprehensive coverage and definitions of the most important issues, concepts, trends, and technologies in fuzzy topics applied to databases, discussing current investigation into uncertainty and imprecision management by means of fuzzy sets and fuzzy logic in the field of databases and data mining. It offers a guide to fuzzy information processing in databases"--Provided by publisher.

When we learn from books or daily experience, we make associations and draw inferences on the basis of information that is insufficient for under standing. One example of insufficient information may be a small sample derived from observing experiments. With this perspective, the need for de veloping a better understanding of the behavior of a small sample presents a problem that is far beyond purely academic importance. During the past 15 years considerable progress has been achieved in the study of this issue in China. One distinguished result is the principle of in formation diffusion. According to this principle, it is possible to partly fill gaps caused by incomplete information by changing crisp observations into fuzzy sets so that one can improve the recognition of relationships between input and output. The principle of information diffusion has been proven suc cessful for the estimation of a probability density function. Many successful applications reflect the advantages of this new approach. It also supports an argument that fuzzy set theory can be used not only in "soft" science where some subjective adjustment is necessary, but also in "hard" science where all data are recorded.