Valentina Emilia Balas

The book explores advanced risk management strategies through fuzzy decision-making and optimisation techniques. It provides a structured approach to handling uncertainty, ambiguity, and imprecise data in critical decision-making environments. This book examines fuzzy set theory, fuzzy numbers, fuzzy decision trees, and fuzzy clustering for risk assessment. With a focus on fuzzy goal programming, the book offers practical methodologies for maximising returns, minimising risks, and maintaining liquidity across diverse sectors. Integrates finance, management, mathematics, information technology, and engineering to enhance risk assessment. Includes case studies on credit risk assessment, portfolio optimisation, supplier selection, and environmental impact analysis. Provides frameworks for addressing ambiguity in data-driven decision-making environments. Demonstrates how fuzzy techniques enhance risk mitigation and strategic decision-making and offers step-by-step methodologies for applying fuzzy optimisation in real-world scenarios. This book is a valuable resource for students, researchers, and professionals seeking to improve decision-making under uncertainty in finance, management, and engineering.

Emerging Fuzzy Intelligent Systems for Smart Healthcare Management: Applications of Disc q-Rung Orthopair Fuzzy Sets presents comprehensive methodological frameworks and the latest empirical research findings concerning disc q-rung orthopair fuzzy operators, with a specific focus on their applications in smart technologies for healthcare management. The book solves a crucial problem by offering readers an invaluable opportunity to conduct a comparative analysis, contrasting the proposed methods with their existing knowledge base. Disc q-Rung Orthopair sets, being the generalization of q-Rung Orthopair fuzzy sets, which are, in turn, the generalization of Pythagorean fuzzy sets, extend the capabilities of handling uncertainty beyond conventional fuzzy sets. The authors strive to narrow the knowledge gap by clarifying the practical applications of disc q-rung orthopair fuzzy logic. In addition, it explores an enhanced version of q-Rung Orthopair Fuzzy Sets, specifically focusing on Disc q-Rung Orthopair Fuzzy Sets, introducing various types of operators. These operators play a crucial role in solving decision-making and optimization problems. A notable contribution is the development of a hybrid operator, termed as the Disc q-Rung Orthopair Fuzzy Hybrid Weighted Averaging/Geometric (D-qROFHWA/G) operator.

Picture Fuzzy Logic and Its Applications in Decision Making Problems provides methodological frameworks and the latest empirical research findings in the field of picture fuzzy operators and their applications in scientific research and real-world engineering problems. In this book, picture fuzzy sets are investigated, and different types of operators are defined to solve a number of important decision-making and optimization problems. The hybrid operator on picture fuzzy set based on the combination of picture fuzzy weighted averaging operators and picture fuzzy weighted geometric operators is developed and named Hybrid Picture Fuzzy Weighted Averaging Geometric (H-PFWAG) operator. In addition, another operator is developed for interval-valued picture fuzzy environment, which is named Hybrid Interval-Valued Picture Fuzzy Weighted Averaging Geometric (H-IVPFWAG) operator. These two operators are then demonstrated as solutions to Multiple-Attribute Decision-Making (MADM) problems. The picture fuzzy soft weighted aggregation operators (averaging and geometric) are defined, and these are applied to develop a multi-criteria group decision making system.

Nonlinear Control for Blood Glucose Regulation of Diabetic Patients: An LMI-Based Approach exposes readers to the various existing mathematical models that define the dynamics of glucose-insulin for Type 1 diabetes patients. After providing insights into the mathematical model of patients, the authors discuss the need and emergence of new control techniques that can lead to further development of an artificial pancreas. The book presents various nonlinear control techniques to address the challenges that Type 1 diabetic patients face in maintaining their blood glucose level in the safe range (70-180 mg/dl). The closed-loop solution provided by the artificial pancreas depends mainly on the effectiveness of the control algorithm, which acts as the brain of the system. APS control algorithms require a mathematical model of the gluco-regulatory system of the T1D patients for their design. Since the gluco-regulatory system is inherently nonlinear and largely affected by external disturbances and parametric uncertainty, developing an accurate model is very difficult.

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance. Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation.

This book provides a clear understanding in formulating stability analysis and state feedback control of retarded time delay systems using Lyapunov’s second method in an LMI framework. The chapters offer a clear overview of the evolution of stability analysis in terms of the construction of a Lyapunov functional and use of the integral inequalities in order to reduce the gap of delay upper bound estimate compared to frequency domain method through existing and proposed stability theorems. Power system engineering problem has been presented here to give readers fair idea on applicability of the model and method for solving engineering problems. Without deviating from the framework of analysis more complex dynamics of the system have been dealt with here that includes actuator saturation and thereby ascertaining local stability for an estimated time-delay and domain of attraction. Nonlinearity in a time-delay system has been dealt with in the T-S fuzzy modeling approach. Thisbook is useful as a textbook for Master’s students and advanced researcher working in the field of control system engineering, and for practicing engineers dealing with such complex dynamical systems. The strengths of the book are lucidity of presentation, lucidity of solution method, MATLAB programs given in the appendix that help the novice researcher to carry out research in this area independently, clear idea about the formulation of desired stability and control problem in a LMI framework, application problem provided can motivate students and researcher to recast their problems in the similar framework easily, helpful for readers to use the stability (stabilization) conditions or formulate their own stability conditions easily for a complicated linear or nonlinear dynamical system.

Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions.

This book provides a clear understanding in formulating stability analysis and state feedback control of retarded time delay systems using Lyapunov’s second method in an LMI framework. The chapters offer a clear overview of the evolution of stability analysis in terms of the construction of a Lyapunov functional and use of the integral inequalities in order to reduce the gap of delay upper bound estimate compared to frequency domain method through existing and proposed stability theorems. Power system engineering problem has been presented here to give readers fair idea on applicability of the model and method for solving engineering problems. Without deviating from the framework of analysis more complex dynamics of the system have been dealt with here that includes actuator saturation and thereby ascertaining local stability for an estimated time-delay and domain of attraction. Nonlinearity in a time-delay system has been dealt with in the T-S fuzzy modeling approach. Thisbook is useful as a textbook for Master’s students and advanced researcher working in the field of control system engineering, and for practicing engineers dealing with such complex dynamical systems. The strengths of the book are lucidity of presentation, lucidity of solution method, MATLAB programs given in the appendix that help the novice researcher to carry out research in this area independently, clear idea about the formulation of desired stability and control problem in a LMI framework, application problem provided can motivate students and researcher to recast their problems in the similar framework easily, helpful for readers to use the stability (stabilization) conditions or formulate their own stability conditions easily for a complicated linear or nonlinear dynamical system.