N Yogeesh
Biographic Data
| ID | 6952899 |
|---|---|
| NAME | N Yogeesh |
| GIVEN NAMES | N |
| FAMILY NAME | Yogeesh |
| SIGNATURE | YOGEESH N |
| AFFILIATIONS | Government of Karnataka |
| ORCID | 0000-0001-8080-7821 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Bayesian and fuzzy hybrid standard setting for pass-fail decisions in clinical examinations
BACKGROUND: Pass-fail decisions in clinical examinations must be defensible, yet traditional standard-setting approaches often report a single cut score without explicitly quantifying uncertainty-an issue amplified in small cohorts and mixed-format assessments (e.g., OSCE plus written). We propose a Bayesian fuzzy hybrid standard-setting framework that (i) treats station cut scores as posterior distributions and (ii) models the inherently linguis…
Using Fuzzy Analytics to Evaluate Organizational Culture Shifts in the Era of Cybernetics
This study explores how cybernetic principles—emphasizing feedback loops, self-regulation, and continuous adaptation—can drive shifts in organizational culture, and proposes a fuzzy analytics framework to measure and interpret these changes. Drawing on a mixed-methods case study of a medium-sized technology firm, the research integrates qualitative inputs (interviews, focus groups) with quantitative survey data to assess culture indicators such a…
Fuzzy Time Series Modeling for Predicting Economic Trends
In fact, this research investigates the potential use of FTS models for economic indicators prediction in the Asian area, particularly the GDP growth. However, traditional forecasting approaches tend to fail to represent the uncertainty and nonlinearity of the economic data, especially in crisis periods. By applying fuzzy inference rules and defuzzification to create crisp forecasts from fuzzy sets, FTS models capitalize on the principles of fuzz…
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Fuzzy Logic-Based Approach to Behavioral Economics
Even the mathematics that some people want to apply to this decision-making is limited, as consumer decisions are filled with uncertainty, subjective evaluation, and cognitive biases. In this paper, we develop and apply a model entitled Fuzzy Logic-Based Decision Model to evaluate consumers' preferences regarding smartphone selections in the presence of uncertainty. The proposed model utilizes fuzzy set theory, linguistic variables and IF-THEN ru…
Fuzzy Logic-Based Approach to Behavioral Economics
Even the mathematics that some people want to apply to this decision-making is limited, as consumer decisions are filled with uncertainty, subjective evaluation, and cognitive biases. In this paper, we develop and apply a model entitled Fuzzy Logic-Based Decision Model to evaluate consumers' preferences regarding smartphone selections in the presence of uncertainty. The proposed model utilizes fuzzy set theory, linguistic variables and IF-THEN ru…
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Mathematical Strategies for Solving Optimization Problems
In the subject of mathematics and computational science, the optimization-problems refer to a process of selecting a feasible alternatives solution from a set. Many of the ideas given in this paper apply to constrained parameter optimization as well. Contrary to unconstrained optimization, it is more difficult to obtain consistent numerical results, making the selection of an appropriate algorithm more complex. Optimization in finite dimensions. …
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Using Fuzzy Analytics to Evaluate Organizational Culture Shifts in the Era of Cybernetics
This study explores how cybernetic principles—emphasizing feedback loops, self-regulation, and continuous adaptation—can drive shifts in organizational culture, and proposes a fuzzy analytics framework to measure and interpret these changes. Drawing on a mixed-methods case study of a medium-sized technology firm, the research integrates qualitative inputs (interviews, focus groups) with quantitative survey data to assess culture indicators such a…
Fuzzy Time Series Modeling for Predicting Economic Trends
In fact, this research investigates the potential use of FTS models for economic indicators prediction in the Asian area, particularly the GDP growth. However, traditional forecasting approaches tend to fail to represent the uncertainty and nonlinearity of the economic data, especially in crisis periods. By applying fuzzy inference rules and defuzzification to create crisp forecasts from fuzzy sets, FTS models capitalize on the principles of fuzz…
A Fuzzy Graphical Approach to Modelling Chemical Interactions
Chemical interactions are central to many scientific and industrial processes, yet conventional modelling methods can struggle to sufficiently represent the complexity of these interactions or their intrinsic uncertainty. Appearing in: Kobi Gal and Stuart Russell, Logical Bayesian Networks. This model is exemplified through two case studies, the reaction of acetone and iodine in acidic medium, as well as a receptor-ligand system containing Imatin…
No prominent works on this page.
A Fuzzy Graphical Approach to Modelling Chemical Interactions
Chemical interactions are central to many scientific and industrial processes, yet conventional modelling methods can struggle to sufficiently represent the complexity of these interactions or their intrinsic uncertainty. Appearing in: Kobi Gal and Stuart Russell, Logical Bayesian Networks. This model is exemplified through two case studies, the reaction of acetone and iodine in acidic medium, as well as a receptor-ligand system containing Imatin…
Fuzzy Logic-Based Approach to Behavioral Economics
Even the mathematics that some people want to apply to this decision-making is limited, as consumer decisions are filled with uncertainty, subjective evaluation, and cognitive biases. In this paper, we develop and apply a model entitled Fuzzy Logic-Based Decision Model to evaluate consumers' preferences regarding smartphone selections in the presence of uncertainty. The proposed model utilizes fuzzy set theory, linguistic variables and IF-THEN ru…
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Mathematical Strategies for Solving Optimization Problems
In the subject of mathematics and computational science, the optimization-problems refer to a process of selecting a feasible alternatives solution from a set. Many of the ideas given in this paper apply to constrained parameter optimization as well. Contrary to unconstrained optimization, it is more difficult to obtain consistent numerical results, making the selection of an appropriate algorithm more complex. Optimization in finite dimensions. …
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Using Fuzzy Analytics to Evaluate Organizational Culture Shifts in the Era of Cybernetics
This study explores how cybernetic principles—emphasizing feedback loops, self-regulation, and continuous adaptation—can drive shifts in organizational culture, and proposes a fuzzy analytics framework to measure and interpret these changes. Drawing on a mixed-methods case study of a medium-sized technology firm, the research integrates qualitative inputs (interviews, focus groups) with quantitative survey data to assess culture indicators such a…
Fuzzy Time Series Modeling for Predicting Economic Trends
In fact, this research investigates the potential use of FTS models for economic indicators prediction in the Asian area, particularly the GDP growth. However, traditional forecasting approaches tend to fail to represent the uncertainty and nonlinearity of the economic data, especially in crisis periods. By applying fuzzy inference rules and defuzzification to create crisp forecasts from fuzzy sets, FTS models capitalize on the principles of fuzz…
Using Fuzzy Analytics to Evaluate Organizational Culture Shifts in the Era of Cybernetics
This study explores how cybernetic principles—emphasizing feedback loops, self-regulation, and continuous adaptation—can drive shifts in organizational culture, and proposes a fuzzy analytics framework to measure and interpret these changes. Drawing on a mixed-methods case study of a medium-sized technology firm, the research integrates qualitative inputs (interviews, focus groups) with quantitative survey data to assess culture indicators such a…
Fuzzy Time Series Modeling for Predicting Economic Trends
In fact, this research investigates the potential use of FTS models for economic indicators prediction in the Asian area, particularly the GDP growth. However, traditional forecasting approaches tend to fail to represent the uncertainty and nonlinearity of the economic data, especially in crisis periods. By applying fuzzy inference rules and defuzzification to create crisp forecasts from fuzzy sets, FTS models capitalize on the principles of fuzz…
A Comprehensive Algebraic Framework for Fuzzy Graphs and Their Operators
This study presents a comprehensive algebraic framework for fuzzy graphs that extends classical graph theory to accommodate uncertainty and partial relationships. We define fuzzy graph operators—namely, fuzzy union (via the maximum function), fuzzy intersection (via the minimum function), and fuzzy complement (via membership inversion)—and demonstrate that these operations endow the set of fuzzy graphs with an idempotent semiring or lattice-like …
Fuzzy Clustering for Economic Data Mining
Fuzzy clustering techniques represent a natural extension of traditional clustering methodologies that have been developed to more accurately model uncertainty and imprecision in economic datasets. We argue that fuzzy clustering is well-suited to tackle many real-world economic problems, including, but not limited to, market segmentation and economic forecasting. Instead of forcing a data point would belong to only one cluster, the fuzzy clusteri…
Fuzzy Logic-Based Approach to Behavioral Economics
Even the mathematics that some people want to apply to this decision-making is limited, as consumer decisions are filled with uncertainty, subjective evaluation, and cognitive biases. In this paper, we develop and apply a model entitled Fuzzy Logic-Based Decision Model to evaluate consumers' preferences regarding smartphone selections in the presence of uncertainty. The proposed model utilizes fuzzy set theory, linguistic variables and IF-THEN ru…
Bayesian and fuzzy hybrid standard setting for pass-fail decisions in clinical examinations
BACKGROUND: Pass-fail decisions in clinical examinations must be defensible, yet traditional standard-setting approaches often report a single cut score without explicitly quantifying uncertainty-an issue amplified in small cohorts and mixed-format assessments (e.g., OSCE plus written). We propose a Bayesian fuzzy hybrid standard-setting framework that (i) treats station cut scores as posterior distributions and (ii) models the inherently linguis…
Computer Science (12 works) · Fuzzy logic (11 works) · Artificial Intelligence (6 works) · Machine learning (6 works) · Artificial Intelligence (5 works) · Mathematics (5 works) · Complex Systems and Time Series Analysis (4 works) · Fuzzy set (4 works) · Management science (4 works) · Data science (3 works)