What is he best known for?
The fields of study he is best known for:
- Algebra
- Artificial intelligence
- Statistics
His primary areas of study are Mathematical optimization, Fuzzy logic, Fuzzy number, Pythagorean fuzzy sets and Pythagorean theorem.
Harish Garg has included themes like Intuitionistic fuzzy, Algorithm, Series and Degree in his Mathematical optimization study.
His study on Soft set is often connected to Measure as part of broader study in Fuzzy logic.
His study in Fuzzy number is interdisciplinary in nature, drawing from both Discrete mathematics, Fuzzy set operations and Fuzzy classification.
His Pythagorean fuzzy sets study combines topics from a wide range of disciplines, such as Interval valued and Operations research.
His studies examine the connections between Pythagorean theorem and genetics, as well as such issues in Generalized mean, with regards to Square.
His most cited work include:
- A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making (327 citations)
- A hybrid PSO-GA algorithm for constrained optimization problems (289 citations)
- Generalized Pythagorean Fuzzy Geometric Aggregation Operators Using Einstein t‐Norm and t‐Conorm for Multicriteria Decision‐Making Process (194 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Fuzzy logic, Intuitionistic fuzzy, Fuzzy set and Fuzzy number.
His work carried out in the field of Mathematical optimization brings together such families of science as TOPSIS and Degree.
His biological study spans a wide range of topics, including Pythagorean theorem, Reliability and Computational intelligence.
His Intuitionistic fuzzy study also includes
- Algebra that intertwine with fields like T-norm,
- Interval valued which intersects with area such as Operational laws.
His Fuzzy set research incorporates elements of Probabilistic logic, Theoretical computer science and Data mining, Aggregate.
His Fuzzy number research is multidisciplinary, incorporating perspectives in Discrete mathematics, Fuzzy set operations and Fuzzy classification.
He most often published in these fields:
- Mathematical optimization (37.41%)
- Fuzzy logic (31.65%)
- Intuitionistic fuzzy (22.66%)
What were the highlights of his more recent work (between 2019-2021)?
- Fuzzy logic (31.65%)
- Fuzzy set (21.22%)
- Mathematical optimization (37.41%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Fuzzy logic, Fuzzy set, Mathematical optimization, Computational intelligence and Group decision-making.
His work on Fuzzy number as part of general Fuzzy logic research is frequently linked to Operator, bridging the gap between disciplines.
His research in Fuzzy set intersects with topics in Multiple-criteria decision analysis, Data mining and Extension.
His work deals with themes such as Intuitionistic fuzzy, TOPSIS, Membership function, Vagueness and Fuzzy soft set, which intersect with Mathematical optimization.
The study incorporates disciplines such as Soft set and Algorithm in addition to Intuitionistic fuzzy.
The various areas that Harish Garg examines in his Computational intelligence study include Ranking, Relation, Variable and Real number.
Between 2019 and 2021, his most popular works were:
- Multi-criteria group decision making based on ELECTRE I method in Pythagorean fuzzy information (61 citations)
- Multiattribute group decision making based on neutrality aggregation operators of q-rung orthopair fuzzy sets (46 citations)
- Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algebra
- Statistics
The scientist’s investigation covers issues in Fuzzy logic, Fuzzy set, Measure, Group decision-making and Operator.
His studies in Fuzzy logic integrate themes in fields like Pythagorean theorem, Mathematical optimization, Computational intelligence and Decision analysis.
The Mathematical optimization study combines topics in areas such as Soft set, TOPSIS and Group.
His Fuzzy set research includes elements of Range, Multiple-criteria decision analysis, Theoretical computer science and Probabilistic logic.
His Multiple-criteria decision analysis study which covers Algorithm that intersects with Hausdorff space, Distance measures and Pattern recognition.
The concepts of his Degree study are interwoven with issues in Ranking, Aggregate and Interval valued.
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