What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Real number
Jun Ye focuses on Ideal, Interval, Artificial intelligence, Measure and Function.
Jun Ye interconnects Ranking, Mathematical optimization and Data mining in the investigation of issues within Ideal.
His Interval study incorporates themes from Joint, Probability and statistics, Applied mathematics and Trigonometric functions.
His research in Artificial intelligence intersects with topics in TOPSIS and Variable.
His studies in Measure integrate themes in fields like Indeterminate, Discrete mathematics, Generalization and Cosine similarity.
Jun Ye combines subjects such as Score, Rock mass classification, Standard deviation, Rock mechanics and Rule-based machine translation with his study of Function.
His most cited work include:
- A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets (393 citations)
- Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making (295 citations)
- Multiple-attribute Decision-Making Method under a Single-Valued Neutrosophic Hesitant Fuzzy Environment (75 citations)
What are the main themes of his work throughout his whole career to date?
Jun Ye spends much of his time researching Artificial intelligence, Interval, Multiple attribute, Indeterminate and Similarity measure.
His Artificial intelligence research includes elements of Function and Pattern recognition.
While working on this project, he studies both Interval and Operator.
His Multiple attribute research incorporates elements of Correlation and Natural language processing.
His study in Similarity measure is interdisciplinary in nature, drawing from both Similarity and Tangent.
In his research, Euclidean distance is intimately related to Ideal, which falls under the overarching field of Data mining.
He most often published in these fields:
- Artificial intelligence (29.69%)
- Interval (26.56%)
- Multiple attribute (23.44%)
What were the highlights of his more recent work (between 2018-2020)?
- Artificial intelligence (29.69%)
- Similarity measure (17.19%)
- Multiple attribute (23.44%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Similarity measure, Multiple attribute, Pattern recognition and Rock mass classification.
Artificial intelligence is frequently linked to Trigonometric functions in his study.
His Similarity measure research is multidisciplinary, relying on both Mathematical analysis, Tangent, Slope stability, Slope stability analysis and Fuzzy number.
His work often combines Multiple attribute and Group decision-making studies.
His Pattern recognition study combines topics from a wide range of disciplines, such as Object and Eye tracking.
His Rock mass classification research is multidisciplinary, incorporating perspectives in Ellipse and Geometry.
Between 2018 and 2020, his most popular works were:
- A Dice Similarity Measure for TBM Penetrability Classification in Hard Rock Condition with the Intuitionistic Fuzzy Information of Rock Mass Properties (7 citations)
- Linguistic neutrosophic uncertain numbers and their multiple attribute group decision-making method (5 citations)
- Multiple attribute decision method using similarity measure of cubic hesitant fuzzy sets (3 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Real number
His scientific interests lie mostly in Multiple attribute, Artificial intelligence, Similarity measure, Natural language processing and Group decision-making.
His studies deal with areas such as Fuzzy set, Pattern recognition and Decision model as well as Multiple attribute.
His Similarity measure study integrates concerns from other disciplines, such as Key and Dice.
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