What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Jonathan M. Garibaldi mostly deals with Artificial intelligence, Fuzzy set, Fuzzy logic, Mathematical optimization and Fuzzy number.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His Fuzzy set research is multidisciplinary, incorporating elements of Operator, Aggregate and Interval.
His studies deal with areas such as Heuristics, Heuristic and Benchmark as well as Fuzzy logic.
His Mathematical optimization research focuses on subjects like Fuzzy rule, which are linked to Fuzzy associative matrix.
His Fuzzy number research is multidisciplinary, relying on both Fuzzy set operations, Fuzzy classification and Data mining.
His most cited work include:
- Can machine-learning improve cardiovascular risk prediction using routine clinical data? (359 citations)
- Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome (324 citations)
- Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism (226 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Fuzzy logic, Fuzzy set, Machine learning and Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Breast cancer and Pattern recognition.
His research investigates the link between Fuzzy logic and topics such as Control theory that cross with problems in Mobile robot.
His studies in Fuzzy set integrate themes in fields like Type, Mathematical optimization and Interval.
His Artificial neural network study, which is part of a larger body of work in Machine learning, is frequently linked to Set, bridging the gap between disciplines.
Jonathan M. Garibaldi works mostly in the field of Fuzzy number, limiting it down to topics relating to Fuzzy classification and, in certain cases, Neuro-fuzzy.
He most often published in these fields:
- Artificial intelligence (43.21%)
- Fuzzy logic (26.32%)
- Fuzzy set (22.44%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (43.21%)
- Fuzzy logic (26.32%)
- Fuzzy set (22.44%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Fuzzy logic, Fuzzy set, Fuzzy control system and Machine learning.
His Artificial intelligence study incorporates themes from Stability, Process and Pattern recognition.
His Fuzzy logic study also includes
- Data mining, which have a strong connection to Cluster analysis,
- Control theory that connect with fields like Singleton,
- Operator which is related to area like Evolutionary algorithm.
His research in Fuzzy set intersects with topics in Range, Algorithm, Interval and Time series.
Jonathan M. Garibaldi regularly ties together related areas like Breast cancer in his Machine learning studies.
His Defuzzification research incorporates themes from Fuzzy set operations, Fuzzy classification and Set.
Between 2015 and 2021, his most popular works were:
- Can machine-learning improve cardiovascular risk prediction using routine clinical data? (359 citations)
- A new accuracy measure based on bounded relative error for time series forecasting. (74 citations)
- Improved Uncertainty Capture for Nonsingleton Fuzzy Systems (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Jonathan M. Garibaldi spends much of his time researching Fuzzy set, Fuzzy logic, Algorithm, Fuzzy control system and Artificial intelligence.
Jonathan M. Garibaldi has included themes like Singleton, Management science, MATLAB, Control theory and Implementation in his Fuzzy set study.
His study looks at the relationship between Fuzzy logic and topics such as Control theory, which overlap with Trajectory, Inertial measurement unit, Sensitivity and Simultaneous localization and mapping.
His Algorithm study combines topics from a wide range of disciplines, such as Direct method, Data mining, Interval and Membership function.
His Fuzzy control system research includes themes of Fuzzy number and Defuzzification.
The Artificial intelligence study combines topics in areas such as Machine learning and Game theory.
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