2021 - IEEE Fellow For contributions to computational intelligence techniques in data analysis and decision support
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 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.
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
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Stephen F. Weng;Jenna Reps;Joe Kai;Jonathan M. Garibaldi.
PLOS ONE (2017)
Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome
Somaia E. Elsheikh;Andrew R. Green;Emad A. Rakha;Des G. Powe.
Cancer Research (2009)
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism
Leah R. Band;Darren M. Wells;Antoine Larrieu;Jianyong Sun.
Proceedings of the National Academy of Sciences of the United States of America (2012)
Umbilical cord blood gas analysis at delivery: a time for quality data
Jennifer A. Westgate;Jonathan M. Garibaldi;Keith R. Greene.
British Journal of Obstetrics and Gynaecology (1994)
Receiver operating characteristic analysis for intelligent medical systems-a new approach for finding confidence intervals
J.B. Tilbury;W.J. Van Eetvelt;J.M. Garibaldi;J.S.H. Curnsw.
IEEE Transactions on Biomedical Engineering (2000)
A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram.
Robert D. F. Keith;Sarah Beckley;Jonathan M. Garibaldi;Jenny A. Westgate.
British Journal of Obstetrics and Gynaecology (1995)
Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers
Shang-Ming Zhou;Francisco Chiclana;Robert I. John;Jonathan M. Garibaldi.
Fuzzy Sets and Systems (2008)
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
Jianyong Sun;Jonathan M. Garibaldi;Charlie Hodgman.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2012)
Fuzzy multiple heuristic orderings for examination timetabling
Hishammuddin Asmuni;Edmund K. Burke;Jonathan M. Garibaldi;Barry McCollum.
Lecture Notes in Computer Science (2004)
Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making
J.M. Garibaldi;T. Ozen.
IEEE Transactions on Fuzzy Systems (2007)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: