What is he best known for?
The fields of study Babak Nadjar Araabi is best known for:
- Statistics
- Inferior longitudinal fasciculus
- Machine learning
In Generalization, Babak Nadjar Araabi works on issues like Mathematical analysis, which are connected to Tree (set theory).
His study ties his expertise on Mathematical analysis together with the subject of Tree (set theory).
Research on Iris recognition and IRIS (biosensor) is a part of his Biometrics study.
His IRIS (biosensor) study often links to related topics such as Biometrics.
Much of his study explores Artificial intelligence relationship to Selection (genetic algorithm).
His Artificial intelligence research extends to Selection (genetic algorithm), which is thematically connected.
Babak Nadjar Araabi integrates Fuzzy logic with Fuzzy control system in his research.
Babak Nadjar Araabi undertakes interdisciplinary study in the fields of Fuzzy control system and Adaptive neuro fuzzy inference system through his works.
His Adaptive neuro fuzzy inference system study frequently draws connections between adjacent fields such as Fuzzy logic.
His most cited work include:
- Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration (132 citations)
- Pigment Melanin: Pattern for Iris Recognition (103 citations)
- Computer-assisted photo-identification of individual marine vertebrates: a multi-species system (61 citations)
What are the main themes of his work throughout his whole career to date
Babak Nadjar Araabi connects Artificial intelligence with Computer vision in his study.
In his works, he conducts interdisciplinary research on Machine learning and Reinforcement learning.
In his research, he undertakes multidisciplinary study on Algorithm and Artificial intelligence.
In his works, Babak Nadjar Araabi undertakes multidisciplinary study on Data mining and Machine learning.
His study brings together the fields of Control theory (sociology) and Control (management).
He regularly ties together related areas like Control (management) in his Control theory (sociology) studies.
Biochemistry and Gene are two areas of study in which he engages in interdisciplinary work.
Borrowing concepts from Robustness (evolution), he weaves in ideas under Gene.
His Robustness (evolution) study often links to related topics such as Biochemistry.
Babak Nadjar Araabi most often published in these fields:
- Artificial intelligence (82.76%)
- Machine learning (36.21%)
- Pattern recognition (psychology) (31.03%)
What were the highlights of his more recent work (between 2019-2022)?
- Machine learning (60.00%)
- Artificial intelligence (60.00%)
- Functional connectivity (40.00%)
In recent works Babak Nadjar Araabi was focusing on the following fields of study:
His study focuses on the intersection of Time series and fields such as Machine learning with connections in the field of Independent component analysis, Feature selection, Bivariate analysis, Regret and Reinforcement learning.
His research brings together the fields of Machine learning and Bivariate analysis.
The Coordinate descent study which covers Algorithm that intersects with Computation.
In his research, he undertakes multidisciplinary study on Computation and Algorithm.
His Geometry study frequently intersects with other fields, such as Correlation and Inverse.
As part of his studies on Correlation, Babak Nadjar Araabi frequently links adjacent subjects like Geometry.
His research on Artificial intelligence often connects related topics like Pattern recognition (psychology).
As part of his studies on Pattern recognition (psychology), Babak Nadjar Araabi frequently links adjacent subjects like Artificial intelligence.
His research on Functional connectivity often connects related areas such as Neuroscience.
Between 2019 and 2022, his most popular works were:
- Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity (18 citations)
- Online Forecasting of Synchronous Time Series Based on Evolving Linear Models (15 citations)
- A new EM algorithm for flexibly tied GMMs with large number of components (13 citations)
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