What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Adel M. Alimi spends much of his time researching Artificial intelligence, Pattern recognition, Handwriting recognition, Artificial neural network and Feature extraction.
The concepts of his Artificial intelligence study are interwoven with issues in Speech recognition and Natural language processing.
His research integrates issues of Fitness proportionate selection, Fitness function and Feature in his study of Pattern recognition.
The Handwriting recognition study combines topics in areas such as Field, Handwriting, Line, Arabic handwriting recognition and Pattern recognition.
His Artificial neural network research is multidisciplinary, relying on both Basis function, Algorithm, Exponential stability and Control theory.
The various areas that Adel M. Alimi examines in his Feature extraction study include Segmentation, Fractal, Selection and Feature vector.
His most cited work include:
- Speed/accuracy trade-offs in target-directed movements. (473 citations)
- The Affective Tutoring System (112 citations)
- Modelling velocity profiles of rapid movements: a comparative study (110 citations)
What are the main themes of his work throughout his whole career to date?
Adel M. Alimi mainly focuses on Artificial intelligence, Pattern recognition, Fuzzy logic, Artificial neural network and Computer vision.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Speech recognition and Natural language processing.
His study in Biometrics extends to Pattern recognition with its themes.
Much of his study explores Artificial neural network relationship to Basis function.
His study looks at the relationship between Fuzzy classification and topics such as Fuzzy set operations, which overlap with Defuzzification.
Adel M. Alimi regularly links together related areas like Handwriting in his Handwriting recognition studies.
He most often published in these fields:
- Artificial intelligence (53.93%)
- Pattern recognition (20.08%)
- Fuzzy logic (13.20%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (53.93%)
- Pattern recognition (20.08%)
- Artificial neural network (12.64%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Artificial neural network, Fuzzy logic and Deep learning are his primary areas of study.
His biological study spans a wide range of topics, including Machine learning and Computer vision.
Adel M. Alimi focuses mostly in the field of Pattern recognition, narrowing it down to topics relating to Biometrics and, in certain cases, Scale-invariant feature transform and Identification.
Data mining is closely connected to Cluster analysis in his research, which is encompassed under the umbrella topic of Fuzzy logic.
His Deep learning research includes elements of Field and Pattern recognition.
His work deals with themes such as Handwriting recognition, Speech recognition, Scripting language and Natural language processing, which intersect with Handwriting.
Between 2015 and 2021, his most popular works were:
- PSO-based analysis of Echo State Network parameters for time series forecasting (90 citations)
- Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication (50 citations)
- Deep Learning System for Automatic License Plate Detection and Recognition (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Adel M. Alimi mostly deals with Artificial intelligence, Pattern recognition, Artificial neural network, Feature extraction and Computer vision.
His Artificial intelligence study frequently involves adjacent topics like Machine learning.
The various areas that he examines in his Pattern recognition study include Pixel, Feature and Biometrics.
His Artificial neural network research is multidisciplinary, incorporating elements of Segmentation, Transport engineering, Traffic congestion and Control theory.
His Feature extraction study combines topics from a wide range of disciplines, such as Image processing, Recurrent neural network, Echo state network and Feature vector.
His Computer vision research incorporates themes from Activity recognition and Representation.
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