What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Kaoru Hirota focuses on Artificial intelligence, Fuzzy logic, Fuzzy control system, Quantum computer and Algorithm.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Computer vision and Pattern recognition.
Kaoru Hirota combines subjects such as Quantum information and Quantum circuit with his study of Computer vision.
His Fuzzy logic research focuses on Fuzzy set in particular.
His biological study spans a wide range of topics, including Theoretical computer science and Data compression ratio.
The Adaptive neuro fuzzy inference system study combines topics in areas such as Fuzzy number, Defuzzification and Fuzzy set operations.
His most cited work include:
- A flexible representation of quantum images for polynomial preparation, image compression, and processing operations (333 citations)
- Size reduction by interpolation in fuzzy rule bases (213 citations)
- Concepts of probabilistic sets (201 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Fuzzy logic, Computer vision, Pattern recognition and Algorithm.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
Kaoru Hirota has researched Fuzzy logic in several fields, including Robot, Image compression and Data mining.
Particularly relevant to Image processing is his body of work in Computer vision.
His Pattern recognition research incorporates themes from Feature and Image retrieval.
His Adaptive neuro fuzzy inference system research incorporates elements of Fuzzy number, Defuzzification and Fuzzy set operations.
He most often published in these fields:
- Artificial intelligence (48.57%)
- Fuzzy logic (34.10%)
- Computer vision (16.95%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (48.57%)
- Fuzzy logic (34.10%)
- Pattern recognition (15.43%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Fuzzy logic, Pattern recognition, Human–robot interaction and Feature extraction.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.
His work on Fuzzy rule as part of general Fuzzy logic study is frequently linked to Two layer, therefore connecting diverse disciplines of science.
His study on Convolutional neural network, Feature selection and AdaBoost is often connected to Modal as part of broader study in Pattern recognition.
His research investigates the connection between Human–robot interaction and topics such as Social robot that intersect with problems in Emotion classification.
His Feature extraction study integrates concerns from other disciplines, such as Cross-validation, Surprise, RGB color model, Convolution and Gesture.
Between 2015 and 2021, his most popular works were:
- Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (79 citations)
- Design and implementation of a simple dynamical 4-D chaotic circuit with applications in image encryption (57 citations)
- A Nested Tensor Product Model Transformation (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Kaoru Hirota mostly deals with Artificial intelligence, Fuzzy logic, Social robot, Human–robot interaction and Robot.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
In his work, Kaoru Hirota performs multidisciplinary research in Fuzzy logic and Convex hull.
His Human–robot interaction study combines topics from a wide range of disciplines, such as Cluster analysis and Mobile robot.
His research investigates the connection between Cluster analysis and topics such as Kernel that intersect with issues in Feature and Computer vision.
His work investigates the relationship between Robot and topics such as Human–computer interaction that intersect with problems in Motion.
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.
