What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Mohammad Teshnehlab mostly deals with Artificial intelligence, Artificial neural network, Particle swarm optimization, Fuzzy logic and Mathematical optimization.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
His Artificial neural network research is multidisciplinary, incorporating elements of Control engineering, Control theory, Open-loop controller and Anomaly detection.
His work carried out in the field of Fuzzy logic brings together such families of science as Algorithm and Control theory.
His work in the fields of Mathematical optimization, such as Multi-swarm optimization and Genetic algorithm, intersects with other areas such as Square root.
Mohammad Teshnehlab combines subjects such as Stability and Gradient descent with his study of Adaptive neuro fuzzy inference system.
His most cited work include:
- A novel binary particle swarm optimization (257 citations)
- Using adaptive neuro-fuzzy inference system for hydrological time series prediction (116 citations)
- Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation (108 citations)
What are the main themes of his work throughout his whole career to date?
Mohammad Teshnehlab spends much of his time researching Control theory, Artificial intelligence, Artificial neural network, Fuzzy control system and Fuzzy logic.
His study focuses on the intersection of Control theory and fields such as Control engineering with connections in the field of Control.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Particle swarm optimization and Pattern recognition.
His research combines Genetic algorithm and Artificial neural network.
Mohammad Teshnehlab is interested in Adaptive neuro fuzzy inference system, which is a field of Fuzzy control system.
In his study, Gradient descent is strongly linked to Algorithm, which falls under the umbrella field of Adaptive neuro fuzzy inference system.
He most often published in these fields:
- Control theory (34.58%)
- Artificial intelligence (34.24%)
- Artificial neural network (30.17%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (34.24%)
- Artificial neural network (30.17%)
- Control theory (34.58%)
In recent papers he was focusing on the following fields of study:
Mohammad Teshnehlab focuses on Artificial intelligence, Artificial neural network, Control theory, Machine learning and Deep learning.
Mohammad Teshnehlab interconnects Field and Pattern recognition in the investigation of issues within Artificial intelligence.
The study incorporates disciplines such as Wavelet and Nonlinear system identification, System identification in addition to Artificial neural network.
His Control theory research incorporates themes from Mixture model, Particle swarm optimization and Component.
His study looks at the relationship between Particle swarm optimization and fields such as PID controller, as well as how they intersect with chemical problems.
His research integrates issues of Weighting, Chaotic and Fuzzy control system, Fuzzy logic in his study of Nonlinear system.
Between 2016 and 2021, his most popular works were:
- Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks (67 citations)
- An anomaly detection method to detect web attacks using Stacked Auto-Encoder (38 citations)
- negFIN: An efficient algorithm for fast mining frequent itemsets (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Artificial neural network, Machine learning, Feature extraction and Deep learning.
Mohammad Teshnehlab works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Sensitivity, as a part of the same area of interest.
The various areas that Mohammad Teshnehlab examines in his Artificial neural network study include Control theory and Nonlinear system.
In general Machine learning study, his work on Convolutional neural network often relates to the realm of Breast cancer, thereby connecting several areas of interest.
His Feature extraction study integrates concerns from other disciplines, such as Classifier, Anomaly detection, Autoencoder and Decision tree.
His Deep learning study also includes fields such as
- Recurrent neural network, which have a strong connection to Field, Tree structure, Pooling and Computational intelligence,
- Network architecture which intersects with area such as Restricted Boltzmann machine and Boltzmann machine.
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