What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
John B. Theocharis mainly focuses on Fuzzy logic, Artificial intelligence, Wind speed, Recurrent neural network and Algorithm.
His studies link Artificial neural network with Fuzzy logic.
As a member of one scientific family, he mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Fuzzy rule.
John B. Theocharis combines subjects such as Gradient descent and Finite impulse response with his study of Wind speed.
The concepts of his Recurrent neural network study are interwoven with issues in Adaptive control, Constrained optimization and System identification.
John B. Theocharis has researched Algorithm in several fields, including Curve fitting and Genetic algorithm, Mathematical optimization, Multi-objective optimization.
His most cited work include:
- A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation (407 citations)
- Long-term wind speed and power forecasting using local recurrent neural network models (398 citations)
- A recurrent fuzzy-neural model for dynamic system identification (240 citations)
What are the main themes of his work throughout his whole career to date?
John B. Theocharis mainly investigates Artificial intelligence, Fuzzy logic, Pattern recognition, Fuzzy rule and Algorithm.
His study brings together the fields of Machine learning and Artificial intelligence.
His work carried out in the field of Fuzzy logic brings together such families of science as Artificial neural network, Genetic algorithm and Control theory.
His work on Recurrent neural network as part of general Artificial neural network study is frequently linked to Term, therefore connecting diverse disciplines of science.
His Feature extraction, Support vector machine and Feature vector study in the realm of Pattern recognition interacts with subjects such as Land cover.
His research investigates the connection between Algorithm and topics such as Mathematical optimization that intersect with problems in Parameter identification problem and Structure.
He most often published in these fields:
- Artificial intelligence (61.98%)
- Fuzzy logic (47.11%)
- Pattern recognition (36.36%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (61.98%)
- Fuzzy rule (25.62%)
- Fuzzy logic (47.11%)
In recent papers he was focusing on the following fields of study:
John B. Theocharis focuses on Artificial intelligence, Fuzzy rule, Fuzzy logic, Pattern recognition and Soil test.
His studies in Artificial intelligence integrate themes in fields like Algorithm and Computer vision.
His research in Algorithm tackles topics such as Genetic algorithm which are related to areas like Multiple hypotheses.
His research in Fuzzy logic focuses on subjects like AdaBoost, which are connected to Fuzzy set.
His study in the fields of Support vector machine under the domain of Pattern recognition overlaps with other disciplines such as Land cover.
His work on Neuro-fuzzy as part of his general Fuzzy control system study is frequently connected to Statistical hypothesis testing, thereby bridging the divide between different branches of science.
Between 2013 and 2021, his most popular works were:
- Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery (23 citations)
- A memory-based learning approach utilizing combined spectral sources and geographical proximity for improved VIS-NIR-SWIR soil properties estimation (22 citations)
- A genetic algorithm‐based stacking algorithm for predicting soil organic matter from vis–NIR spectral data (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
John B. Theocharis spends much of his time researching Fuzzy logic, Artificial intelligence, Algorithm, Fuzzy rule and Topsoil.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His Pattern recognition study incorporates themes from Pixel and Computer vision.
His Algorithm research focuses on Genetic algorithm and how it relates to Ensemble learning, Fuzzy set and Fuzzy classification.
His study on Fuzzy rule also encompasses disciplines like
- Differential evolution which connect with Interpretation, Black box, Data mining, Interpretability and Relation,
- Chromosome, Neuro-fuzzy, Fuzzy number and Instance selection most often made with reference to AdaBoost,
- Competitive learning and related Fuzzy control system.
Soil test is closely connected to Soil carbon in his research, which is encompassed under the umbrella topic of Topsoil.
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