What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Diego Oliva mainly focuses on Mathematical optimization, Photovoltaic system, Estimation theory, Algorithm and Solar cell. His Photovoltaic system study spans across into subjects like Electronic engineering and Chaotic. His Algorithm research includes elements of Hough transform and Artificial intelligence, Benchmark.
Much of his study explores Artificial intelligence relationship to Pattern recognition. He integrates Solar cell with Solar energy in his research. His work often combines Solar energy and Robustness studies.
His most cited work include:
- Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm (226 citations)
- Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm (226 citations)
- Parameter identification of solar cells using artificial bee colony optimization (196 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Algorithm, Segmentation and Thresholding. The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His Algorithm study combines topics in areas such as Swarm behaviour and Benchmark.
The various areas that he examines in his Benchmark study include Computational intelligence, Optimization problem, Mathematical optimization, Global optimization and Feature selection. His work on Differential evolution as part of general Mathematical optimization research is often related to Solar cell and Iterative and incremental development, thus linking different fields of science. Diego Oliva has included themes like Image segmentation, Pixel, Grayscale, Histogram and Evolutionary algorithm in his Thresholding study.
He most often published in these fields:
- Artificial intelligence (60.12%)
- Pattern recognition (38.10%)
- Algorithm (33.33%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (60.12%)
- Metaheuristic (28.57%)
- Algorithm (33.33%)
In recent papers he was focusing on the following fields of study:
Diego Oliva mainly investigates Artificial intelligence, Metaheuristic, Algorithm, Pattern recognition and Image segmentation. Diego Oliva combines subjects such as Robot and Robotics with his study of Metaheuristic. He interconnects Feature selection and Benchmark in the investigation of issues within Algorithm.
His study in the field of Segmentation also crosses realms of Fundus and Lévy flight. His Image segmentation study combines topics from a wide range of disciplines, such as Swarm behaviour and Thresholding. His biological study spans a wide range of topics, including Image processing, Optimization problem, Differential evolution and Grayscale.
Between 2020 and 2021, his most popular works were:
- BEPO: A novel binary emperor penguin optimizer for automatic feature selection (22 citations)
- BEPO: A novel binary emperor penguin optimizer for automatic feature selection (22 citations)
- Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Diego Oliva mostly deals with Metaheuristic, Algorithm, Image segmentation, Artificial intelligence and Swarm behaviour. His studies in Algorithm integrate themes in fields like Feature selection, Crossover and Benchmark. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition.
His studies deal with areas such as Local optimum, Rate of convergence, Solver and Ant colony optimization algorithms as well as Swarm behaviour. His Segmentation research incorporates themes from Image processing, Pixel, Differential evolution, Optimization algorithm and Entropy. His Thresholding study integrates concerns from other disciplines, such as Optimization problem and Grayscale.
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