What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Support vector machine, Artificial neural network, Algorithm, Artificial intelligence and Extreme learning machine.
His Support vector machine study frequently links to other fields, such as Data mining.
The various areas that Sancho Salcedo-Sanz examines in his Artificial neural network study include Covariance, Wind speed, Remote sensing and Kriging.
His Algorithm study incorporates themes from Renewable energy and Sequential minimal optimization.
The Artificial intelligence study combines topics in areas such as Group technology, Genetic algorithm, Machine learning, Staff management and Parallel algorithm.
Sancho Salcedo-Sanz combines subjects such as Meteorology, Weather Research and Forecasting Model, Simulation, Perceptron and Feature selection with his study of Extreme learning machine.
His most cited work include:
- Survey A survey on applications of the harmony search algorithm (244 citations)
- Bio-inspired computation: Where we stand and what's next (172 citations)
- Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction (145 citations)
What are the main themes of his work throughout his whole career to date?
Sancho Salcedo-Sanz spends much of his time researching Mathematical optimization, Artificial intelligence, Algorithm, Evolutionary algorithm and Genetic algorithm.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition.
His study on Support vector machine, Feature selection and Selection is often connected to Context as part of broader study in Machine learning.
He combines subjects such as Artificial neural network, Meteorology and Data mining with his study of Support vector machine.
The study incorporates disciplines such as Evolutionary computation, Scheduling and Theoretical computer science in addition to Evolutionary algorithm.
His research integrates issues of Assignment problem, Simulated annealing, Computer network and Combinatorial optimization in his study of Genetic algorithm.
He most often published in these fields:
- Mathematical optimization (26.11%)
- Artificial intelligence (22.55%)
- Algorithm (20.47%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (26.11%)
- Algorithm (20.47%)
- Artificial intelligence (22.55%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Algorithm, Artificial intelligence, Artificial neural network and Machine learning.
His Mathematical optimization research focuses on Coral reef and how it connects with Structure.
His research investigates the link between Algorithm and topics such as Segmentation that cross with problems in Memetic algorithm.
Sancho Salcedo-Sanz has included themes like Evolutionary algorithm, Data mining, Support vector machine and Time horizon in his Artificial neural network study.
His Machine learning study combines topics from a wide range of disciplines, such as Field and Renewable energy.
His work in Feature selection tackles topics such as Hybrid system which are related to areas like Genetic algorithm.
Between 2017 and 2021, his most popular works were:
- Bio-inspired computation: Where we stand and what's next (172 citations)
- Predicting compressive strength of lightweight foamed concrete using extreme learning machine model (117 citations)
- An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Optimization problem, Algorithm and Extreme learning machine.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Wind speed and Renewable energy.
The subject of his Optimization problem research is within the realm of Mathematical optimization.
His Algorithm study integrates concerns from other disciplines, such as Swell and Significant wave height.
His Extreme learning machine research incorporates themes from Genetic algorithm and Hybrid system.
His research in Artificial neural network focuses on subjects like Support vector machine, which are connected to Regression and Predictability.
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