What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Field, Optimization problem, Data science and Machine learning.
His research on Artificial intelligence often connects related topics like Software deployment.
His research integrates issues of Taxonomy, Interpretability, Deep learning and Rule-based system in his study of Field.
The Optimization problem study combines topics in areas such as Genetic algorithm, Computation, Distributed computing and Visualization.
His research in Data science intersects with topics in Big data and Behavioral pattern.
His work on Spiking neural network as part of his general Machine learning study is frequently connected to Term, thereby bridging the divide between different branches of science.
His most cited work include:
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (496 citations)
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (496 citations)
- Bio-inspired computation: Where we stand and what's next (172 citations)
What are the main themes of his work throughout his whole career to date?
Javier Del Ser spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Optimization problem.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Field, Heuristics and Pattern recognition.
In his study, which falls under the umbrella issue of Field, Big data is strongly linked to Data science.
When carried out as part of a general Machine learning research project, his work on Concept drift, Data stream mining and Feature selection is frequently linked to work in Online learning, therefore connecting diverse disciplines of study.
The various areas that he examines in his Optimization problem study include Evolutionary computation, Distributed computing and Metaheuristic.
The study incorporates disciplines such as Task and Taxonomy in addition to Deep learning.
He most often published in these fields:
- Artificial intelligence (43.44%)
- Machine learning (25.95%)
- Algorithm (18.08%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (43.44%)
- Deep learning (12.83%)
- Machine learning (25.95%)
In recent papers he was focusing on the following fields of study:
Javier Del Ser mostly deals with Artificial intelligence, Deep learning, Machine learning, Field and Optimization problem.
His Artificial intelligence research is multidisciplinary, incorporating elements of Swarm intelligence, Task, Heuristics and Data science.
His studies in Deep learning integrate themes in fields like Convolutional neural network and Taxonomy.
His Machine learning research is multidisciplinary, relying on both Computation and State.
The concepts of his Field study are interwoven with issues in Interpretability, Computational intelligence, Management science and Intelligent transportation system.
The Interpretability study which covers Rule-based system that intersects with Software deployment.
Between 2019 and 2021, his most popular works were:
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (496 citations)
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (496 citations)
- Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Javier Del Ser focuses on Artificial intelligence, Machine learning, Deep learning, Field and Metaheuristic.
His Artificial intelligence research incorporates elements of Heuristics and Data science.
His work on Concept drift, Spiking neural network and Transfer of learning as part of general Machine learning research is frequently linked to Retraining and Online learning, thereby connecting diverse disciplines of science.
His work deals with themes such as Feature extraction, Expert system, Time series and Taxonomy, which intersect with Deep learning.
His research in Field intersects with topics in Management science, Software deployment, Task, Interpretability and Rule-based system.
He studied Metaheuristic and Evolutionary computation that intersect with Swarm intelligence.
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.
