What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Classifier and Deep learning.
Raw data is closely connected to Identification in his research, which is encompassed under the umbrella topic of Artificial intelligence.
His Machine learning study incorporates themes from Image, Robustness and Pattern recognition.
His studies in Pattern recognition integrate themes in fields like Supervised learning and Connectivity.
His Classifier research incorporates elements of Contextual image classification, Algorithm, Data mining and Binary number.
In general Deep learning, his work in Boltzmann machine is often linked to Oesophageal adenocarcinoma linking many areas of study.
His most cited work include:
- Supervised pattern classification based on optimum-path forest (269 citations)
- BBA: A Binary Bat Algorithm for Feature Selection (232 citations)
- Efficient supervised optimum-path forest classification for large datasets (186 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Classifier, Pattern recognition and Support vector machine.
Artificial intelligence is frequently linked to Data mining in his study.
His Data mining research is multidisciplinary, incorporating elements of Harmony search and Cluster analysis.
As part of the same scientific family, he usually focuses on Machine learning, concentrating on Metaheuristic and intersecting with Particle swarm optimization.
João Paulo Papa studied Pattern recognition and Biometrics that intersect with Spoofing attack.
His research in Deep learning intersects with topics in Binary image and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (77.54%)
- Machine learning (43.08%)
- Classifier (28.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (77.54%)
- Machine learning (43.08%)
- Deep learning (16.31%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Artificial neural network.
Robustness, Convolutional neural network, Contextual image classification, Feature extraction and Support vector machine are among the areas of Artificial intelligence where João Paulo Papa concentrates his study.
His Machine learning research incorporates themes from Classifier, Task and Range.
His Classifier research includes elements of Handwriting and Medical diagnosis.
João Paulo Papa has researched Pattern recognition in several fields, including Speedup and Identification.
His Artificial neural network research includes themes of Isolation and Risk analysis.
Between 2019 and 2021, his most popular works were:
- Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus (32 citations)
- FEMa: a finite element machine for fast learning (27 citations)
- A metaheuristic-driven approach to fine-tune Deep Boltzmann Machines (9 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, Machine learning, Deep learning, Artificial neural network and Feature extraction.
His research integrates issues of Field and Pattern recognition in his study of Artificial intelligence.
His Machine learning research integrates issues from Classifier, Textual information and Finite element method.
The Classifier study combines topics in areas such as Training set, Basis function, Probabilistic logic, Fuzzy logic and Supervised learning.
His work deals with themes such as F1 score, Binary image, Robustness and Hyperparameter, which intersect with Deep learning.
His Support vector machine study integrates concerns from other disciplines, such as Unsupervised learning, Cluster analysis and Feature vector.
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