What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Workflow, Workflow management system, Software engineering, Workflow technology and Workflow engine.
Jacques Wainer interconnects Role-based access control, Computer security and Access control in the investigation of issues within Workflow.
His work in the fields of Workflow management system, such as XPDL, intersects with other areas such as Constraint.
As part of the same scientific family, Jacques Wainer usually focuses on XPDL, concentrating on Document management system and intersecting with Artificial intelligence.
His work in the fields of Feature extraction overlaps with other areas such as Automation.
His Software engineering research is multidisciplinary, incorporating perspectives in Collaborative software and Event-driven process chain.
His most cited work include:
- A conceptual model of groupware (194 citations)
- Groupware and computer supported cooperative work (190 citations)
- Automatic fruit and vegetable classification from images (166 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Workflow, Machine learning, Pattern recognition and Software engineering.
His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Natural language processing.
His biological study spans a wide range of topics, including Distributed computing and Knowledge management.
His research combines Feature extraction and Machine learning.
His study connects XPDL and Software engineering.
The study of Workflow management system is intertwined with the study of Workflow engine in a number of ways.
He most often published in these fields:
- Artificial intelligence (33.33%)
- Workflow (14.81%)
- Machine learning (12.35%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (33.33%)
- Pattern recognition (9.88%)
- Machine learning (12.35%)
In recent papers he was focusing on the following fields of study:
Jacques Wainer mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Support vector machine and Diabetic retinopathy.
His research on Artificial intelligence frequently connects to adjacent areas such as Coding.
His study in the field of Classifier is also linked to topics like Breast density, Kappa score and Balanced histogram thresholding.
His Machine learning research incorporates elements of Feature extraction and Face Presentation.
His Feature extraction research includes themes of Liveness, Software and Feature.
His Support vector machine research integrates issues from Gradient boosting, Random forest, Open set and Feature vector.
Between 2013 and 2021, his most popular works were:
- A competition on generalized software-based face presentation attack detection in mobile scenarios (84 citations)
- Advancing bag-of-visual-words representations for lesion classification in retinal images. (43 citations)
- Beyond Lesion-based Diabetic Retinopathy: a Direct Approach for Referral (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Support vector machine and Diabetic retinopathy.
His Artificial intelligence study frequently intersects with other fields, such as Logistic regression.
His Machine learning research includes elements of Feature extraction, Software and Linear discriminant analysis.
His work in the fields of Pattern recognition, such as Classifier and Statistical classification, overlaps with other areas such as Elastic net regularization.
The study incorporates disciplines such as Binary data, Data mining and Test set in addition to Classifier.
His research integrates issues of Cross-validation, Artificial neural network, Learning vector quantization, Model selection and Hyperparameter in his study of Support vector machine.
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