What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Paulo Cortez mainly investigates Data mining, Artificial neural network, Artificial intelligence, Support vector machine and Data science.
The study incorporates disciplines such as Quality, Resource management, Social media, Microblogging and Receiver operating characteristic in addition to Data mining.
His Artificial neural network research incorporates elements of Traffic engineering and Internet traffic.
His biological study spans a wide range of topics, including Machine learning and Computer vision.
The various areas that he examines in his Support vector machine study include Decision tree, Random forest and Feature selection.
His study in Data science is interdisciplinary in nature, drawing from both Domain, Decision support system, Marketing research and Business intelligence.
His most cited work include:
- Modeling wine preferences by data mining from physicochemical properties (657 citations)
- A Data-Driven Approach to Predict the Success of Bank Telemarketing (306 citations)
- Using data mining to predict secondary school student performance (240 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Artificial neural network, Machine learning, Data mining and Support vector machine.
His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition.
As part of the same scientific family, Paulo Cortez usually focuses on Artificial neural network, concentrating on Time series and intersecting with Estimation of distribution algorithm.
Many of his research projects under Data mining are closely connected to Set with Set, tying the diverse disciplines of science together.
Paulo Cortez combines topics linked to Random forest with his work on Support vector machine.
His Evolutionary algorithm study incorporates themes from Evolutionary computation, Distributed computing and Open Shortest Path First.
He most often published in these fields:
- Artificial intelligence (39.32%)
- Artificial neural network (31.62%)
- Machine learning (26.92%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (39.32%)
- Machine learning (26.92%)
- Artificial neural network (31.62%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Data mining and Set.
His study looks at the relationship between Artificial intelligence and topics such as Natural language processing, which overlap with Classifier.
When carried out as part of a general Machine learning research project, his work on Multilayer perceptron and Regression analysis is frequently linked to work in Scheme and Tear resistance, therefore connecting diverse disciplines of study.
His research integrates issues of Time series, Stability, Distress, Evolutionary algorithm and Algorithm in his study of Artificial neural network.
His Data mining research is multidisciplinary, incorporating elements of Social media, Microblogging, Feature selection and Fibre-reinforced plastic.
His work on Feature relevance as part of general Feature selection research is frequently linked to Computational Science and Engineering, thereby connecting diverse disciplines of science.
Between 2015 and 2021, his most popular works were:
- The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices (122 citations)
- Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis (94 citations)
- Stock market sentiment lexicon acquisition using microblogging data and statistical measures (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Data mining, Information retrieval, Data science, Topic model and Social media.
His research in Data mining intersects with topics in Fibre-reinforced plastic and Feature selection, Artificial intelligence.
His study with Feature selection involves better knowledge in Machine learning.
His study looks at the intersection of Data science and topics like Big data with Representation and Theme.
His study in the field of Microblogging is also linked to topics like Stock market.
His work deals with themes such as Algorithm, Structural engineering, Column and Support vector machine, which intersect with Artificial neural network.
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