What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Victor Hugo C. de Albuquerque mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Feature extraction.
His Artificial intelligence study incorporates themes from Medical diagnosis and Computer vision.
His research in Machine learning intersects with topics in Classifier and Intrusion detection system.
In his study, Gait is inextricably linked to Identification, which falls within the broad field of Pattern recognition.
His work on Backpropagation as part of his general Artificial neural network study is frequently connected to Pneumonia, thereby bridging the divide between different branches of science.
His Feature extraction study integrates concerns from other disciplines, such as Feature and Kernel.
His most cited work include:
- Efficient supervised optimum-path forest classification for large datasets (186 citations)
- Drilling tool geometry evaluation for reinforced composite laminates (173 citations)
- Enabling Technologies for the Internet of Health Things (125 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Support vector machine and Feature extraction.
Victor Hugo C. de Albuquerque regularly links together related areas like Computer vision in his Artificial intelligence studies.
His study connects Artificial neural network and Pattern recognition.
In his papers, he integrates diverse fields, such as Machine learning and Context.
His Support vector machine research includes elements of Classifier, Random forest and Multilayer perceptron.
Much of his study explores Feature extraction relationship to Data mining.
He most often published in these fields:
- Artificial intelligence (47.42%)
- Pattern recognition (20.62%)
- Machine learning (14.43%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (47.42%)
- Pattern recognition (20.62%)
- Deep learning (9.28%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Convolutional neural network.
His study in Support vector machine, Feature, Segmentation, Naive Bayes classifier and Feature extraction is done as part of Artificial intelligence.
His Pattern recognition research includes themes of Artificial neural network, Process, Medical diagnosis and Electroencephalography.
His Deep learning research incorporates elements of Image segmentation, The Internet, Robustness and Computer vision.
His work carried out in the field of Machine learning brings together such families of science as Semantics and Set.
Victor Hugo C. de Albuquerque has included themes like Transfer of learning and Smoke in his Convolutional neural network study.
Between 2019 and 2021, his most popular works were:
- A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images (113 citations)
- Optimal feature-based multi-kernel SVM approach for thyroid disease classification (74 citations)
- A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Victor Hugo C. de Albuquerque mainly focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Feature extraction and Deep learning.
His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Classifier.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Field, Feature and Robustness.
His biological study spans a wide range of topics, including Real-time computing and Benchmark.
His Feature extraction research incorporates elements of Key, Data mining and Automatic summarization.
The study incorporates disciplines such as Computer-aided diagnosis, Enhanced Data Rates for GSM Evolution and Server in addition to Deep learning.
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.
