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 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.
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.
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.
Drilling tool geometry evaluation for reinforced composite laminates
Luís Miguel P. Durão;Daniel J.S. Gonçalves;João Manuel R.S. Tavares;João Manuel R.S. Tavares;Victor Hugo C. de Albuquerque.
Composite Structures (2010)
Efficient supervised optimum-path forest classification for large datasets
JoãO P. Papa;Alexandre X. FalcãO;Victor Hugo C. De Albuquerque;JoãO Manuel R. S. Tavares.
Pattern Recognition (2012)
Enabling Technologies for the Internet of Health Things
Joel J. P. C. Rodrigues;Dante Borges De Rezende Segundo;Heres Arantes Junqueira;Murilo Henrique Sabino.
IEEE Access (2018)
A Reference Model for Internet of Things Middleware
Mauro A. A. da Cruz;Joel Jose P. C. Rodrigues;Jalal Al-Muhtadi;Valery V. Korotaev.
IEEE Internet of Things Journal (2018)
Automatic 3D pulmonary nodule detection in CT images
Igor Rafael S. Valente;Paulo César Cortez;Edson Cavalcanti Neto;José Marques Soares.
Computer Methods and Programs in Biomedicine (2016)
Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications
Tiago Carneiro;Raul Victor Medeiros Da Nobrega;Thiago Nepomuceno;Gui-Bin Bian.
IEEE Access (2018)
Optimal feature-based multi-kernel SVM approach for thyroid disease classification
K. Shankar;S. K. Lakshmanaprabu;Deepak Gupta;Andino Maseleno.
The Journal of Supercomputing (2020)
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Vikash Chouhan;Sanjay Kumar Singh;Aditya Khamparia;Deepak Gupta.
Applied Sciences (2020)
ECG arrhythmia classification based on optimum-path forest
Eduardo José Da S. Luz;Thiago M. Nunes;Victor Hugo C. De Albuquerque;JoãO P. Papa.
Expert Systems With Applications (2013)
Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease
Deepak Gupta;Arnav Julka;Sanchit Jain;Tushar Aggarwal.
Cognitive Systems Research (2018)
Profile was last updated on December 6th, 2021.
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