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João Gonçalves holds a BS/MS in Electrical Engineering from IPB (2011) and specializes in image processing, clustering, pattern recognition, deep learning, machine vision, and hardware/software development for engineering and science applications. With experience in real-world visual inspection and prototype development under variable lighting conditions, he has published research on Machine Learning and Computer Vision and is currently working on certification and privacy-preserving techniques for AI systems.
Identificação

Identificação pessoal

Nome completo
João Nuno Castro Gonçalves

Nomes de citação

  • Gonçalves, João

Identificadores de autor

Ciência ID
9312-65AE-DF6D
ORCID iD
0000-0002-8341-8167

Idiomas

Idioma Conversação Leitura Escrita Compreensão Peer-review
Português (Idioma materno)
Formação
Grau Classificação
2010/12
Concluído
 Engenharia Industrial (Mestrado integrado)
Instituto Politécnico de Bragança Escola Superior de Tecnologia e Gestão, Portugal
2008/06
Concluído
 Engenharia Electrotécnica (Licenciatura)
Instituto Politécnico de Bragança Escola Superior de Tecnologia e Gestão, Portugal
Produções

Publicações

Artigo em conferência
  1. "Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy". 2019.
    10.5220/0007580904810491
  2. "Mobile-based Risk Assessment of Diabetic Retinopathy using a Smartphone and Adapted Ophtalmoscope". 2018.
    10.5220/0006599701680175
  3. "Supervised learning for Out-of-Stock detection in panoramas of retail shelves". 2016.
    10.1109/ist.2016.7738260
Artigo em revista
  1. Rafaela Carvalho; Ana C. Morgado; João Gonçalves; Anil Kumar; Alberto Gil e Sá Rolo; Rui Carreira; Filipe Soares. "Computer-Aided Visual Inspection of Glass-Coated Tableware Ceramics for Multi-Class Defect Detection". Applied Sciences (2023): https://doi.org/10.3390/app132111708.
    10.3390/app132111708
  2. João Gonçalves; Eduardo Silva; Pedro Faria; Telmo Nogueira; Ana Ferreira; Cristina Carlos; Luís Rosado. "Edge-Compatible Deep Learning Models for Detection of Pest Outbreaks in Viticulture". Agronomy (2022): https://doi.org/10.3390/agronomy12123052.
    10.3390/agronomy12123052
  3. Luís Rosado; Pedro Faria; João Gonçalves; Eduardo Silva; Ana Vasconcelos; Cristiana Braga; João Oliveira; et al. "EyesOnTraps: AI-Powered Mobile-Based Solution for Pest Monitoring in Viticulture". Sustainability (2022): https://doi.org/10.3390/su14159729.
    10.3390/su14159729
  4. Ricardo Leonardo; Joao Goncalves; Andre Carreiro; Beatriz Simoes; Tiago Oliveira; Filipe Soares. "Impact of Generative Modeling for Fundus Image Augmentation With Improved and Degraded Quality in the Classification of Glaucoma". IEEE Access (2022): https://doi.org/10.1109/ACCESS.2022.3215126.
    10.1109/ACCESS.2022.3215126
  5. Antonio Oliveira-Jr; Carlos Resende; André Pereira; Pedro Madureira; joao goncalves; Ruben Moutinho; Filipe Soares; Waldir Moreira. "IoT Sensing Platform as a Driver for Digital Farming in Rural Africa". Sensors (2020): https://www.mdpi.com/1424-8220/20/12/3511.
    10.3390/s20123511
Capítulo de livro
  1. Antonio Oliveira-Jr; Carlos Resende; André Pereira; Pedro Madureira; João Gonçalves; Ruben Moutinho; Filipe Soares; Waldir Moreira. "IoT Sensing Box to Support Small-Scale Farming in Africa". 171-184. Springer International Publishing, 2021.
    10.1007/978-3-030-70572-5_11
  2. "A New Compact Optical System Proposal and Image Quality Comparison Against Other Affordable Non-mydriatic Fundus Cameras". 26-48. Springer International Publishing, 2019.
    10.1007/978-3-030-29196-9_2