T
Tiago Carvalho
Researcher at São Paulo Federal Institute of Education, Science and Technology
Publications - 200
Citations - 2933
Tiago Carvalho is an academic researcher from São Paulo Federal Institute of Education, Science and Technology. The author has contributed to research in topics: Vector field & Piecewise. The author has an hindex of 24, co-authored 188 publications receiving 2198 citations. Previous affiliations of Tiago Carvalho include University of Cambridge & University of Louisiana at Lafayette.
Papers
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Proceedings ArticleDOI
Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network
TL;DR: A malware family classification approach using a deep neural network based on the ResNet-50 architecture that can effectively be used to classify malware families with an accuracy of 98.62% is presented.
Journal ArticleDOI
Going deeper into copy-move forgery detection
TL;DR: This work presents a new approach toward copy-move forgery detection based on multi-scale analysis and voting processes of a digital image and compares the proposed method to 15 others from the literature and reports promising results.
Journal ArticleDOI
Points of Interest and Visual Dictionaries for Automatic Retinal Lesion Detection
TL;DR: The robustness of the visual dictionary against image quality (blurring), resolution, and retinal background, makes it a strong candidate for DR screening of large, diverse communities with varying cameras and settings and levels of expertise for image capture.
Journal ArticleDOI
Exudate detection in fundus images using deeply-learnable features.
Parham Khojasteh,Leandro Aparecido Passos Junior,Tiago Carvalho,Edmar Rezende,Behzad Aliahmad,João Paulo Papa,Dinesh Kumar +6 more
TL;DR: The results show that ResNet-50 with Support Vector Machines outperformed other networks with an accuracy and sensitivity of 98% and 0.99, respectively, which shows that Res net-50 can be used for the analysis of the fundus images to detect exudates.
Journal ArticleDOI
Experimentally based topology models for E. coli inner membrane proteins
Mikaela Rapp,David A. Drew,Daniel O. Daley,Johan Nilsson,Johan Nilsson,Tiago Carvalho,Karin Melén,Jan-Willem de Gier,Gunnar von Heijne +8 more
TL;DR: It is shown that determination of the location of a protein's C terminus, rather than some internal loop, is the best strategy for large‐scale topology mapping studies.