Priscila M. V. Lima
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Electrical and Electronic Engineering
- Information Systems
- Co-authors
- Felipe M. G. FrançaMassimo De GregorioCarlos E. PedreiraValmir C. BarbosaAna AguiarInês DutraMaurício BreternitzLizy K. John
- Topics
- Neural Networks and Applications (15 papers)Advanced Neural Network Applications (8 papers)Machine Learning and ELM (6 papers)
- Partner nations
- BrazilPortugalUnited States
In The Last Decade
Priscila M. V. Lima
51 papers receiving 359 citations
Peers
Comparison fields: 5 of 72
- Artificial Intelligence 244
- Computer Vision and Pattern Recognition 80
- Computer Networks and Communications 60
- Electrical and Electronic Engineering 47
- Information Systems 42
Countries citing papers authored by Priscila M. V. Lima
This map shows the geographic impact of Priscila M. V. Lima's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Priscila M. V. Lima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Priscila M. V. Lima more than expected).
Fields of papers citing papers by Priscila M. V. Lima
This network shows the impact of papers produced by Priscila M. V. Lima. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Priscila M. V. Lima. The network helps show where Priscila M. V. Lima may publish in the future.
Co-authorship network of co-authors of Priscila M. V. Lima
This figure shows the co-authorship network connecting the top 25 collaborators of Priscila M. V. Lima. A scholar is included among the top collaborators of Priscila M. V. Lima based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Priscila M. V. Lima. Priscila M. V. Lima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | LCAD - UFES at FakeDeS 2021: Fake News Detection Using Named Entity Recognition and Part-of-Speech Sequences. | 2 |
| 5 | Detection of elementary particles with the WiSARD n-tuple classifier. | 2 |
| 6 | Fast Deep Neural Networks Convergence using a Weightless Neural Model. | 1 |
| 7 | 29 | |
| 8 | Near-optimal facial emotion classification using a WiSARD-based weightless system. | 4 |
| 9 | 5 | |
| 10 | Q-SATyrus: Mapping Neuro-symbolic Reasoning into an Adiabatic Quantum Computer. | 1 |
| 11 | Semi-Supervised Classification of Social Textual Data Using WiSARD. | 5 |
| 12 | 35 | |
| 13 | Advances on Weightless Neural Systems. | 9 |
| 14 | A Method for Verifying the Consistency of Business Rules Using Alloy. | 2 |
| 15 | WIPS: the WiSARD Indoor Positioning System | 5 |
| 16 | B-bleaching: Agile Overtraining Avoidance in the WiSARD Weightless Neural Classifier. | 15 |
| 17 | 4 | |
| 18 | Clustering data streams with weightless neural networks | 8 |
| 19 | 1 | |
| 20 | Uma Abordagem para a Transformação Automática do Modelo de Negócio em Modelo de Requisitos. | 8 |
About Priscila M. V. Lima
Priscila M. V. Lima is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Software, having authored 57 papers that have together received 378 indexed citations. Recurring topics across this work include Neural Networks and Applications (15 papers), Advanced Neural Network Applications (8 papers) and Machine Learning and ELM (6 papers). The work is most often cited by research in Artificial Intelligence (244 citations), Computer Vision and Pattern Recognition (80 citations) and Hardware and Architecture (24 citations). Priscila M. V. Lima has collaborated with scholars based in Brazil, Portugal and United States. Frequent co-authors include Felipe M. G. França, Massimo De Gregorio, Carlos E. Pedreira, Valmir C. Barbosa, Ana Aguiar, Inês Dutra, Maurício Breternitz, Lizy K. John, Daniel Sadoc Menasché and Jonice Oliveira. Their work appears in journals such as Neural Computation, Neurocomputing and Neural Networks.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.