Marcelo Keese Albertini

488 total citations
31 papers, 303 citations indexed

About

Marcelo Keese Albertini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Marcelo Keese Albertini has authored 31 papers receiving a total of 303 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 7 papers in Media Technology. Recurrent topics in Marcelo Keese Albertini's work include Advanced Clustering Algorithms Research (5 papers), Advanced Image Processing Techniques (5 papers) and Data Stream Mining Techniques (4 papers). Marcelo Keese Albertini is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Advanced Image Processing Techniques (5 papers) and Data Stream Mining Techniques (4 papers). Marcelo Keese Albertini collaborates with scholars based in Brazil, China and South Korea. Marcelo Keese Albertini's co-authors include Rodrigo Fernandes de Mello, Ricardo Batista, Louise Bouchard, Gwanggil Jeon, Xiaomin Yang, Geraldo Caixeta Guimarães, Turgay Çelik, Henrique Fernandes, André Ricardo Backes and Geórgia das Graças Pena and has published in prestigious journals such as Social Science & Medicine, IEEE Access and Information Sciences.

In The Last Decade

Marcelo Keese Albertini

29 papers receiving 294 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marcelo Keese Albertini Brazil 10 88 77 55 39 34 31 303
Shereen Afifi Egypt 8 71 0.8× 81 1.1× 11 0.2× 36 0.9× 36 1.1× 36 320
Marzia Hoque Tania United Kingdom 9 66 0.8× 31 0.4× 16 0.3× 11 0.3× 16 0.5× 26 297
Saman Iftikhar Pakistan 11 98 1.1× 105 1.4× 11 0.2× 29 0.7× 8 0.2× 36 324
Ming He China 8 71 0.8× 82 1.1× 6 0.1× 66 1.7× 18 0.5× 34 412
Quan Tang China 10 101 1.1× 74 1.0× 10 0.2× 36 0.9× 14 0.4× 31 346
Zhiyuan Chen China 6 92 1.0× 42 0.5× 6 0.1× 14 0.4× 7 0.2× 11 244
Jiaxin Li China 9 83 0.9× 46 0.6× 11 0.2× 12 0.3× 11 0.3× 37 297
Mohamed Yaseen Jabarulla South Korea 10 86 1.0× 103 1.3× 30 0.5× 14 0.4× 3 0.1× 18 365
Guangwen Liu China 10 28 0.3× 35 0.5× 8 0.1× 38 1.0× 29 0.9× 62 296
Ilias Kalamaras Greece 9 42 0.5× 73 0.9× 6 0.1× 34 0.9× 60 1.8× 28 345

Countries citing papers authored by Marcelo Keese Albertini

Since Specialization
Citations

This map shows the geographic impact of Marcelo Keese Albertini'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 Marcelo Keese Albertini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcelo Keese Albertini more than expected).

Fields of papers citing papers by Marcelo Keese Albertini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marcelo Keese Albertini. 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 Marcelo Keese Albertini. The network helps show where Marcelo Keese Albertini may publish in the future.

Co-authorship network of co-authors of Marcelo Keese Albertini

This figure shows the co-authorship network connecting the top 25 collaborators of Marcelo Keese Albertini. A scholar is included among the top collaborators of Marcelo Keese Albertini 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 Marcelo Keese Albertini. Marcelo Keese Albertini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Albertini, Marcelo Keese, et al.. (2023). Machine learning for hospital readmission prediction in pediatric population. Computer Methods and Programs in Biomedicine. 244. 107980–107980. 12 indexed citations
2.
Albertini, Marcelo Keese, et al.. (2023). Validation of the HOSPITAL score as predictor of 30-day potentially avoidable readmissions in pediatric hospitalized population: retrospective cohort study. European Journal of Pediatrics. 182(4). 1579–1585. 1 indexed citations
3.
Albertini, Marcelo Keese, et al.. (2022). Practical Evaluation of Lyndon Factors via Alphabet Reordering. Mathematics. 11(1). 139–139. 1 indexed citations
4.
Jeon, Gwanggil, Marcelo Keese Albertini, Valerio Bellandi, & Abdellah Chehri. (2022). Intelligent mobile edge computing for IoT big data. Complex & Intelligent Systems. 8(5). 3595–3601. 5 indexed citations
5.
Albertini, Marcelo Keese, et al.. (2021). A dynamic data structure for temporal reachability with unsorted contact insertions. Social Network Analysis and Mining. 12(1). 1 indexed citations
6.
Son, Chang‐Hwan, et al.. (2020). Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble Gradient Boosting in Estimating Respiratory Rate. IEEE Access. 8. 125648–125658. 5 indexed citations
7.
Albertini, Marcelo Keese, et al.. (2020). Wild boar recognition using convolutional neural networks. Concurrency and Computation Practice and Experience. 33(22). 5 indexed citations
8.
Yang, Xiaomin, et al.. (2020). Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter. Entropy. 22(1). 118–118. 24 indexed citations
9.
Albertini, Marcelo Keese, et al.. (2020). Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images. Applied Optics. 59(17). E23–E23. 30 indexed citations
10.
Yang, Xiaomin, et al.. (2020). LMSN:a lightweight multi-scale network for single image super-resolution. Multimedia Systems. 27(4). 845–856. 4 indexed citations
11.
Yu, Wen, et al.. (2020). Multifocus image fusion using convolutional neural network. Multimedia Tools and Applications. 79(45-46). 34531–34543. 13 indexed citations
12.
Chen, Lihui, et al.. (2020). Medical image super-resolution with laplacian dense network. Multimedia Tools and Applications. 81(3). 3131–3144. 3 indexed citations
13.
Albertini, Marcelo Keese, et al.. (2019). A study of publication trajectories of the Brazilian Computer Science community. Anais da Academia Brasileira de Ciências. 91(3). e20180559–e20180559.
14.
Albertini, Marcelo Keese, et al.. (2017). Using Multiple Clustering Algorithms to Generate Constraint Rules and Create Consensus Clusters. 3. 312–317. 3 indexed citations
15.
Albertini, Marcelo Keese & Rodrigo Fernandes de Mello. (2017). Estimating data stream tendencies to adapt clustering parameters. International Journal of High Performance Computing and Networking. 11(1). 34–34. 3 indexed citations
16.
Guimarães, Geraldo Caixeta, et al.. (2016). Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter. Electric Power Systems Research. 143. 643–656. 49 indexed citations
17.
Bouchard, Louise, et al.. (2015). Research on health inequalities: A bibliometric analysis (1966–2014). Social Science & Medicine. 141. 100–108. 62 indexed citations
18.
Albertini, Marcelo Keese, et al.. (2015). Min-heap-based scheduling algorithm: an approximation algorithm for homogeneous and heterogeneous distributed systems. International Journal of Parallel Emergent and Distributed Systems. 31(1). 64–84. 1 indexed citations
19.
Albertini, Marcelo Keese & Rodrigo Fernandes de Mello. (2013). Data stream dynamic clustering supported by Markov chain isomorphisms. Intelligent Data Analysis. 17(3). 439–457. 1 indexed citations
20.
Albertini, Marcelo Keese & Rodrigo Fernandes de Mello. (2007). A self-organizing neural network for detecting novelties. 462–466. 23 indexed citations

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.

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