Prospero C. Naval

1.7k total citations · 1 hit paper
46 papers, 1.1k citations indexed

About

Prospero C. Naval is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Prospero C. Naval has authored 46 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 5 papers in Molecular Biology. Recurrent topics in Prospero C. Naval's work include Digital Imaging for Blood Diseases (4 papers), Reinforcement Learning in Robotics (4 papers) and Protein Structure and Dynamics (3 papers). Prospero C. Naval is often cited by papers focused on Digital Imaging for Blood Diseases (4 papers), Reinforcement Learning in Robotics (4 papers) and Protein Structure and Dynamics (3 papers). Prospero C. Naval collaborates with scholars based in Philippines, Germany and France. Prospero C. Naval's co-authors include Eduardo Mendoza, Christoph Küper, Orland Gonzalez, Kirsten Jung, David Earl Hostallero, Pilarita T. Rivera, Laura David, Elena A. Villacorte, Helen Yap and Maricor Soriano and has published in prestigious journals such as Bioinformatics, Conservation Biology and Agriculture Ecosystems & Environment.

In The Last Decade

Prospero C. Naval

43 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

An effective use of crowding distance in multiobjective particle swarm optimization

2005 476 citations Prospero C. Naval et al. profile →

Peers

Prospero C. Naval

AI

CTM

CVPR

MB

WST

Olga Valenzuela Spain

Osama Ahmad Alomari Jordan

Weizheng Zhang China

Waqas Haider Bangyal Pakistan

Malik Braik Jordan

Mohammed Alswaitti Malaysia

Özgür Yeniay Türkiye

Amel Ali Alhussan Saudi Arabia

Wuquan Deng China

Olga Valenzuela Spain

Prospero C. Naval

462 ×1.3

AI

46 ×0.2

CTM

163 ×1.0

CVPR

133 ×0.8

MB

33 ×0.3

WST

Citations per year, relative to Prospero C. Naval Prospero C. Naval (= 1×) peers Olga Valenzuela

Countries citing papers authored by Prospero C. Naval

Since Specialization

Citations

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

Fields of papers citing papers by Prospero C. Naval

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prospero C. Naval. 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 Prospero C. Naval. The network helps show where Prospero C. Naval may publish in the future.

Co-authorship network of co-authors of Prospero C. Naval

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

All Works

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20 of 20 papers shown

1.

Naval, Prospero C., et al.. (2024). Beyond Canonical Fine-tuning: Leveraging Hybrid Multi-Layer Pooled Representations of BERT for Automated Essay Scoring. 2285–2295.

2.

Naval, Prospero C., et al.. (2024). 1D-CNN Model for Fast Assessment of Beef Freshness from Noisy Electronic Nose Signal. 67–71. 1 indexed citations

3.

Naval, Prospero C., et al.. (2024). beats2beats: Activity-Driven Music Playlist Recommender System Using Wearable Sensor Data. 57. 1–6. 1 indexed citations

4.

Naval, Prospero C., et al.. (2022). Towards a Feasible Hand Gesture Recognition System as Sterile Non-contact Interface in the Operating Room with 3D Convolutional Neural Network. Informatica. 46(1). 2 indexed citations

5.

Naval, Prospero C., et al.. (2022). Automatic segmentation of nasopharyngeal carcinoma on CT images using efficient UNet‐2.5D ensemble with semi‐supervised pretext task pretraining. Frontiers in Oncology. 12. 980312–980312. 4 indexed citations

6.

Naval, Prospero C., et al.. (2021). CLRGaze: Contrastive Learning of Representations for Eye Movement Signals. 2021 29th European Signal Processing Conference (EUSIPCO). 1241–1245. 7 indexed citations

7.

Naval, Prospero C., et al.. (2020). A3C-GS: Adaptive Moment Gradient Sharing With Locks for Asynchronous Actor–Critic Agents. IEEE Transactions on Neural Networks and Learning Systems. 32(3). 1162–1176. 21 indexed citations

8.

Naval, Prospero C., et al.. (2020). A Semi-Supervised Approach to Monocular Depth Estimation, Depth Refinement, and Semantic Segmentation of Driving Scenes using a Siamese Triple Decoder Architecture. Informatica. 44(4). 3 indexed citations

9.

Naval, Prospero C., et al.. (2020). Behavioral Phenotyping for Autism Spectrum Disorder Biomarkers Using Computer Vision. Journal of Image and Graphics. 47–52. 9 indexed citations

10.

Naval, Prospero C., et al.. (2019). TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology Images using Local Texture, Appearance, and Gradient Features. 155–159. 1 indexed citations

11.

Naval, Prospero C., et al.. (2019). Air Drums: Playing Drums Using Computer Vision. 1–6. 8 indexed citations

12.

Naval, Prospero C., et al.. (2019). Predicting Protein-Protein Interactions based on Biological Information using Extreme Gradient Boosting. 1–6. 9 indexed citations

13.

Naval, Prospero C., et al.. (2018). Alphabet Sign Language Image Classification Using Deep Learning. 646–650. 27 indexed citations

14.

Rivera, Pilarita T., et al.. (2017). Malaria parasite detection and species identification on thin blood smears using a convolutional neural network. 1–6. 18 indexed citations

15.

Rivera, Pilarita T., et al.. (2017). Malaria Parasite Detection and Species Identification on Thin Blood Smears Using a Convolutional Neural Network. 1–6. 21 indexed citations

16.

David, Laura, et al.. (2013). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. 1–6. 45 indexed citations

17.

Naval, Prospero C., et al.. (2012). Coral reef mosaicking using Teardrop and Fast Image Labeling. 1–6. 3 indexed citations

18.

Naval, Prospero C.. (2012). Dispersal, queueing and navigation of swarm robots using color and geometric constraints. 1–5.

19.

Batista-Navarro, Riza, et al.. (2010). ESP: An expert system for poisoning diagnosis and management. Informatics for Health and Social Care. 35(2). 53–63. 9 indexed citations

20.

Naval, Prospero C., et al.. (2008). Design of stock trading system for historical market data using multiobjective particle swarm optimization of technical indicators. 1871–1878. 15 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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