Masaki Aono
About
In The Last Decade
Masaki Aono
116 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 116
- Computer Vision and Pattern Recognition 829
- Artificial Intelligence 483
- Computational Mechanics 465
- Computer Graphics and Computer-Aided Design 450
- Information Systems 191
Countries citing papers authored by Masaki Aono
This map shows the geographic impact of Masaki Aono'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 Masaki Aono with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masaki Aono more than expected).
Fields of papers citing papers by Masaki Aono
This network shows the impact of papers produced by Masaki Aono. 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 Masaki Aono. The network helps show where Masaki Aono may publish in the future.
Co-authorship network of co-authors of Masaki Aono
This figure shows the co-authorship network connecting the top 25 collaborators of Masaki Aono. A scholar is included among the top collaborators of Masaki Aono 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 Masaki Aono. Masaki Aono is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | Kdelab at ImageCLEF 2021: Medical Caption Prediction with Effective Data Pre-processing and Deep Learning. | 4 |
| 4 | Humor Detection in Spanish Tweets Using Neural Network. | 0 |
| 5 | 4 | |
| 6 | kdevqa at VQA-Med 2020: Focusing on GLU-based Classification. | 1 |
| 7 | Leveraging Human Pose Estimation Model for Stroke Classification in Table Tennis. | 2 |
| 8 | Neural Networks and Support Vector Machine based Approach for Classifying Tweets by Information Types at TREC 2018 Incident Streams Task. | 1 |
| 9 | KDEIM at NTCIR-12 IMine-2 Search Intent Mining Task: Query Understanding through Diversified Ranking of Subtopics. | 1 |
| 10 | KDEVIR at ImageCLEF 2014 Scalable Concept Image Annotation Task: Ontology-based Automatic Image Annotation | 2 |
| 11 | SEM12 at the NTCIR-10 INTENT-2 English Subtopic Mining Subtask. | 3 |
| 12 | A large-scale Shape Benchmark for 3D object retrieval: Toyohashi shape benchmark | 33 |
| 13 | 3D shape retrieval from a 2D image as query | 8 |
| 14 | Scalability in ontology instance matching of large semantic knowledge base | 3 |
| 15 | Metric of intrinsic information content for measuring semantic similarity in an ontology | 12 |
| 16 | Anchor-flood: results for OAEI 2009 | 19 |
| 17 | Ontology based Approach to Patent Mining for Relating International Patent Classification (IPC) to a Scientific Abstract. | 0 |
| 18 | Alignment results of anchor-flood algorithm for OAEI-2008 | 13 |
| 19 | Leveraging Category-based LSI for Patent Retrieval | 2 |
| 20 | A Prototype of Content-based Recommendation System based on RSS | 3 |
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.