Inay Ha

468 citations
15 papers · 299 · h-index 6

Impact in

Papers in

Inay Ha

12 papers receiving 282 citations

Peers

Inay Ha
Comparison fields: 5 of 47
  • Information Systems 231
  • Computational Mathematics 6
  • Marketing 43
  • Computer Vision and Pattern Recognition 90
  • Transportation 21
Replace Cosimo Palmisano with:
Cosimo Palmisano Italy
Rahul Pandey United States
Marcos Aurélio Domingues Brazil
Sheetal Girase India
Marcelo Garcia Manzato Brazil
Simon Dooms Belgium
Inma Garcia Spain
Sunita Barve India
Kenneth Wai-Ting Leung Hong Kong
Xiaoyi Zeng China
Inay Ha relative to Cosimo Palmisano Italy Cosimo Palmisano's profile →
Citations per field
00.5×1.5×2.4×
Cosimo Palmisano · 1×
Citations per year

Countries citing papers authored by Inay Ha

Since Specialization
Citations

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

Fields of papers citing papers by Inay Ha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Inay Ha. 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 Inay Ha. The network helps show where Inay Ha may publish in the future.

Co-authors

The 6 scholars most cited alongside Inay Ha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Inay Ha Line = papers co-authored together Inay Ha links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2009170
2 201150
3 201423
4
A unified framework for augmented reality and knowledge-based systems in maintaining aircraft
201419
5 201312
6 20148
7 20085
8 20135
9 20114
10
Collaborative Filtering based on Clustering method using Genre and Interest in SNS
20121
11
Collaborative Filtering Method based on User’s Behavior in Social Network
20121
12 20131
13
Discovering Association Rules using Item Clustering on Frequent Pattern Network
20080
14
Collaborative Recommendation of Online Video Lectures in e-Learning System
20090
15 20110

About Inay Ha

Inay Ha is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence, Sociology and Political Science and Marketing, having authored 15 papers that have together received 299 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Digital Marketing and Social Media (3 papers), Semantic Web and Ontologies (2 papers), Image and Video Quality Assessment (2 papers), Advanced Computational Techniques and Applications (2 papers), Augmented Reality Applications (2 papers), Image Retrieval and Classification Techniques (2 papers) and Human Mobility and Location-Based Analysis (2 papers). The work is most often cited by research in Information Systems (231 citations), Computational Mathematics (6 citations), Marketing (43 citations), Computer Vision and Pattern Recognition (90 citations) and Transportation (21 citations). Inay Ha has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Geun‐Sik Jo, Heung-Nam Kim, Abdulmotaleb El-Saddik, Kyungyong Chung, Ulrich Neumann and Suya You. Their work appears in journals such as Multimedia Tools and Applications, Decision Support Systems, Electronic Commerce Research and Applications, Cluster Computing and Journal of the Korea Society of Computer and Information.

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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