Jianhua Dai

7.1k citations
243 papers · 5.3k indexed · 1 hit paper · h-index 42

Jianhua Dai

220 papers receiving 5.2k citations

Hit Papers

Attribute selection based on information gain ratio in fu...283201220262016202150100150200250

Peers

Jianhua Dai
Comparison fields: 5 of 146
  • Computational Theory and Mathematics 2.6k
  • Management Science and Operations Research 1.1k
  • Artificial Intelligence 2.5k
  • Information Systems 1.4k
  • Computer Vision and Pattern Recognition 920
Replace Duoqian Miao with:
Duoqian Miao China
Chris Cornelis Belgium
William Zhu China
Thierry Denœux France
Joaquín Derrac Spain
Shangce Gao Japan
Deyu Li China
Manuel Lozano Spain
Peilin Zhao China
Jianhua Dai relative to Duoqian Miao China Duoqian Miao's profile →
Citations per field
00.5×1.5×
Duoqian Miao · 1×
Citations per year

Countries citing papers authored by Jianhua Dai

Since Specialization
Citations

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

Fields of papers citing papers by Jianhua Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jianhua Dai, 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 Jianhua Dai Line = papers co-authored together Jianhua Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
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MicroRNA-182 Promotes Cell Migration by Targeting Programmed Cell Death 4 in Hepatocellular Carcinoma Cells
20201
17
Adherence to Hepatocellular Carcinoma Surveillance and Perceived Barriers Among High-Risk Chronic Liver Disease Patients in Yunnan, China
20201
18
TERT copy gain predicts the outcome of high-dose interferon α-2b therapy in acral melanoma
20180
19
Identification of critically carcinogenesis-related genes in basal cell carcinoma
20180
20
Judgement of satisfying consistency on fuzzy judgement matrix
20061

About Jianhua Dai

Jianhua Dai is a scholar working on Computational Theory and Mathematics, Management Science and Operations Research and Artificial Intelligence, having authored 243 papers that have together received 5.3k indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (105 papers), Data Mining Algorithms and Applications (57 papers), Multi-Criteria Decision Making (49 papers), Neural Networks and Applications (34 papers), Text and Document Classification Technologies (24 papers), Robotic Mechanisms and Dynamics (21 papers), Image Retrieval and Classification Techniques (16 papers) and Fuzzy Logic and Control Systems (12 papers). The work is most often cited by research in Computational Theory and Mathematics (2.6k citations), Management Science and Operations Research (1.1k citations) and Artificial Intelligence (2.5k citations). Jianhua Dai has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Qing Xu, Lin Xiao, Lei Jia, Wentao Wang, Jiaolong Chen, Hu Hu, Kai Zhang, Xiaohong Zhang, Qinghua Hu and Qiu Xiao. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioresource Technology and Gut.

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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