Ling Ma

902 citations
45 papers · 558 · 1 hit paper · h-index 11

Impact in

  • Accounting top 5%
    • Financial Distress and Bankruptcy Prediction
    • Auditing, Earnings Management, Governance
  • Finance top 10%
    • Credit Risk and Financial Regulations

Papers in

Ling Ma

42 papers receiving 547 citations

Ling Ma's Hit Papers

Deep learning models for bankruptcy prediction using textual disclosures 2018 · 285 citations
2850+2+5Years since publication50100150200250

Peers

Ling Ma
Comparison fields: 5 of 93
  • Accounting 207
  • Finance 80
  • Otorhinolaryngology 27
  • Management Science and Operations Research 76
  • Media Technology 48
Replace Lin Ma with:
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Ling Ma relative to Lin Ma United States Lin Ma's profile →
Citations per field
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Citations per year

Countries citing papers authored by Ling Ma

Since Specialization
Citations

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

Fields of papers citing papers by Ling Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep learning models for bankruptcy prediction using textual disclosures
Hit paper breakdown →
2018285
2 202141
3 200419
4 202118
5 202016
6 202215
7 202215
8 202114
9 201913
10 202411
11 202010
12 20218
13 20238
14 20207
15 20106
16 20166
17
The effect of intensity-modulated radiotherapy versus conventional radiotherapy on quality of life in patients with nasopharyngeal cancer: a cross-sectional study.
20135
18 20185
19 20225
20 20225

About Ling Ma

Ling Ma is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Molecular Biology and Media Technology, having authored 45 papers that have together received 558 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (8 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Industrial Vision Systems and Defect Detection (6 papers), COVID-19 diagnosis using AI (6 papers), Advanced Image Fusion Techniques (4 papers), Multiple Myeloma Research and Treatments (4 papers), AI in cancer detection (4 papers) and Image and Object Detection Techniques (3 papers). The work is most often cited by research in Accounting (207 citations), Finance (80 citations), Otorhinolaryngology (27 citations), Management Science and Operations Research (76 citations) and Media Technology (48 citations). Ling Ma has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Chihoon Lee, Feng Mai, Shaonan Tian, Huiqin Jiang, Lihua Jian, Rakiba Rayhana, Zheng Liu, Yueqi Sun, Jianbo Shi and Rui Xu. Their work appears in journals such as IEEE Access, Amyloid, PeerJ Computer Science, BMC Pulmonary Medicine and International Immunopharmacology.

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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