Daniel D’souza

484 citations
3 papers · 48 · h-index 3

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Natural Language Processing Techniques
    • Topic Modeling
    • Machine Learning and Data Classification
    • Adversarial Robustness in Machine Learning
    • Domain Adaptation and Few-Shot Learning

Papers in

    • Topic Modeling 2
    • Sentiment Analysis and Opinion Mining 1
    • Advanced Text Analysis Techniques 1
    • Natural Language Processing Techniques 1
    • Anomaly Detection Techniques and Applications 1
    • Machine Learning and Data Classification 1
    • Adversarial Robustness in Machine Learning 1

Daniel D’souza

3 papers receiving 46 citations

Peers

Daniel D’souza
Comparison fields: 5 of 33
  • Health Informatics 3
  • Artificial Intelligence 32
  • Computer Vision and Pattern Recognition 11
  • Family Practice 1
  • Health Information Management 1
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Nikola Momchev United States
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Sungsoo Ahn South Korea
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Ahmed Hamdi France
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Citations per field
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Citations per year

Countries citing papers authored by Daniel D’souza

Since Specialization
Citations

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

Fields of papers citing papers by Daniel D’souza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel D’souza. 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 Daniel D’souza. The network helps show where Daniel D’souza may publish in the future.

Co-authors

The 12 scholars most cited alongside Daniel D’souza, 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 Daniel D’souza Line = papers co-authored together Daniel D’souza links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1 202226
2 202417
3 20225

About Daniel D’souza

Daniel D’souza is a scholar working on Artificial Intelligence, Infectious Diseases, Organic Chemistry, Surgery and Communication, having authored 3 papers that have together received 48 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Sentiment Analysis and Opinion Mining (1 paper), Advanced Text Analysis Techniques (1 paper), Natural Language Processing Techniques (1 paper), Anomaly Detection Techniques and Applications (1 paper), Machine Learning and Data Classification (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (32 citations), Computer Vision and Pattern Recognition (11 citations), Family Practice (1 citation) and Health Information Management (1 citation). Daniel D’souza has collaborated with scholars based in United States, India and Philippines. Frequent co-authors include Sara Hooker, Chirag Agarwal, Anwesh Reddy Paduri, Zheng Yong, Wei-Yin Ko, Julia Kreutzer, Shayne Longpre, Narayana Darapaneni, Ahmet Üstün and Marzieh Fadaee. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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