Pankaj Chittora

485 citations
3 papers · 254 indexed · 1 hit paper · h-index 3
Topics
earthquake and tectonic studies (1 paper)Artificial Intelligence in Healthcare (1 paper)Imbalanced Data Classification Techniques (1 paper)
Journals
IEEE AccessSustainabilityJournal of Circuits Systems and Computers
Partner nations
IndiaPolandCzechia

In The Last Decade

Pankaj Chittora

3 papers receiving 222 citations

Hit Papers

Prediction of Chronic Kidney Disease - A Machine Learning...2021202620222024202150100150

Peers

Pankaj Chittora
Comparison fields: 5 of 69
  • Health Information Management 139
  • Artificial Intelligence 125
  • Radiology, Nuclear Medicine and Imaging 22
  • Information Systems 20
  • Neurology 19
Replace Md. Ariful Islam with:
Md. Ariful Islam Bangladesh
Homay Danaei Mehr Türkiye
Md. Ziaul Hasan Majumder Bangladesh
Saloni Kumari India
Rajib Kumar Halder Bangladesh
Abrar Yaqoob India
Navneet Kumar Verma India
Farhana Yasmin China
Uma N. Dulhare India
S. Revathy India
Pankaj Chittora relative to Md. Ariful Islam Bangladesh Md. Ariful Islam's profile →
Citations per field
00.5×1.5×2.1×
Md. Ariful Islam · 1×
Citations per year

Countries citing papers authored by Pankaj Chittora

Since Specialization
Citations

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

Fields of papers citing papers by Pankaj Chittora

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pankaj Chittora

This figure shows the co-authorship network connecting the top 25 collaborators of Pankaj Chittora. A scholar is included among the top collaborators of Pankaj Chittora 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 Pankaj Chittora. Pankaj Chittora is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

3 of 3 papers shown
#WorkIndexed citations
1 38
2 34
3
Prediction of Chronic Kidney Disease - A Machine Learning Perspectivebreakdown →
182

About Pankaj Chittora

Pankaj Chittora is a scholar working on Health Information Management, Obstetrics and Gynecology and Artificial Intelligence, having authored 3 papers that have together received 254 indexed citations. Recurring topics across this work include earthquake and tectonic studies (1 paper), Artificial Intelligence in Healthcare (1 paper) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Health Information Management (139 citations), Health Informatics (6 citations) and Artificial Intelligence (125 citations). Pankaj Chittora has collaborated with scholars based in India, Poland and Czechia. Frequent co-authors include Tulika Chakrabarti, Prąsun Chakrabarti, Michał Jasiński, Zbigniew Leonowicz, Elżbieta Jasińska, Radomír Goňo, Vadim Bolshev, Sandeep Chaurasia, Santosh Kumar Vishwakarma and Jerry Chun‐Wei Lin. Their work appears in journals such as IEEE Access, Sustainability and Journal of Circuits Systems and Computers.

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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