Mohammadali Kargar

417 citations
7 papers · 163 · 1 hit paper · h-index 5

Impact in

Papers in

Mohammadali Kargar

6 papers receiving 156 citations

Mohammadali Kargar's Hit Papers

Trust in AI: progress, challenges, and future directions 2024 · 111 citations
1110+1Years since publication255075100

Peers

Mohammadali Kargar
Comparison fields: 5 of 55
  • Health Informatics 15
  • Safety Research 19
  • Automotive Engineering 27
  • Medical Laboratory Technology 2
  • Applied Psychology 5
Replace Shahin Atakishiyev with:
Shahin Atakishiyev Canada
Daniel Omeiza United Kingdom
Shao Zhang United States
Nur Yildirim United States
Aditi Singh United States
Stephanie Milani United States
Alison O’Connell Ireland
Xingmei Wang China
Kyeong‐Ah Jeong United States
Mohammadali Kargar relative to Shahin Atakishiyev Canada Shahin Atakishiyev's profile →
Citations per field
00.5×
Shahin Atakishiyev · 1×
Citations per year

Countries citing papers authored by Mohammadali Kargar

Since Specialization
Citations

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

Fields of papers citing papers by Mohammadali Kargar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 6 scholars most cited alongside Mohammadali Kargar, 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 Mohammadali Kargar Line = papers co-authored together Mohammadali Kargar links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown

About Mohammadali Kargar

Mohammadali Kargar is a scholar working on Automotive Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Safety Research and Biomedical Engineering, having authored 7 papers that have together received 163 indexed citations. Recurring topics across this work include Electric Vehicles and Infrastructure (5 papers), Electric and Hybrid Vehicle Technologies (5 papers), Advanced Battery Technologies Research (2 papers), Mechanical Circulatory Support Devices (2 papers), Ethics and Social Impacts of AI (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Adaptive Dynamic Programming Control (1 paper) and Big Data and Business Intelligence (1 paper). The work is most often cited by research in Health Informatics (15 citations), Safety Research (19 citations), Automotive Engineering (27 citations), Medical Laboratory Technology (2 citations) and Applied Psychology (5 citations). Mohammadali Kargar has collaborated with scholars based in United States and China. Frequent co-authors include Hananeh Alambeigi, Ali Akbari, Saleh Afroogh, Xingyong Song, Chen Zhang and Chen Zhang. Their work appears in journals such as IEEE Transactions on Vehicular Technology, Humanities and Social Sciences Communications and 2022 American Control Conference (ACC).

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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