Muhammad Kazim

623 citations
14 papers · 414 indexed · 1 hit paper · h-index 7

Impact in

Papers in

Muhammad Kazim

14 papers receiving 404 citations

Hit Papers

Drone Deep Reinforcement Learning: A Review 2021 · 192 citations
192202120262022202450100150

Peers

Muhammad Kazim
Comparison fields: 5 of 60
  • Computer Vision and Pattern Recognition 113
  • Aerospace Engineering 132
  • Building and Construction 58
  • Control and Systems Engineering 94
  • Computer Networks and Communications 87
Replace Qingyan Yang with:
Qingyan Yang China
Luciano Perdig�ão Cota Brazil
Ittetsu Taniguchi Japan
Carlos Santos Spain
D. Pagac Australia
Derlis Gregor Paraguay
Véronique Cherfaoui France
Yuxiang Feng United Kingdom
Habeeb Bello-Salau Nigeria
Justin Bradley United States
Muhammad Kazim relative to Qingyan Yang China Qingyan Yang's profile →
Citations per field
00.5×4.4×
Qingyan Yang · 1×
Citations per year

Countries citing papers authored by Muhammad Kazim

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Kazim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

14 of 14 papers shown
#Work
1 20254
2 20255
3 202413
4 20226
5 20222
6 20221
7 20222
8 20223
9
Drone Deep Reinforcement Learning: A Review
Hit paper breakdown →
2021192
10 202140
11 202121
12 202022
13 2018100
14 20173

About Muhammad Kazim

Muhammad Kazim is a scholar working on Control and Systems Engineering, Energy Engineering and Power Technology, Aerospace Engineering, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 14 papers that have together received 414 indexed citations. Recurring topics across this work include Adaptive Control of Nonlinear Systems (6 papers), Robotic Path Planning Algorithms (5 papers), Distributed Control Multi-Agent Systems (4 papers), UAV Applications and Optimization (3 papers), Advanced Control Systems Optimization (2 papers), Adaptive Dynamic Programming Control (2 papers), Guidance and Control Systems (2 papers) and Autonomous Vehicle Technology and Safety (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (113 citations), Aerospace Engineering (132 citations), Building and Construction (58 citations), Control and Systems Engineering (94 citations) and Computer Networks and Communications (87 citations). Muhammad Kazim has collaborated with scholars based in China, Saudi Arabia and Egypt. Frequent co-authors include Ahmad Taher Azar, Anis Koubâa, Adel Ammar, Bilel Benjdira, Alaa Khamis, Adeel Zaidi, Ibrahim A. Hameed, Nada Ali Mohamed, Habiba A. Ibrahim and Gabriella Casalino. Their work appears in journals such as IEEE Access, Annual Reviews in Control, Electronics, Applied Energy and Journal of Intelligent & Robotic Systems.

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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