Dalila B. Megherbi

3.8k citations
85 papers · 366 indexed · h-index 9

Dalila B. Megherbi

79 papers receiving 347 citations

Peers

Dalila B. Megherbi
Comparison fields: 5 of 70
  • Computer Vision and Pattern Recognition 164
  • Media Technology 47
  • Artificial Intelligence 106
  • Management Science and Operations Research 33
  • Computer Science Applications 14
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Chao Yao China
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Adis Alihodžić Bosnia and Herzegovina
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Countries citing papers authored by Dalila B. Megherbi

Since Specialization
Citations

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

Fields of papers citing papers by Dalila B. Megherbi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dalila B. Megherbi. 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 Dalila B. Megherbi. The network helps show where Dalila B. Megherbi may publish in the future.

Co-authorship network

The 11 scholars most cited alongside Dalila B. Megherbi, 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 Dalila B. Megherbi Line = papers co-authored together Dalila B. Megherbi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20191
3 20163
4 20162
5 20162
6 20162
7 20141
8 20143
9 20132
10 20122
11 20116
12
Knowledge Sharing in Multi-Agent Environments with the Distance-and-Frequency Reinforcement Learning Technique.
20101
13 20107
14 20098
15 20087
16
A Lyapunov-Stability-Based System Hardware Architecture for a Real-Time Multiple-Look-Ahead-Levels Reinforcement Learning.
20072
17
An Autonomous Hybrid Cognitive/Reactive Agent Path Planning Technique in a Networked-Distributed Unstructured Environment for Reinforcement Learning.
20077
18
A Fast Reinforcement Learning Technique via Multiple Lookahead Levels.
20065
19
Fast Reinforcement Learning Techniques Using the Euclidean Distance and Agent State Occurrence Frequency.
20062
20 20051

About Dalila B. Megherbi

Dalila B. Megherbi is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering, having authored 85 papers that have together received 366 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (10 papers), Advanced Steganography and Watermarking Techniques (10 papers), Face and Expression Recognition (9 papers), Digital Media Forensic Detection (9 papers), Photonic and Optical Devices (8 papers), Chaos-based Image/Signal Encryption (8 papers), Robotics and Sensor-Based Localization (8 papers) and Image Retrieval and Classification Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (164 citations), Media Technology (47 citations) and Artificial Intelligence (106 citations). Dalila B. Megherbi has collaborated with scholars based in United States and Philippines. Frequent co-authors include Brian W. Soper, Nicholaos I. Limberopoulos, Augustine Urbas, Peter J. Mack, Alan Myers, Minsuk Kim, Peter Mack, Jean‐Baptiste Thomas, Hongsheng Zhang and Clare Dixon. Their work appears in journals such as Signal Processing Image Communication, Future Generation Computer Systems, IEEE Sensors Journal, Computers and Electronics in Agriculture and Journal of Visual Communication and Image Representation.

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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