Heba Askr

16 papers receiving 383 citations

Heba Askr's Hit Papers

Deep learning in drug discovery: an integrative review and future challenges 2022 · 203 citations
2030+1+2Years since publication50100150200

Peers

Heba Askr
Comparison fields: 5 of 87
  • Computational Theory and Mathematics 147
  • Health Informatics 10
  • Energy Engineering and Power Technology 18
  • Artificial Intelligence 105
  • Health Information Management 11
Replace Peiliang Zhang with:
Peiliang Zhang China
Nanda Dulal Jana India
Maryam Abbasi Portugal
K. V. Prema India
Yasmine S. Moemen Egypt
Santos Kumar Baliarsingh India
Rajesh Kumar M India
Ruihan Yang China
Shankar Thawkar India
Yongqi Sun China
Heba Askr relative to Peiliang Zhang China Peiliang Zhang's profile →
Citations per field
00.5×3.5×
Peiliang Zhang · 1×
Citations per year

Countries citing papers authored by Heba Askr

Since Specialization
Citations

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

Fields of papers citing papers by Heba Askr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Heba Askr, 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 Heba Askr Line = papers co-authored together Heba Askr links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Deep learning in drug discovery: an integrative review and future challenges
Hit paper breakdown →
2022203
2 202355
3 202430
4 202419
5 202412
6 202410
7 202310
8 202110
9 201910
10 20237
11 20247
12 20246
13 20255
14 20253
15 20252
16 20251
17 20260

About Heba Askr

Heba Askr is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications and Automotive Engineering, having authored 17 papers that have together received 390 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (3 papers), Evolutionary Algorithms and Applications (2 papers), Computational Drug Discovery Methods (2 papers), COVID-19 diagnosis using AI (2 papers), Network Security and Intrusion Detection (2 papers), Energy and Environment Impacts (2 papers), Software-Defined Networks and 5G (2 papers) and Hybrid Renewable Energy Systems (2 papers). The work is most often cited by research in Computational Theory and Mathematics (147 citations), Health Informatics (10 citations), Energy Engineering and Power Technology (18 citations), Artificial Intelligence (105 citations) and Health Information Management (11 citations). Heba Askr has collaborated with scholars based in Egypt, Kuwait and Czechia. Frequent co-authors include Aboul Ella Hassanien, Mamdouh M. Gomaa, Enas Elgeldawi, Yaseen A. M. M. Elshaier, Mahmoud Abdel-Salam, Václav Snåšel, Ashraf Darwish, M. A. El-Dosuky, Ashraf Darwish and Mostafa A. Elhosseini. Their work appears in journals such as Scientific Reports, Expert Systems with Applications, Engineering Science and Technology an International Journal, Renewable and Sustainable Energy Reviews and Drones.

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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