Heba Askr

628 total citations · 1 hit paper
16 papers, 339 citations indexed

About

Heba Askr is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Heba Askr has authored 16 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computational Theory and Mathematics and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Heba Askr's work include Metaheuristic Optimization Algorithms Research (3 papers), Hybrid Renewable Energy Systems (2 papers) and Computational Drug Discovery Methods (2 papers). Heba Askr is often cited by papers focused on Metaheuristic Optimization Algorithms Research (3 papers), Hybrid Renewable Energy Systems (2 papers) and Computational Drug Discovery Methods (2 papers). Heba Askr collaborates with scholars based in Egypt, Kuwait and Czechia. Heba Askr's co-authors include Aboul Ella Hassanien, Yaseen A. M. M. Elshaier, Enas Elgeldawi, Mahmoud Abdel-Salam, Ashraf Darwish, Václav Snåšel, M. A. El-Dosuky, Mostafa A. Elhosseini, Ashraf Darwish and Rizk M. Rizk‐Allah and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, PLoS ONE and Scientific Reports.

In The Last Decade

Heba Askr

13 papers receiving 330 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning in drug discovery: an integrative review and future challenges

2022 181 citations Heba Askr, Enas Elgeldawi et al. Artificial Intelligence Review profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Heba Askr Egypt	9	136	99	98	31	31	16	339
Hanqun Cao China	4	103 0.8×	103 1.0×	48 0.5×	60 1.9×	48 1.5×	5	330
Shuangbao Song Japan	11	96 0.7×	82 0.8×	215 2.2×	29 0.9×	12 0.4×	23	353
André M. S. Barreto Brazil	9	131 1.0×	84 0.8×	115 1.2×	20 0.6×	23 0.7×	18	316
Yasmine S. Moemen Egypt	10	61 0.4×	71 0.7×	79 0.8×	22 0.7×	58 1.9×	18	326
Maryam Abbasi Portugal	9	131 1.0×	106 1.1×	34 0.3×	13 0.4×	66 2.1×	55	299
Haojie Wang China	11	66 0.5×	64 0.6×	108 1.1×	55 1.8×	34 1.1×	62	376
K. V. Prema India	11	133 1.0×	117 1.2×	84 0.9×	75 2.4×	43 1.4×	55	521
Zhonghua Wang China	9	80 0.6×	107 1.1×	53 0.5×	49 1.6×	35 1.1×	52	354

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-authorship network of co-authors of Heba Askr

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

All Works

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16 of 16 papers shown

Askr, Heba, et al.. (2025). An Intelligent and Optimized System for Predicting Sustainable Biodiesel Production Using IoT-Based Palm-Oil Trees. BioEnergy Research. 18(1).

Askr, Heba, Marwa A. A. Fayed, Heba Mamdouh Farghaly, et al.. (2025). Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid. Scientific Reports. 15(1). 2224–2224. 2 indexed citations

Askr, Heba, et al.. (2025). Artificial intelligence for sustainable green hydrogen production: A systematic literature review. Renewable and Sustainable Energy Reviews. 224. 116071–116071.

Abdel-Salam, Mahmoud, Heba Askr, & Aboul Ella Hassanien. (2025). Revolutionizing cervical cancer detection: a new optimized explainable artificial intelligence model. Neural Computing and Applications. 37(29). 23979–24023.

Askr, Heba, M. A. El-Dosuky, Ashraf Darwish, & Aboul Ella Hassanien. (2024). Explainable ResNet50 learning model based on copula entropy for cotton plant disease prediction. Applied Soft Computing. 164. 112009–112009. 13 indexed citations

Askr, Heba, Mahmoud Abdel-Salam, Václav Snåšel, & Aboul Ella Hassanien. (2024). A green hydrogen production model from solar powered water electrolyze based on deep chaotic Lévy gazelle optimization. Engineering Science and Technology an International Journal. 60. 101874–101874. 8 indexed citations

Askr, Heba, et al.. (2024). Intelligent Parcel Delivery Scheduling Using Truck-Drones to Cut down Time and Cost. Drones. 8(9). 477–477. 10 indexed citations

Askr, Heba, et al.. (2024). Prediction of methane emission and electricity generation from landfills: Deep learning approach. Energy Reports. 12. 5462–5472. 5 indexed citations

Abdel-Salam, Mahmoud, Heba Askr, & Aboul Ella Hassanien. (2024). Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems. Expert Systems with Applications. 256. 124882–124882. 28 indexed citations

10.

Askr, Heba, et al.. (2024). Multiclass deep learning model for predicting lung diseases based on honey badger algorithm. International Journal of Information Technology. 17(2). 1147–1154. 6 indexed citations

11.

Askr, Heba, Mahmoud Abdel-Salam, & Aboul Ella Hassanien. (2023). Copula entropy-based golden jackal optimization algorithm for high-dimensional feature selection problems. Expert Systems with Applications. 238. 121582–121582. 51 indexed citations

12.

Askr, Heba, et al.. (2023). Many-objective African vulture optimization algorithm: A novel approach for many-objective problems. PLoS ONE. 18(5). e0284110–e0284110. 9 indexed citations

13.

Farghaly, Heba Mamdouh, Enas Elgeldawi, Heba Askr, et al.. (2023). A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines. Scientific Reports. 13(1). 9171–9171. 7 indexed citations

14.

Askr, Heba, et al.. (2022). Deep learning in drug discovery: an integrative review and future challenges. Artificial Intelligence Review. 56(7). 5975–6037. 181 indexed citations breakdown →

15.

Askr, Heba, et al.. (2021). Dynamic Routing Optimization Algorithm for Software Defined Networking. Computers, materials & continua/Computers, materials & continua (Print). 70(1). 1349–1362. 10 indexed citations

16.

Askr, Heba, et al.. (2019). ACOSDN-Ant Colony Optimization Algorithm for Dynamic Routing In Software Defined Networking. 141–148. 9 indexed citations

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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