Noha Negm

540 total citations
37 papers, 282 citations indexed

About

Noha Negm is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Noha Negm has authored 37 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 8 papers in Computer Networks and Communications. Recurrent topics in Noha Negm's work include Network Security and Intrusion Detection (4 papers), Advanced Neural Network Applications (4 papers) and Energy Load and Power Forecasting (3 papers). Noha Negm is often cited by papers focused on Network Security and Intrusion Detection (4 papers), Advanced Neural Network Applications (4 papers) and Energy Load and Power Forecasting (3 papers). Noha Negm collaborates with scholars based in Saudi Arabia, Egypt and Pakistan. Noha Negm's co-authors include Marwa Obayya, Muhammad Sohail Khan, Ahmed S. Salama, Wahab Khan, Javed Ali Khan, Naeem Ullah, Radwa Marzouk, Manar Ahmed Hamza, Fahd N. Al‐Wesabi and Majdi Khalid and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Noha Negm

32 papers receiving 261 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Noha Negm Saudi Arabia 8 110 98 79 48 39 37 282
Omar Hisham Alsadoon Iraq 11 129 1.2× 149 1.5× 67 0.8× 39 0.8× 83 2.1× 37 375
Amena Mahmoud Egypt 11 76 0.7× 151 1.5× 53 0.7× 23 0.5× 77 2.0× 27 340
Mighty Abra Ayidzoe Ghana 7 131 1.2× 141 1.4× 43 0.5× 17 0.4× 69 1.8× 17 422
Sapiah Sakri Saudi Arabia 7 59 0.5× 205 2.1× 58 0.7× 30 0.6× 50 1.3× 16 374
Mohemmed Sha Saudi Arabia 9 58 0.5× 137 1.4× 56 0.7× 29 0.6× 63 1.6× 45 319
J. Arokia Renjit India 9 55 0.5× 104 1.1× 35 0.4× 89 1.9× 23 0.6× 30 264
Patrick Kwabena Mensah Ghana 7 117 1.1× 119 1.2× 42 0.5× 12 0.3× 64 1.6× 27 371
Vandana Bhattacharjee India 10 47 0.4× 84 0.9× 46 0.6× 40 0.8× 18 0.5× 37 349
P. Dayananda India 11 59 0.5× 73 0.7× 20 0.3× 50 1.0× 26 0.7× 54 286
Amin Alqudah Jordan 14 112 1.0× 138 1.4× 22 0.3× 45 0.9× 129 3.3× 32 413

Countries citing papers authored by Noha Negm

Since Specialization
Citations

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

Fields of papers citing papers by Noha Negm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Noha Negm

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

All Works

20 of 20 papers shown
1.
Aldehim, Ghadah, et al.. (2025). Integrating geospatial techniques and machine learning for assessing soil erosion and associated geomorphic risks. Journal of South American Earth Sciences. 156. 105463–105463. 3 indexed citations
2.
Maashi, Mashael, et al.. (2025). Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging. Scientific Reports. 15(1). 6610–6610. 3 indexed citations
3.
Maashi, Mashael, et al.. (2025). Forecasting land use changes in crop classification and drought using remote sensing. Journal of Arid Land. 17(5). 575–589.
4.
Khan, Wali Ullah, et al.. (2025). Digital Twin Enabled 6G MEC Networks: Computational Energy Efficiency for Consumer IIoT in Industry 5.0. IEEE Transactions on Consumer Electronics. 71(3). 7874–7881.
5.
Maashi, Mashael, et al.. (2024). Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models. Journal of South American Earth Sciences. 151. 105272–105272.
6.
Maashi, Mashael, Eatedal Alabdulkreem, Noha Negm, et al.. (2024). Energy efficiency optimization for 6G multi-IRS multi-cell NOMA vehicle-to-infrastructure communication networks. Computer Communications. 225. 350–360.
7.
Negm, Noha, et al.. (2024). Tasmanian devil optimization with deep autoencoder for intrusion detection in IoT assisted unmanned aerial vehicle networks. Ain Shams Engineering Journal. 15(11). 102943–102943. 2 indexed citations
8.
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10.
Alrowais, Fadwa, Noha Negm, Majdi Khalid, et al.. (2023). Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface. IEEE Access. 11. 35089–35096. 7 indexed citations
11.
Alrowais, Fadwa, Noha Negm, Majdi Khalid, et al.. (2023). Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface. IEEE Access. 11. 62233–62241. 11 indexed citations
12.
Marzouk, Radwa, Nuha Alruwais, Noha Negm, et al.. (2023). Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning. IEEE Access. 11. 82199–82207. 29 indexed citations
13.
Hamza, Manar Ahmed, Hanan Abdullah Mengash, Noha Negm, et al.. (2023). Deep Consensus Network for Recycling Waste Detection in Smart Cities. Computers, materials & continua/Computers, materials & continua (Print). 75(2). 4191–4205. 1 indexed citations
14.
Negm, Noha, et al.. (2023). Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging. IEEE Access. 11. 75474–75483. 3 indexed citations
15.
Alrowais, Fadwa, Saud S. Alotaibi, Fahd N. Al‐Wesabi, et al.. (2022). Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems. Applied Sciences. 12(13). 6665–6665. 14 indexed citations
16.
Alotaibi, Saud S., Hanan Abdullah Mengash, Noha Negm, et al.. (2022). Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks. Applied Sciences. 12(13). 6488–6488. 5 indexed citations
17.
Ali, Mushtaq, Marwa Obayya, Junaid Asghar, et al.. (2022). Machine learning based skin lesion segmentation method with novel borders and hair removal techniques. PLoS ONE. 17(11). e0275781–e0275781. 6 indexed citations
18.
Ullah, Naeem, Javed Ali Khan, Muhammad Sohail Khan, et al.. (2022). An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning. Applied Sciences. 12(11). 5645–5645. 97 indexed citations
19.
Al‐Wesabi, Fahd N., et al.. (2021). Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen. Computers, materials & continua/Computers, materials & continua (Print). 70(1). 1177–1193. 5 indexed citations
20.
Kasemy, Zeinab A., et al.. (2016). Factors related to depression symptoms among working women in Menoufia, Egypt.. PubMed. 91(4). 163–168. 5 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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