Rabbia Mahum

1.2k total citations · 1 hit paper
35 papers, 683 citations indexed

About

Rabbia Mahum is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Rabbia Mahum has authored 35 papers receiving a total of 683 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 8 papers in Signal Processing. Recurrent topics in Rabbia Mahum's work include Speech and Audio Processing (6 papers), Digital Imaging for Blood Diseases (6 papers) and Music and Audio Processing (5 papers). Rabbia Mahum is often cited by papers focused on Speech and Audio Processing (6 papers), Digital Imaging for Blood Diseases (6 papers) and Music and Audio Processing (5 papers). Rabbia Mahum collaborates with scholars based in Pakistan, Saudi Arabia and China. Rabbia Mahum's co-authors include AbdulMalik S. Al‐Salman, Aun Irtaza, Marriam Nawaz, Talha Meraj, Iskander Tlili, Momina Masood, Hafiz Tayyab Rauf, Suliman Aladhadh, Tahira Nazir and Falak Sher Khan and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Rabbia Mahum

33 papers receiving 649 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.

A novel framework for potato leaf disease detection using an efficient deep learning model

2022 157 citations Rabbia Mahum et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rabbia Mahum Pakistan	12	235	197	147	140	67	35	683
Md. Nahiduzzaman Bangladesh	20	277 1.2×	474 2.4×	128 0.9×	325 2.3×	31 0.5×	45	1.1k
Jianhua Qu China	10	76 0.3×	126 0.6×	136 0.9×	276 2.0×	49 0.7×	36	707
Dildar Hussain South Korea	14	151 0.6×	200 1.0×	99 0.7×	164 1.2×	3 0.0×	60	746
Parthasarathi Mangipudi India	11	249 1.1×	292 1.5×	15 0.1×	62 0.4×	18 0.3×	27	521
Saeed Ur Rehman Pakistan	9	145 0.6×	122 0.6×	22 0.1×	99 0.7×	4 0.1×	15	513
Hermawan Nugroho Malaysia	13	81 0.3×	144 0.7×	38 0.3×	25 0.2×	44 0.7×	53	389
Umair Khan Pakistan	9	109 0.5×	84 0.4×	41 0.3×	241 1.7×	17 0.3×	27	597
Mighty Abra Ayidzoe Ghana	7	131 0.6×	69 0.4×	77 0.5×	141 1.0×	25 0.4×	17	422
Md. Omaer Faruq Goni Bangladesh	13	173 0.7×	312 1.6×	45 0.3×	182 1.3×	14 0.2×	19	589
Pradeep Kumar Singh India	9	62 0.3×	127 0.6×	21 0.1×	81 0.6×	5 0.1×	51	431

Countries citing papers authored by Rabbia Mahum

Since Specialization

Citations

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

Fields of papers citing papers by Rabbia Mahum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rabbia Mahum

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

All Works

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20 of 20 papers shown

1.

Mahum, Rabbia, et al.. (2025). A novel Swin transformer based framework for speech recognition for dysarthria. Scientific Reports. 15(1). 20070–20070. 1 indexed citations

2.

Mahum, Rabbia, et al.. (2025). Plant Disease Detection Using Deep Learning Techniques. 1(1). 36–44.

3.

Mahum, Rabbia, et al.. (2024). DeepDet: YAMNet with BottleNeck Attention Module (BAM) for TTS synthesis detection. EURASIP Journal on Audio Speech and Music Processing. 2024(1). 8 indexed citations

4.

Mahum, Rabbia, et al.. (2024). Tran-DSR: A hybrid model for dysarthric speech recognition using transformer encoder and ensemble learning. Applied Acoustics. 222. 110019–110019. 9 indexed citations

5.

Qadir, Abdul, et al.. (2024). An efficient deepfake video detection using robust deep learning. Heliyon. 10(5). e25757–e25757. 16 indexed citations

6.

Mahum, Rabbia, Aun Irtaza, Ali Javed, Haitham A. Mahmoud, & Haseeb Hassan. (2024). Correction: DeepDet: YAMNet with BottleNeck Attention Module (BAM) for TTS synthesis detection. EURASIP Journal on Audio Speech and Music Processing. 2024(1).

7.

Awais, Muhammad, Rabbia Mahum, Hao Zhang, et al.. (2024). Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism. Scientific Reports. 14(1). 10042–10042. 11 indexed citations

8.

Mahum, Rabbia, et al.. (2024). UTran-DSR: a novel transformer-based model using feature enhancement for dysarthric speech recognition. EURASIP Journal on Audio Speech and Music Processing. 2024(1). 2 indexed citations

9.

Mahum, Rabbia, et al.. (2023). Automatic Crop Expert System Using Improved LSTM with Attention Block. Computer Systems Science and Engineering. 47(2). 2007–2025. 5 indexed citations

10.

Mahum, Rabbia, et al.. (2023). A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion. Applied Sciences. 13(7). 4581–4581. 17 indexed citations

11.

Mahum, Rabbia, et al.. (2023). SCDet: A Robust Approach for the Detection of Skin Lesions. Diagnostics. 13(11). 1824–1824. 3 indexed citations

12.

Mahum, Rabbia & AbdulMalik S. Al‐Salman. (2023). Lung-RetinaNet: Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module. IEEE Access. 11. 53850–53861. 27 indexed citations

13.

Mahum, Rabbia, et al.. (2023). A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding. Biomedicines. 11(6). 1715–1715. 10 indexed citations

14.

Mahum, Rabbia, et al.. (2022). A Robust Framework for Object Detection in a Traffic Surveillance System. Electronics. 11(21). 3425–3425. 25 indexed citations

15.

Hayat, Faisal & Rabbia Mahum. (2022). Deep Learning System for Detecting the Diabetic Retinopathy. 6. 1–7. 2 indexed citations

16.

Mahum, Rabbia & Suliman Aladhadh. (2022). Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM. Diagnostics. 12(12). 2974–2974. 22 indexed citations

17.

Mahum, Rabbia, Aun Irtaza, Marriam Nawaz, et al.. (2021). A generic framework for Generation of Summarized Video Clips using Transfer Learning (SumVClip). 1–8. 7 indexed citations

18.

Nazir, Tahira, Marriam Nawaz, Junaid Rashid, et al.. (2021). Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. Sensors. 21(16). 5283–5283. 87 indexed citations

19.

Mehmood, Awais, et al.. (2021). Brain Tumor Segmentation and Classification using Machine Learning. 13–18. 3 indexed citations

20.

Mahum, Rabbia, et al.. (2017). A review on humanoid robots. International Journal of ADVANCED AND APPLIED SCIENCES. 4(2). 83–90. 6 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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