Sameera V. Mohd Sagheer

467 total citations
11 papers, 329 citations indexed

About

Sameera V. Mohd Sagheer is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Computational Mechanics. According to data from OpenAlex, Sameera V. Mohd Sagheer has authored 11 papers receiving a total of 329 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 4 papers in Media Technology and 2 papers in Computational Mechanics. Recurrent topics in Sameera V. Mohd Sagheer's work include Image and Signal Denoising Methods (8 papers), Advanced Image Processing Techniques (7 papers) and Advanced Image Fusion Techniques (3 papers). Sameera V. Mohd Sagheer is often cited by papers focused on Image and Signal Denoising Methods (8 papers), Advanced Image Processing Techniques (7 papers) and Advanced Image Fusion Techniques (3 papers). Sameera V. Mohd Sagheer collaborates with scholars based in India. Sameera V. Mohd Sagheer's co-authors include Sudhish N. George, Mohamed Abbas, P. M. Ameer and Amal BaQais and has published in prestigious journals such as Biomedical Signal Processing and Control, Artificial Intelligence in Medicine and Computers, materials & continua/Computers, materials & continua (Print).

In The Last Decade

Sameera V. Mohd Sagheer

9 papers receiving 316 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sameera V. Mohd Sagheer India	6	217	110	104	74	46	11	329
Qiyu Jin China	9	174 0.8×	77 0.7×	75 0.7×	32 0.4×	28 0.6×	36	328
Zhi‐Feng Pang China	11	285 1.3×	53 0.5×	91 0.9×	30 0.4×	39 0.8×	53	385
Xiaotong Lu China	5	304 1.4×	66 0.6×	160 1.5×	69 0.9×	40 0.9×	9	435
Zhuo‐Xu Cui China	10	87 0.4×	219 2.0×	18 0.2×	68 0.9×	25 0.5×	37	326
Matteo Maggioni Sweden	5	351 1.6×	34 0.3×	164 1.6×	56 0.8×	25 0.5×	12	443
Le Han China	10	78 0.4×	82 0.7×	26 0.3×	108 1.5×	64 1.4×	28	311
Tiep H. Vu United States	5	281 1.3×	81 0.7×	128 1.2×	34 0.5×	136 3.0×	7	390
Saad Rizvi China	11	144 0.7×	82 0.7×	99 1.0×	65 0.9×	83 1.8×	20	438
Worku Jifara China	5	132 0.6×	54 0.5×	68 0.7×	37 0.5×	43 0.9×	7	212
Chi-Hieu Pham France	7	247 1.1×	150 1.4×	84 0.8×	38 0.5×	42 0.9×	14	377

Countries citing papers authored by Sameera V. Mohd Sagheer

Since Specialization

Citations

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

Fields of papers citing papers by Sameera V. Mohd Sagheer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sameera V. Mohd Sagheer. 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 Sameera V. Mohd Sagheer. The network helps show where Sameera V. Mohd Sagheer may publish in the future.

Co-authorship network of co-authors of Sameera V. Mohd Sagheer

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

All Works

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11 of 11 papers shown

Sagheer, Sameera V. Mohd, et al.. (2025). A Deep Learning Approach to Classification of Diseases in Date Palm Leaves. Computers, materials & continua/Computers, materials & continua (Print). 84(1). 1329–1349.

Sagheer, Sameera V. Mohd, et al.. (2025). Transformers for Multi-Modal Image Analysis in Healthcare. Computers, materials & continua/Computers, materials & continua (Print). 84(3). 4259–4297. 1 indexed citations

Sagheer, Sameera V. Mohd, et al.. (2023). A Review on Various Deep Learning Techniques Used for Melanoma Detection. 450–455.

Sagheer, Sameera V. Mohd & Sudhish N. George. (2020). A review on medical image denoising algorithms. Biomedical Signal Processing and Control. 61. 102036–102036. 207 indexed citations

Sagheer, Sameera V. Mohd, et al.. (2019). Despeckling of 3D ultrasound image using tensor low rank approximation. Biomedical Signal Processing and Control. 54. 101595–101595. 9 indexed citations

Sagheer, Sameera V. Mohd & Sudhish N. George. (2018). Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization. Artificial Intelligence in Medicine. 94. 1–17. 37 indexed citations

Sagheer, Sameera V. Mohd, et al.. (2018). Denoising of Rician corrupted 3D magnetic resonance images using tensor -SVD. Biomedical Signal Processing and Control. 44. 82–95. 24 indexed citations

Sagheer, Sameera V. Mohd & Sudhish N. George. (2017). Ultrasound image despeckling using low rank matrix approximation approach. Biomedical Signal Processing and Control. 38. 236–249. 35 indexed citations

Sagheer, Sameera V. Mohd & Sudhish N. George. (2017). An Approach for Despeckling a Sequence of Ultrasound Images Based on Statistical Analysis. Sensing and Imaging. 18(1). 4 indexed citations

10.

Sagheer, Sameera V. Mohd & Sudhish N. George. (2017). Denoising of medical ultrasound images based on non-local similarity: A low-rank approach. 176–181. 4 indexed citations

11.

Sagheer, Sameera V. Mohd & Sudhish N. George. (2016). A novel approach for de-speckling of ultrasound images using bilateral filter. 22. 453–459. 8 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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