Saifun Nahar

537 total citations
38 papers, 371 citations indexed

About

Saifun Nahar is a scholar working on Radiology, Nuclear Medicine and Imaging, Epidemiology and Artificial Intelligence. According to data from OpenAlex, Saifun Nahar has authored 38 papers receiving a total of 371 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Epidemiology and 8 papers in Artificial Intelligence. Recurrent topics in Saifun Nahar's work include COVID-19 diagnosis using AI (6 papers), Mesenchymal stem cell research (6 papers) and AI in cancer detection (5 papers). Saifun Nahar is often cited by papers focused on COVID-19 diagnosis using AI (6 papers), Mesenchymal stem cell research (6 papers) and AI in cancer detection (5 papers). Saifun Nahar collaborates with scholars based in Japan, United States and China. Saifun Nahar's co-authors include Happy Nkanta Monday, Grace Ugochi Nneji, Md Altab Hossin, Jiro Fujita, Jingye Cai, Jianhua Deng, Yoshiki Nakashima, Takao Kinjo, Hirofumi Noguchi and Chika Miyagi‐Shiohira and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and International Journal of Molecular Sciences.

In The Last Decade

Saifun Nahar

37 papers receiving 362 citations

Peers

Saifun Nahar
Arjun Sharma United States
Wataru Fukuda Japan
Siqi Li China
Qiong Zhang China
Fatemeh Pak Iran
Sanghamitra Satpathi India
Bowen Xin China
Jie‐Yu You China
Joe M. Butler Netherlands
Jonas Schulte-Schrepping Germany
Arjun Sharma United States
Saifun Nahar
Citations per year, relative to Saifun Nahar Saifun Nahar (= 1×) peers Arjun Sharma

Countries citing papers authored by Saifun Nahar

Since Specialization
Citations

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

Fields of papers citing papers by Saifun Nahar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saifun Nahar

This figure shows the co-authorship network connecting the top 25 collaborators of Saifun Nahar. A scholar is included among the top collaborators of Saifun Nahar 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 Saifun Nahar. Saifun Nahar 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.
Nneji, Grace Ugochi, et al.. (2025). FFS-IML: fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease. International Journal of Machine Learning and Cybernetics. 16(9). 6215–6248. 1 indexed citations
2.
Nneji, Grace Ugochi, et al.. (2023). Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification. Diagnostics. 13(2). 299–299. 9 indexed citations
3.
Monday, Happy Nkanta, et al.. (2022). WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis. Diagnostics. 12(3). 765–765. 2 indexed citations
4.
Nneji, Grace Ugochi, Jingye Cai, Jianhua Deng, et al.. (2022). Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans. Diagnostics. 12(2). 540–540. 45 indexed citations
5.
Monday, Happy Nkanta, Jianping Li, Grace Ugochi Nneji, et al.. (2022). A wavelet convolutional capsule network with modified super resolution generative adversarial network for fault diagnosis and classification. Complex & Intelligent Systems. 8(6). 4831–4847. 13 indexed citations
6.
Pramanik, Amit, et al.. (2022). Relationship of serum testosterone with coronary artery disease in young adult males of Bangladesh. SHILAP Revista de lepidopterología. 14(3). 67–73. 1 indexed citations
7.
Monday, Happy Nkanta, et al.. (2022). COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare. 10(3). 422–422. 8 indexed citations
8.
Mita, Toshihiro, Makoto Hirai, Saifun Nahar, et al.. (2021). Derivatives of Dictyostelium differentiation-inducing factors suppress the growth of Plasmodium parasites in vitro and in vivo. Biochemical Pharmacology. 194. 114834–114834. 5 indexed citations
9.
Nakashima, Yoshiki, Saifun Nahar, Chika Miyagi‐Shiohira, et al.. (2019). Identification of Proteins Differentially Expressed by Adipose-derived Mesenchymal Stem Cells Isolated from Immunodeficient Mice. International Journal of Molecular Sciences. 20(11). 2672–2672. 6 indexed citations
10.
Nahar, Saifun, Akira Hokama, & Jiro Fujita. (2019). Perspectives of cytomegalovirus infection in ulcerative colitis. Polskie Archiwum Medycyny Wewnętrznej. 129(9). 620–626. 7 indexed citations
11.
Nahar, Saifun, Yoshiki Nakashima, Chika Miyagi‐Shiohira, et al.. (2018). A Comparison of Proteins Expressed between Human and Mouse Adipose-Derived Mesenchymal Stem Cells by a Proteome Analysis through Liquid Chromatography with Tandem Mass Spectrometry. International Journal of Molecular Sciences. 19(11). 3497–3497. 10 indexed citations
12.
Nahar, Saifun, Yoshiki Nakashima, Chika Miyagi‐Shiohira, et al.. (2018). Cytokines in adipose-derived mesenchymal stem cells promote the healing of liver disease. World Journal of Stem Cells. 10(11). 146–159. 21 indexed citations
13.
Nahar, Saifun, Yoshiki Nakashima, Chika Miyagi‐Shiohira, et al.. (2018). A Comparison of the Preservation of Mouse Adipose Tissue-Derived Mesenchymal Stem Cells Using the University of Wisconsin Solution and Hank’s Balanced Salt Solution. Stem Cells International. 2018. 1–9. 9 indexed citations
14.
Haranaga, Shusaku, Gretchen Parrott, Takeshi Kinjo, et al.. (2017). Pulmonary strongyloidiasis: assessment between manifestation and radiological findings in 16 severe strongyloidiasis cases. BMC Infectious Diseases. 17(1). 320–320. 28 indexed citations
15.
Parrott, Gretchen, Takeshi Kinjo, Saifun Nahar, et al.. (2017). Evaluation of Anyplex™ II RV16 and RB5 real-time RT-PCR compared to Seeplex® RV15 OneStep ACE and PneumoBacter ACE for the simultaneous detection of upper respiratory pathogens. Journal of Infection and Chemotherapy. 23(12). 859–861. 3 indexed citations
16.
Nahar, Saifun. (2015). Evaluation of a multiplex PCR assay for detection of cytomegalovirus in stool samples from patients with ulcerative colitis. World Journal of Gastroenterology. 21(44). 12667–12667. 17 indexed citations
17.
Shahana, Nahid, et al.. (2012). Mental illness as a contributor to intentional self inflicted suicidal burn injury. 18(1). 49–57. 3 indexed citations
18.
Jakubı́ková, Jana, David Cervi, Melissa Ooi, et al.. (2011). Anti-tumor activity and signaling events triggered by the isothiocyanates, sulforaphane and phenethyl isothiocyanate, in multiple myeloma. Haematologica. 96(8). 1170–1179. 63 indexed citations
19.
Soydan, Ender, Wentao Song, Mariateresa Fulciniti, et al.. (2010). CS1 promotes multiple myeloma cell adhesion, clonogenic growth, and tumorigenicity via c-maf-mediated interactions with bone marrow stromal cells (Blood (2009) 113, 18, (4309-4318)). Blood. 115(14). 5 indexed citations
20.
Nahar, Saifun, et al.. (1966). Vogt-Koyanagi syndrome with optic neuritis.. PubMed. 14(3). 128–30. 1 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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