Md Ekramul Hossain

1.9k total citations · 1 hit paper
13 papers, 1.3k citations indexed

About

Md Ekramul Hossain is a scholar working on Health Information Management, Epidemiology and Artificial Intelligence. According to data from OpenAlex, Md Ekramul Hossain has authored 13 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Health Information Management, 4 papers in Epidemiology and 4 papers in Artificial Intelligence. Recurrent topics in Md Ekramul Hossain's work include Artificial Intelligence in Healthcare (4 papers), Chronic Disease Management Strategies (4 papers) and Machine Learning in Healthcare (4 papers). Md Ekramul Hossain is often cited by papers focused on Artificial Intelligence in Healthcare (4 papers), Chronic Disease Management Strategies (4 papers) and Machine Learning in Healthcare (4 papers). Md Ekramul Hossain collaborates with scholars based in Australia, Malaysia and United States. Md Ekramul Hossain's co-authors include Shahadat Uddin, Arif Khan, Mohammad Ali Moni, Muhammad Ashad Kabir, Lihong Zheng, Shawn McGrath, Dave L. Swain, Muhammad S. A. Zilany, Wissam A. Jassim and David L. Swain and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

Md Ekramul Hossain

12 papers receiving 1.2k citations

Hit Papers

Comparing different supervised machine learning algorithm... 2019 2026 2021 2023 2019 250 500 750 1000
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.

  1. Comparing different supervised machine learning algorithms for disease prediction
    2019 1.0k citations Shahadat Uddin, Arif Khan et al. BMC Medical Informatics and Decision Making profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Md Ekramul Hossain Australia 8 353 310 201 131 127 13 1.3k
Md Manjurul Ahsan United States 18 530 1.5× 254 0.8× 317 1.6× 140 1.1× 138 1.1× 45 1.8k
Yogesh Kumar India 27 683 1.9× 258 0.8× 290 1.4× 92 0.7× 67 0.5× 130 2.0k
Melissa Berthelot United Kingdom 6 511 1.4× 162 0.5× 346 1.7× 106 0.8× 64 0.5× 11 1.3k
Zhenxing Xu United States 17 320 0.9× 112 0.4× 100 0.5× 107 0.8× 154 1.2× 51 1.2k
Mohamad Roshanzamir Iran 16 555 1.6× 555 1.8× 277 1.4× 72 0.5× 61 0.5× 30 1.3k
Sunil Gupta Australia 14 428 1.2× 109 0.4× 168 0.8× 64 0.5× 161 1.3× 43 1.3k
Sabri Boughorbel Qatar 15 496 1.4× 80 0.3× 145 0.7× 231 1.8× 87 0.7× 46 1.6k
Anurag Jain India 20 324 0.9× 272 0.9× 136 0.7× 40 0.3× 86 0.7× 136 1.6k
Abir Hussain United Kingdom 14 351 1.0× 181 0.6× 99 0.5× 37 0.3× 103 0.8× 47 1.3k
Nipon Theera‐Umpon Thailand 20 620 1.8× 102 0.3× 205 1.0× 101 0.8× 114 0.9× 128 1.5k

Countries citing papers authored by Md Ekramul Hossain

Since Specialization
Citations

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

Fields of papers citing papers by Md Ekramul Hossain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md Ekramul Hossain. 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 Md Ekramul Hossain. The network helps show where Md Ekramul Hossain may publish in the future.

Co-authorship network of co-authors of Md Ekramul Hossain

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

All Works

13 of 13 papers shown
1.
Hossain, Md Ekramul, et al.. (2024). Near-infrared spectroscopy for analysing livestock diet quality: A systematic review. Heliyon. 10(22). e40016–e40016. 3 indexed citations
2.
Hossain, Md Ekramul, et al.. (2023). Handwritten Bangla character recognition using convolutional neural networks: a comparative study and new lightweight model. Neural Computing and Applications. 36(1). 337–348.
3.
Hossain, Md Ekramul, et al.. (2022). A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions. Artificial Intelligence in Agriculture. 6. 138–155. 57 indexed citations
4.
Uddin, Shahadat, Tasadduq Imam, Md Ekramul Hossain, et al.. (2021). Intelligent type 2 diabetes risk prediction from administrative claim data. Informatics for Health and Social Care. 47(3). 243–257. 3 indexed citations
5.
Hossain, Md Ekramul, Shahadat Uddin, & Arif Khan. (2020). Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Systems with Applications. 164. 113918–113918. 61 indexed citations
6.
Hossain, Md Ekramul, Shahadat Uddin, Arif Khan, & Mohammad Ali Moni. (2020). A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach. International Journal of Environmental Research and Public Health. 17(2). 596–596. 17 indexed citations
7.
Hossain, Md Ekramul, Arif Khan, Mohammad Ali Moni, & Shahadat Uddin. (2019). Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(2). 745–758. 45 indexed citations
8.
Uddin, Shahadat, Arif Khan, Md Ekramul Hossain, & Mohammad Ali Moni. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making. 19(1). 281–281. 1024 indexed citations breakdown →
9.
Hossain, Md Ekramul, Arif Khan, & Shahadat Uddin. (2019). Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach. Proceedings of the Australasian Computer Science Week Multiconference. 1–7. 7 indexed citations
10.
Uddin, Shahadat, et al.. (2019). A research framework to explore knowledge evolution and scholarly quantification of collaborative research. Scientometrics. 119(2). 789–803. 8 indexed citations
11.
Hossain, Md Ekramul, et al.. (2019). On the feasibility of using a bispectral measure as a nonintrusive predictor of speech intelligibility. Computer Speech & Language. 57. 59–80. 7 indexed citations
12.
Uddin, Shahadat, Md Ekramul Hossain, & Arif Khan. (2018). Triad Census and Subgroup Analysis of Patient-Sharing Physician Collaborations. IEEE Access. 6. 72233–72240. 4 indexed citations
13.
Hossain, Md Ekramul, Wissam A. Jassim, & Muhammad S. A. Zilany. (2016). Reference-Free Assessment of Speech Intelligibility Using Bispectrum of an Auditory Neurogram. PLoS ONE. 11(3). e0150415–e0150415. 20 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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