Majid Bashir Malik

843 total citations
17 papers, 394 citations indexed

About

Majid Bashir Malik is a scholar working on Artificial Intelligence, Health Information Management and Information Systems. According to data from OpenAlex, Majid Bashir Malik has authored 17 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Health Information Management and 4 papers in Information Systems. Recurrent topics in Majid Bashir Malik's work include Artificial Intelligence in Healthcare (7 papers), Machine Learning in Healthcare (4 papers) and Stock Market Forecasting Methods (3 papers). Majid Bashir Malik is often cited by papers focused on Artificial Intelligence in Healthcare (7 papers), Machine Learning in Healthcare (4 papers) and Stock Market Forecasting Methods (3 papers). Majid Bashir Malik collaborates with scholars based in India, Saudi Arabia and South Korea. Majid Bashir Malik's co-authors include Shahid Mohammad Ganie, Tasleem Arif, Rashid Ali, Nusrat Rouf, Hee‐Cheol Kim, Satyabrata Aich, Saurabh Singh, Pijush Kanti Dutta Pramanik, Kyung Sup Kwak and Anand Nayyar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Frontiers in Genetics.

In The Last Decade

Majid Bashir Malik

16 papers receiving 360 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Majid Bashir Malik India 8 193 133 116 49 32 17 394
Nusrat Rouf India 3 135 0.7× 42 0.3× 112 1.0× 43 0.9× 35 1.1× 5 342
Matthew Herland United States 7 281 1.5× 172 1.3× 30 0.3× 46 0.9× 17 0.5× 9 453
Anand Sharma India 8 123 0.6× 71 0.5× 36 0.3× 19 0.4× 14 0.4× 18 247
Tasleem Arif India 8 55 0.3× 26 0.2× 115 1.0× 45 0.9× 29 0.9× 23 275
K. Nirmala Devi India 6 135 0.7× 20 0.2× 41 0.4× 28 0.6× 11 0.3× 50 254
Ching-Seh Wu United States 10 97 0.5× 57 0.4× 49 0.4× 13 0.3× 14 0.4× 24 299
Muhammad Ayaz Malaysia 7 110 0.6× 86 0.6× 36 0.3× 13 0.3× 9 0.3× 12 432
Braden Soper United States 6 164 0.8× 38 0.3× 27 0.2× 13 0.3× 15 0.5× 16 284
Johannes Jurgovsky Germany 4 266 1.4× 18 0.1× 36 0.3× 35 0.7× 6 0.2× 4 307
Frédéric Oblé Belgium 6 292 1.5× 27 0.2× 19 0.2× 35 0.7× 7 0.2× 7 334

Countries citing papers authored by Majid Bashir Malik

Since Specialization
Citations

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

Fields of papers citing papers by Majid Bashir Malik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Majid Bashir Malik. 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 Majid Bashir Malik. The network helps show where Majid Bashir Malik may publish in the future.

Co-authorship network of co-authors of Majid Bashir Malik

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

All Works

17 of 17 papers shown
1.
2.
Ganie, Shahid Mohammad, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, Saurav Mallik, & Hong Qin. (2023). An ensemble learning approach for diabetes prediction using boosting techniques. Frontiers in Genetics. 14. 1252159–1252159. 30 indexed citations
3.
Ganie, Shahid Mohammad, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, Anand Nayyar, & Kyung Sup Kwak. (2023). An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms. Computer Systems Science and Engineering. 46(3). 3993–4006. 30 indexed citations
4.
Rouf, Nusrat, et al.. (2022). Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network. Computational Intelligence and Neuroscience. 2022. 1–15. 4 indexed citations
5.
Ganie, Shahid Mohammad, Majid Bashir Malik, & Tasleem Arif. (2022). Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches. Journal of Diabetes & Metabolic Disorders. 21(1). 339–352. 22 indexed citations
6.
Arif, Tasleem, et al.. (2022). Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions. Archives of Computational Methods in Engineering. 30(3). 2013–2039. 24 indexed citations
7.
Ganie, Shahid Mohammad & Majid Bashir Malik. (2022). Comparative analysis of various supervised machine learning algorithms for the early prediction of type-II diabetes mellitus. International Journal of Medical Engineering and Informatics. 14(6). 473–473. 11 indexed citations
8.
Ganie, Shahid Mohammad & Majid Bashir Malik. (2022). An ensemble Machine Learning approach for predicting Type-II diabetes mellitus based on lifestyle indicators. SHILAP Revista de lepidopterología. 2. 100092–100092. 55 indexed citations
9.
Ganie, Shahid Mohammad, Majid Bashir Malik, & Tasleem Arif. (2021). Early prediction of diabetes mellitus using various artificial intelligence techniques: a technological review. 1(4). 325–325. 6 indexed citations
10.
Rouf, Nusrat, Majid Bashir Malik, Tasleem Arif, et al.. (2021). Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics. 10(21). 2717–2717. 124 indexed citations
11.
Ganie, Shahid Mohammad & Majid Bashir Malik. (2021). Comparative analysis of various supervised machine learning algorithms for the early prediction of type-II diabetes mellitus. International Journal of Medical Engineering and Informatics. 1(1). 1–1. 7 indexed citations
12.
Arif, Tasleem, et al.. (2021). Extraction and Summarization of Reviews using Lexicon based Approach. IOP Conference Series Materials Science and Engineering. 1022(1). 12117–12117. 1 indexed citations
13.
Rouf, Nusrat, Majid Bashir Malik, & Tasleem Arif. (2021). A Regression Based Approach To Predict The Indian Stock Market Trend Amid COVID-19. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). 2014–2020. 1 indexed citations
14.
Arif, Tasleem, et al.. (2020). Browser simulation-based crawler for online social network profile extraction. International Journal of Web Based Communities. 16(4). 321–321. 5 indexed citations
15.
Malik, Majid Bashir, et al.. (2015). A model for privacy preserving in data mining using Soft Computing techniques. 181–186. 2 indexed citations
16.
Arif, Tasleem, et al.. (2015). Extracting academic social networks among conference participants. 18. 42–47. 1 indexed citations
17.
Malik, Majid Bashir, et al.. (2012). Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects. 26–32. 71 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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