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.
Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample
2020196 citationsMohd Syahril Ramadhan Mohd Saufi, Zair Asrar Ahmad et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by M. Salman Leong
Since
Specialization
Citations
This map shows the geographic impact of M. Salman Leong'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 M. Salman Leong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Salman Leong more than expected).
This network shows the impact of papers produced by M. Salman Leong. 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 M. Salman Leong. The network helps show where M. Salman Leong may publish in the future.
Co-authorship network of co-authors of M. Salman Leong
This figure shows the co-authorship network connecting the top 25 collaborators of M. Salman Leong.
A scholar is included among the top collaborators of M. Salman Leong 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 M. Salman Leong. M. Salman Leong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ahmad, Zair Asrar, et al.. (2020). A study of the parameters that effect the quality of the estimated modal parameters in automated enhanced frequency domain decomposition algorithm. Pertanika journal of science & technology. 28(3).1 indexed citations
Ngui, Wai Keng, M. Salman Leong, Mohd Ibrahim Shapiai, & Meng Hee Lim. (2017). Blade fault diagnosis using artificial neural network. International Journal of Applied Engineering Research. 12(4). 519–526.10 indexed citations
10.
Saufi, Mohd Syahril Ramadhan Mohd, Zair Asrar Ahmad, Meng Hee Lim, & M. Salman Leong. (2017). A review on signal processing techniques for bearing diagnostics. 8(6). 327–337.8 indexed citations
11.
Leong, M. Salman, et al.. (2017). Empirical mode decomposition: A review on mode selection method for rotating machinery diagnosis. 8(6). 16–26.4 indexed citations
Leong, M. Salman, et al.. (2016). Acoustic emission parameters evaluation in machinery condition monitoring by using the concept of multivariate analysis. ARPN Journal of Engineering and Applied Sciences. 11(12). 7507–7514.6 indexed citations
14.
Leong, M. Salman & Lim Meng Hee. (2013). Blades rubs and looseness detection in gas turbines - operational field experience and laboratory study. Journal of Vibroengineering. 15(3). 1311–1321.5 indexed citations
15.
Hee, Lim Meng & M. Salman Leong. (2012). Experimental study of dynamic responses of casing deflection profile for blade rubbing classification. Journal of Vibroengineering. 14(4). 1668–1680.4 indexed citations
16.
Leong, M. Salman, et al.. (2012). Turbine Generator Synchronization - Two Case Studies. Sound&Vibration. 46(5). 8–11.17 indexed citations
17.
Huang, Yu‐Lun, J. D. Tygar, Hsuan-Yin Lin, et al.. (2008). SWOON: a testbed for secure wireless overlay networks. USENIX Security Symposium. 8.8 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.