Zuherman Rustam

1.9k total citations
147 papers, 1.4k citations indexed

About

Zuherman Rustam is a scholar working on Artificial Intelligence, Health Information Management and Computer Vision and Pattern Recognition. According to data from OpenAlex, Zuherman Rustam has authored 147 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Artificial Intelligence, 45 papers in Health Information Management and 30 papers in Computer Vision and Pattern Recognition. Recurrent topics in Zuherman Rustam's work include Artificial Intelligence in Healthcare (45 papers), AI in cancer detection (39 papers) and Data Mining and Machine Learning Applications (23 papers). Zuherman Rustam is often cited by papers focused on Artificial Intelligence in Healthcare (45 papers), AI in cancer detection (39 papers) and Data Mining and Machine Learning Applications (23 papers). Zuherman Rustam collaborates with scholars based in Indonesia, Canada and Morocco. Zuherman Rustam's co-authors include Jacub Pandelaki, Titin Siswantining, Devvi Sarwinda, Rahmat Hidayat, Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, María Jesús Segovia Vargas, Dipo Aldila, Alhadi Bustamam and Benyamin Kusumoputro and has published in prestigious journals such as Symmetry, Ophthalmic Research and Ophthalmology Retina.

In The Last Decade

Zuherman Rustam

136 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zuherman Rustam Indonesia 21 696 344 220 206 197 147 1.4k
Pedro Henriques Abreu Portugal 20 895 1.3× 153 0.4× 270 1.2× 138 0.7× 175 0.9× 82 1.7k
R.N.G. Naguib United Kingdom 19 513 0.7× 196 0.6× 188 0.9× 78 0.4× 336 1.7× 174 1.5k
Bichen Zheng United States 6 609 0.9× 241 0.7× 157 0.7× 61 0.3× 126 0.6× 7 891
Sameem Abdul Kareem Malaysia 23 614 0.9× 80 0.2× 266 1.2× 126 0.6× 500 2.5× 104 1.7k
Yu Tian China 22 335 0.5× 158 0.5× 164 0.7× 72 0.3× 100 0.5× 112 1.3k
Liang Yao China 18 1.4k 2.0× 108 0.3× 115 0.5× 275 1.3× 243 1.2× 36 1.9k
V. Vinoth Kumar India 21 435 0.6× 119 0.3× 194 0.9× 112 0.5× 142 0.7× 75 1.1k
Umesh Kumar Lilhore India 20 529 0.8× 177 0.5× 112 0.5× 325 1.6× 148 0.8× 133 1.8k
Rabia Musheer Aziz India 23 627 0.9× 100 0.3× 79 0.4× 148 0.7× 151 0.8× 37 1.2k
Ji‐Jiang Yang China 23 476 0.7× 88 0.3× 723 3.3× 258 1.3× 529 2.7× 75 1.7k

Countries citing papers authored by Zuherman Rustam

Since Specialization
Citations

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

Fields of papers citing papers by Zuherman Rustam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zuherman Rustam

This figure shows the co-authorship network connecting the top 25 collaborators of Zuherman Rustam. A scholar is included among the top collaborators of Zuherman Rustam 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 Zuherman Rustam. Zuherman Rustam 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.
Rustam, Zuherman, et al.. (2024). Association of Neighborhood Socioeconomic Disadvantage with Proliferative Diabetic Retinopathy. Ophthalmology Retina. 9(2). 98–104. 2 indexed citations
2.
Rustam, Zuherman, et al.. (2023). An Analysis of Several Optimizers on CNNSVM and CNNRF for COVID–19 Chest X–ray Images. International Journal on Advanced Science Engineering and Information Technology. 13(4). 1486–1491.
3.
Rustam, Zuherman, et al.. (2023). An Efficient and Robust Ischemic Stroke Detection Using a Combination of Convolutional Neural Network (CNN) and Kernel K-Means Clustering. International Journal on Advanced Science Engineering and Information Technology. 13(3). 969–974.
4.
Rustam, Zuherman, et al.. (2021). Tuberculosis Detection based on Chest X-Rays using Ensemble Method with CNN Feature Extraction. 682–686. 8 indexed citations
5.
Rustam, Zuherman, et al.. (2021). Twin Support Vector Machines for Thalassemia Classification. 160–164. 2 indexed citations
6.
Rustam, Zuherman, et al.. (2021). Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image. IAES International Journal of Artificial Intelligence. 10(2). 291–291. 8 indexed citations
7.
Rustam, Zuherman, et al.. (2021). Pancreatic Cancer Classification Using the Kernel-based Support Vector Machine (KSVM). Journal of Physics Conference Series. 1752(1). 12032–12032. 1 indexed citations
8.
Alaoui, El Arbi Abdellaoui, et al.. (2021). Improvement in automated diagnosis of soft tissues tumors using machine learning. Big Data Mining and Analytics. 4(1). 33–46. 29 indexed citations
9.
Rustam, Zuherman, et al.. (2021). Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine. Journal of Physics Conference Series. 1821(1). 12007–12007. 10 indexed citations
10.
Siswantining, Titin, et al.. (2019). Classification of thalassemia data using K-nearest neighbor and Naïve Bayes. International Journal of Advanced Science and Technology. 28. 15–19. 3 indexed citations
11.
Rustam, Zuherman, et al.. (2019). Decision making in the indonesian stock exchange using a fuzzy logic method. Journal of Theoretical and Applied Information Technology. 97(14). 3937–3946. 1 indexed citations
12.
Rustam, Zuherman, et al.. (2019). Soft Tissue Tumor Classification using Stochastic Support Vector Machine. IOP Conference Series Materials Science and Engineering. 546(5). 52089–52089. 7 indexed citations
13.
Rustam, Zuherman, et al.. (2019). Enhancement of hepatitis virus outcome predictions with application of K-means clustering. AIP conference proceedings. 2168. 20044–20044. 1 indexed citations
14.
Rustam, Zuherman, et al.. (2018). Fuzzy Kernel Robust Clustering for Anomaly based Intrusion Detection. 2. 1–4. 5 indexed citations
15.
Rustam, Zuherman, et al.. (2018). Multiclass classification on brain cancer with multiple support vector machine and feature selection based on kernel function. AIP conference proceedings. 2023. 20233–20233. 2 indexed citations
16.
Rustam, Zuherman, et al.. (2016). Cancer classification using Fuzzy C-Means with feature selection. 31–34. 21 indexed citations
17.
Rustam, Zuherman, et al.. (2015). Fuzzy kernel C-means algorithm for intrusion detection systems. Journal of Theoretical and Applied Information Technology. 81(1). 161–165. 15 indexed citations
18.
Rustam, Zuherman, et al.. (2015). Fuzzy kernel K-medoids algorithm for multiclass multidimensional data classification. Journal of Theoretical and Applied Information Technology. 80(1). 147–151. 13 indexed citations
19.
Rustam, Zuherman, et al.. (2011). Non-invasive Intracranial Pressure classification using Strong Jumping Emerging Patterns. 377–380. 2 indexed citations
20.
Rustam, Zuherman, et al.. (2011). Application of Fuzzy Kernel K-Medoids Method for Cancer Classification based on Metal Concentration in Blood. JURNAL ILMU KEFARMASIAN INDONESIA. 9(2). 147–151. 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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