Riries Rulaningtyas

592 total citations
77 papers, 372 citations indexed

About

Riries Rulaningtyas is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Riries Rulaningtyas has authored 77 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Vision and Pattern Recognition, 28 papers in Artificial Intelligence and 20 papers in Media Technology. Recurrent topics in Riries Rulaningtyas's work include Digital Imaging for Blood Diseases (24 papers), Image Processing Techniques and Applications (17 papers) and COVID-19 diagnosis using AI (9 papers). Riries Rulaningtyas is often cited by papers focused on Digital Imaging for Blood Diseases (24 papers), Image Processing Techniques and Applications (17 papers) and COVID-19 diagnosis using AI (9 papers). Riries Rulaningtyas collaborates with scholars based in Indonesia, Malaysia and Australia. Riries Rulaningtyas's co-authors include Andriyan Bayu Suksmono, Tati L. R. Mengko, Wahyudi Setiawan, Budi Dwi Satoto, Imas Sukaesih Sitanggang, Nur Chamidah, Tati Rajab Mengko, Ahmad Haziq Aiman Rosol, A.A.A. Jafry and Sulaiman Wadi Harun and has published in prestigious journals such as SHILAP Revista de lepidopterología, Acta Odontologica Scandinavica and Applied Intelligence.

In The Last Decade

Riries Rulaningtyas

63 papers receiving 356 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Riries Rulaningtyas Indonesia 11 119 104 85 83 55 77 372
Ziquan Zhu United Kingdom 7 146 1.2× 121 1.2× 31 0.4× 98 1.2× 8 0.1× 10 384
Rishav Pramanik India 11 101 0.8× 202 1.9× 23 0.3× 99 1.2× 8 0.1× 14 383
Meenakshi M. Pawar India 7 121 1.0× 105 1.0× 51 0.6× 54 0.7× 6 0.1× 10 303
Wataru Shimoda Japan 10 267 2.2× 169 1.6× 20 0.2× 62 0.7× 21 0.4× 28 480
Asma Naseer Pakistan 12 123 1.0× 163 1.6× 27 0.3× 61 0.7× 7 0.1× 26 376
Mohammed Aloraini Saudi Arabia 10 227 1.9× 109 1.0× 14 0.2× 27 0.3× 14 0.3× 21 376
P. S. Hiremath India 12 154 1.3× 84 0.8× 63 0.7× 36 0.4× 37 0.7× 34 367
Yufeng Zheng United States 9 144 1.2× 142 1.4× 47 0.6× 95 1.1× 4 0.1× 35 308
Maria João M. Vasconcelos Portugal 11 119 1.0× 118 1.1× 23 0.3× 31 0.4× 12 0.2× 41 335

Countries citing papers authored by Riries Rulaningtyas

Since Specialization
Citations

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

Fields of papers citing papers by Riries Rulaningtyas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Riries Rulaningtyas

This figure shows the co-authorship network connecting the top 25 collaborators of Riries Rulaningtyas. A scholar is included among the top collaborators of Riries Rulaningtyas 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 Riries Rulaningtyas. Riries Rulaningtyas 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.
Chamidah, Nur, et al.. (2025). Multimodal deep learning from sputum image segmentation to classify <em>Mycobacterium tuberculosis</em> using IUATLD assessment. Bulletin of Electrical Engineering and Informatics. 14(2). 1579–1590. 2 indexed citations
2.
3.
Putra, Alfian Pramudita, et al.. (2024). Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods. Indonesian Journal of Electrical Engineering and Computer Science. 34(1). 173–173. 1 indexed citations
4.
Rulaningtyas, Riries, et al.. (2024). The potential of synthetic minority oversampling technique to enhance the precision of gender prediction: an investigation of artificial neural networks with cephalometry. Russian Journal of Forensic Medicine. 10(2). 139–151. 1 indexed citations
5.
Setiawan, Wahyudi, et al.. (2024). Transfer Learning and Fine Tuning in Modified VGG for Haploid Diploid Corn Seed Images Classification. Revue d intelligence artificielle. 38(2). 483–490.
6.
Rulaningtyas, Riries, et al.. (2024). Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research. Acta Odontologica Scandinavica. 83. 308–316. 7 indexed citations
7.
8.
Rulaningtyas, Riries, et al.. (2023). Classification of Pneumonia from Chest X-ray Images Using Keras Module TensorFlow. 4(1). 38–44. 1 indexed citations
9.
Kashif, Muhammad, et al.. (2023). Multilevel feedback queue: Efficient scheduling and implementation by using dynamic quantum. AIP conference proceedings. 2554. 40004–40004. 1 indexed citations
10.
Astuti, Suryani Dyah, et al.. (2023). Design and Application of Near Infrared LED and Solenoid Magnetic Field Instrument to Inactivate Pathogenic Bacteria. Micromachines. 14(4). 848–848. 1 indexed citations
11.
Rulaningtyas, Riries, et al.. (2022). ELBOW ANGLE ESTIMATION FROM EMG SIGNALS BASED ON MONTE CARLO SIMULATION. Jurnal Teknologi. 84(4). 79–90.
12.
Setiawan, Noor Akhmad, et al.. (2022). DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance. 206–211. 6 indexed citations
13.
Rulaningtyas, Riries, et al.. (2021). Simulation of Knee Joint Angle Estimation from EMG Signal for Post ACL Reconstruction Surgical Rehabilitation. Journal of Physics Conference Series. 1805(1). 12019–12019. 1 indexed citations
14.
Chamidah, Nur, et al.. (2021). Classification of mycobacterium tuberculosis based on color feature extraction using adaptive boosting method. AIP conference proceedings. 2329. 50005–50005. 6 indexed citations
15.
Setiawan, Wahyudi, et al.. (2020). Denoising Convolutional Neural Network for Fundus Patches Quality. Journal of Physics Conference Series. 1569(2). 22061–22061. 2 indexed citations
16.
Setiawan, Wahyudi, et al.. (2020). Transfer learning with multiple pre-trained network for fundus classification. TELKOMNIKA (Telecommunication Computing Electronics and Control). 18(3). 1382–1382. 9 indexed citations
17.
Rulaningtyas, Riries, et al.. (2020). Comparison of Histogram Based Image Enhancement Methods on Iris Images. Journal of Physics Conference Series. 1569(2). 22002–22002. 9 indexed citations
18.
Rulaningtyas, Riries, et al.. (2018). Post-Stroke Rehabilitation Exosceleton Movement Control using EMG Signal. International Journal on Advanced Science Engineering and Information Technology. 8(2). 616–616. 8 indexed citations
19.
Rulaningtyas, Riries, et al.. (2017). Colour segmentation of multi variants tuberculosis sputum images using self organizing map. Journal of Physics Conference Series. 853. 12012–12012. 6 indexed citations
20.
Rulaningtyas, Riries, et al.. (2014). The Improvement of Automatic Scanning Microscope Based on Intelligent Systems to Identify Mycobacterium Tuberculosis. International Journal of Ecology & Development. 29(3). 14–31. 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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