Shafaf Ibrahim

610 total citations
65 papers, 402 citations indexed

About

Shafaf Ibrahim is a scholar working on Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence. According to data from OpenAlex, Shafaf Ibrahim has authored 65 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 16 papers in Neurology and 11 papers in Artificial Intelligence. Recurrent topics in Shafaf Ibrahim's work include Brain Tumor Detection and Classification (16 papers), Medical Image Segmentation Techniques (12 papers) and COVID-19 diagnosis using AI (6 papers). Shafaf Ibrahim is often cited by papers focused on Brain Tumor Detection and Classification (16 papers), Medical Image Segmentation Techniques (12 papers) and COVID-19 diagnosis using AI (6 papers). Shafaf Ibrahim collaborates with scholars based in Malaysia, Indonesia and United Kingdom. Shafaf Ibrahim's co-authors include Mazani Manaf, Nurbaity Sabri, Derek D. Lichti, Khyrina Airin Fariza Abu Samah, Zaidah Ibrahim, Umi Kalthum Ngah, Nor Azura Md Ghani, Azlee Zabidi, Ahmad Ihsan Mohd Yassin and Nursuriati Jamil and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Advanced Computer Science and Applications and TELKOMNIKA (Telecommunication Computing Electronics and Control).

In The Last Decade

Shafaf Ibrahim

51 papers receiving 350 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shafaf Ibrahim Malaysia	12	116	72	68	65	51	65	402
Yuchen Zheng China	10	166 1.4×	28 0.4×	82 1.2×	27 0.4×	41 0.8×	33	392
Nikita Andriyanov Russia	13	148 1.3×	23 0.3×	74 1.1×	77 1.2×	16 0.3×	78	494
Yasser Baleghi Iran	15	240 2.1×	8 0.1×	59 0.9×	69 1.1×	49 1.0×	55	586
R Meghana India	4	115 1.0×	12 0.2×	73 1.1×	39 0.6×	14 0.3×	6	350
G Divya India	3	98 0.8×	12 0.2×	68 1.0×	38 0.6×	14 0.3×	6	327
Tahir Nawaz United Kingdom	11	203 1.8×	23 0.3×	97 1.4×	52 0.8×	18 0.4×	40	362
Muhammad Attique Khan Saudi Arabia	14	76 0.7×	17 0.2×	134 2.0×	39 0.6×	27 0.5×	76	492
Syam Machinathu Parambil Gangadharan India	9	67 0.6×	29 0.4×	76 1.1×	20 0.3×	19 0.4×	16	371
Afef Abdelkrim Tunisia	8	141 1.2×	17 0.2×	130 1.9×	26 0.4×	10 0.2×	49	441

Countries citing papers authored by Shafaf Ibrahim

Since Specialization

Citations

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

Fields of papers citing papers by Shafaf Ibrahim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shafaf Ibrahim

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

All Works

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20 of 20 papers shown

Ibrahim, Shafaf, et al.. (2025). Experimental research on text CAPTCHA of fine-grained security features. Indonesian Journal of Electrical Engineering and Computer Science. 38(1). 535–535.

Jamil, Nursuriati, et al.. (2025). Optimizing deep learning models from multi-objective perspective via Bayesian optimization. IAES International Journal of Artificial Intelligence. 14(2). 1420–1420.

Zhang, Lifan, et al.. (2024). Rumor detection based on deep learning techniques: a systematic review. TELKOMNIKA (Telecommunication Computing Electronics and Control). 22(4). 869–869. 2 indexed citations

Ibrahim, Shafaf, et al.. (2024). Automated Receipt Scanning Using Convolutional Recurrent Neural Network (CRNN). 494–497.

Jamil, Nursuriati, et al.. (2024). Multi objective hyperparameter tuning via random search on deep learning models. TELKOMNIKA (Telecommunication Computing Electronics and Control). 22(4). 956–956. 4 indexed citations

Sabri, Nurbaity, et al.. (2024). Visual Facial Paralysis Detection using InceptionResNetV2. 211–215.

Sabri, Nurbaity, et al.. (2024). A real-time system for detecting personal protective equipment compliance using deep learning model YOLOv5. Procedia Computer Science. 245. 647–656. 1 indexed citations

Ibrahim, Shafaf, et al.. (2023). COVID-19 Lung Detection using Keras Deep Learning Models. 195. 87–92.

Ibrahim, Shafaf, et al.. (2023). Automated Platelet Counter with Detection Using K-Means Clustering. 7(5). 39–49.

10.

Ibrahim, Shafaf, et al.. (2023). Deep Learning Food Detection and Calories Counter. 8. 397–401.

11.

Ibrahim, Shafaf, et al.. (2023). Automated Defective Ceramic Tiles Classification using Image Processing Techniques. Journal of Advanced Research in Applied Sciences and Engineering Technology. 32(3). 355–365.

12.

Ibrahim, Shafaf, et al.. (2023). A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research. Journal of Advanced Research in Applied Sciences and Engineering Technology. 34(2). 363–373. 1 indexed citations

13.

Ibrahim, Shafaf, et al.. (2022). Substantial adaptive artificial bee colony algorithm implementation for glioblastoma detection. IAES International Journal of Artificial Intelligence. 12(1). 443–443. 3 indexed citations

14.

Ibrahim, Shafaf, et al.. (2022). Fish diseases detection using convolutional neural network (CNN). International journal of nonlinear analysis and applications. 13(1). 1977–1984. 26 indexed citations

15.

Ibrahim, Shafaf, et al.. (2022). Palm leaf nutrient deficiency detection using convolutional neural network (CNN). International journal of nonlinear analysis and applications. 13(1). 1949–1956. 7 indexed citations

16.

Samah, Khyrina Airin Fariza Abu, et al.. (2022). Implementation of Faster Region-Based Convolutional Neural Network for Waste Type Classification. 125–130. 3 indexed citations

17.

Ibrahim, Shafaf, et al.. (2019). A Real Time Malaysian Sign Language Detection Algorithm Based on YOLOv3. International Journal of Recent Technology and Engineering (IJRTE). 8(2S11). 651–656. 10 indexed citations

18.

Ibrahim, Shafaf, et al.. (2019). Automatic Tajweed Rules Recognition using K-Nearest Neighbour (K-NN). International Journal of Recent Technology and Engineering (IJRTE). 8(2S11). 552–557. 4 indexed citations

19.

Ibrahim, Shafaf, et al.. (2011). Brain abnormalities segmentation performances contrasting: adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM). Annual Conference on Computers. 285–290. 8 indexed citations

20.

Ibrahim, Shafaf, et al.. (2011). Evaluation method for MRI brain tissue abnormalities segmentation study. Annual Conference on Computers. 297–302. 2 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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