Ghazal Bargshady

627 total citations
20 papers, 407 citations indexed

About

Ghazal Bargshady is a scholar working on Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging and Management Information Systems. According to data from OpenAlex, Ghazal Bargshady has authored 20 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Experimental and Cognitive Psychology, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Management Information Systems. Recurrent topics in Ghazal Bargshady's work include Emotion and Mood Recognition (6 papers), Big Data and Business Intelligence (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Ghazal Bargshady is often cited by papers focused on Emotion and Mood Recognition (6 papers), Big Data and Business Intelligence (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Ghazal Bargshady collaborates with scholars based in Australia, Malaysia and India. Ghazal Bargshady's co-authors include Xujuan Zhou, Frank Whittaker, Ravinesh C. Deo, Hua Wang, Jeffrey Soar, Raj Gururajan, Yuefeng Li, Prabal Datta Barua, Seyed Mojib Zahraee and U. Rajendra Acharya and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Ghazal Bargshady

18 papers receiving 383 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ghazal Bargshady Australia	12	93	88	84	77	51	20	407
Nursena Bayğın Türkiye	10	89 1.0×	15 0.2×	92 1.1×	77 1.0×	22 0.4×	24	389
Abdullah Alshehri Saudi Arabia	12	98 1.1×	30 0.3×	69 0.8×	21 0.3×	14 0.3×	42	460
Sabyasachi Chakraborty South Korea	12	86 0.9×	18 0.2×	67 0.8×	74 1.0×	27 0.5×	21	598
Fabian Lecron Belgium	11	104 1.1×	12 0.1×	110 1.3×	29 0.4×	61 1.2×	30	419
Ghulam Mujtaba Pakistan	13	240 2.6×	84 1.0×	100 1.2×	73 0.9×	23 0.5×	45	565
Ekkarat Boonchieng Thailand	10	65 0.7×	63 0.7×	34 0.4×	17 0.2×	69 1.4×	55	389
Adhi Dharma Wibawa Indonesia	12	89 1.0×	146 1.7×	55 0.7×	8 0.1×	226 4.4×	97	507
Moon-Il Joo South Korea	9	98 1.1×	17 0.2×	27 0.3×	27 0.4×	15 0.3×	25	370
R. Kishore Kanna India	13	81 0.9×	24 0.3×	56 0.7×	23 0.3×	26 0.5×	59	403

Countries citing papers authored by Ghazal Bargshady

Since Specialization

Citations

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

Fields of papers citing papers by Ghazal Bargshady

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ghazal Bargshady

This figure shows the co-authorship network connecting the top 25 collaborators of Ghazal Bargshady. A scholar is included among the top collaborators of Ghazal Bargshady 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 Ghazal Bargshady. Ghazal Bargshady 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

Bargshady, Ghazal, et al.. (2025). A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion. Scientific Reports. 15(1). 29467–29467. 2 indexed citations

Bargshady, Ghazal, et al.. (2025). Pain Assessment Using Multi-Kernel-FCN-LSTM and Haemoglobin Difference in fNIRS. 6(4). 1–17. 1 indexed citations

Bargshady, Ghazal, et al.. (2025). NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition. Sensors. 25(10). 2965–2965.

Bargshady, Ghazal, et al.. (2024). Acute Pain Recognition from Facial Expression Videos using Vision Transformers. PubMed. 2024. 1–4. 6 indexed citations

Rojas, Raul Fernandez, et al.. (2024). Empirical comparison of deep learning models for fNIRS pain decoding. Frontiers in Neuroinformatics. 18. 1320189–1320189. 16 indexed citations

Bargshady, Ghazal & Roland Goecke. (2023). Estimating Depression Severity from Long-Sequence Face Videos via an Ensemble Global Diverse Convolutional Model. 296–303.

Bargshady, Ghazal, Xujuan Zhou, Prabal Datta Barua, et al.. (2021). Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images. Pattern Recognition Letters. 153. 67–74. 52 indexed citations

Bargshady, Ghazal, Xujuan Zhou, Ravinesh C. Deo, et al.. (2020). Ensemble neural network approach detecting pain intensity from facial expressions. Artificial Intelligence in Medicine. 109. 101954–101954. 40 indexed citations

Zhou, Xujuan, Yuefeng Li, Raj Gururajan, et al.. (2020). A New Deep Convolutional Neural Network Model for Automated Breast Cancer Detection. 1–4. 11 indexed citations

10.

Zhou, Xujuan, Raj Gururajan, Yuefeng Li, et al.. (2020). A survey on text classification and its applications. 18(3). 205–216. 20 indexed citations

11.

Bargshady, Ghazal, Xujuan Zhou, Ravinesh C. Deo, et al.. (2020). The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space. Applied Soft Computing. 97. 106805–106805. 34 indexed citations

12.

Bargshady, Ghazal, Xujuan Zhou, Ravinesh C. Deo, et al.. (2020). Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications. 149. 113305–113305. 104 indexed citations

13.

Bargshady, Ghazal, Jeffrey Soar, Xujuan Zhou, et al.. (2019). A Joint Deep Neural Network Model for Pain Recognition from Face. 52–56. 34 indexed citations

14.

Abdar, Moloud, et al.. (2019). Performance Improvement of Decision Trees for Diagnosis of Coronary Artery Disease Using Multi Filtering Approach. 26–30. 23 indexed citations

15.

Zhou, Xujuan, Ghazal Bargshady, Moloud Abdar, et al.. (2019). A Case Study of Predicting Banking Customers Behaviour by Using Data Mining. 1–6. 2 indexed citations

16.

Bargshady, Ghazal, et al.. (2016). A Relational Study of Supply Chain Agility and Firms’ Performance in the Services Providers. SHILAP Revista de lepidopterología. 5 indexed citations

17.

Bargshady, Ghazal, et al.. (2016). The effect of information technology on the agility of the supply chain in the Iranian power plant industry. Journal of Manufacturing Technology Management. 27(3). 427–442. 29 indexed citations

18.

Bargshady, Ghazal, et al.. (2014). Evaluation of firm's potential in adoption of Green IT.. AMERICAN-EURASIAN JOURNAL OF SUSTAINABLE AGRICULTURE. 8(14). 8–13. 1 indexed citations

19.

Bargshady, Ghazal, et al.. (2014). Business Inteligence Technology Implimentation Readiness Factors. Jurnal Teknologi. 68(3). 15 indexed citations

20.

Bargshady, Ghazal, et al.. (2014). The Effective Factors on User Acceptance in Mobile Business Intelligence. Jurnal Teknologi. 72(4). 12 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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