Foad Ghaderi

618 total citations
46 papers, 411 citations indexed

About

Foad Ghaderi is a scholar working on Signal Processing, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Foad Ghaderi has authored 46 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Signal Processing, 14 papers in Cognitive Neuroscience and 11 papers in Artificial Intelligence. Recurrent topics in Foad Ghaderi's work include Blind Source Separation Techniques (15 papers), EEG and Brain-Computer Interfaces (13 papers) and Functional Brain Connectivity Studies (5 papers). Foad Ghaderi is often cited by papers focused on Blind Source Separation Techniques (15 papers), EEG and Brain-Computer Interfaces (13 papers) and Functional Brain Connectivity Studies (5 papers). Foad Ghaderi collaborates with scholars based in Iran, United Kingdom and Germany. Foad Ghaderi's co-authors include Saeid Sanei, H. Mohseni, Ali Safaei, M. J. Ebadi, Elsa Andrea Kirchner, Nadia Mammone, Nasrollah Moghadam Charkari, Su Kyoung Kim, J.G. McWhirter and Bahador Makkiabadi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Foad Ghaderi

42 papers receiving 394 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Foad Ghaderi Iran 9 148 116 66 65 47 46 411
Saeid Sanei United Kingdom 12 256 1.7× 202 1.7× 16 0.2× 47 0.7× 48 1.0× 28 515
Cristhian Potes United States 9 162 1.1× 103 0.9× 190 2.9× 49 0.8× 47 1.0× 17 536
Valérie Louis-Dorr France 13 183 1.2× 132 1.1× 84 1.3× 21 0.3× 77 1.6× 42 456
Nurettin Acır Türkiye 10 161 1.1× 217 1.9× 9 0.1× 53 0.8× 48 1.0× 35 394
Sabine Van Huffel Belgium 7 248 1.7× 222 1.9× 19 0.3× 27 0.4× 42 0.9× 13 437
A. Petrosian United States 7 356 2.4× 216 1.9× 28 0.4× 164 2.5× 155 3.3× 19 721
Péter Kovács Hungary 9 440 3.0× 318 2.7× 14 0.2× 79 1.2× 82 1.7× 49 678
Taikang Ning United States 10 173 1.2× 129 1.1× 95 1.4× 40 0.6× 48 1.0× 66 451
Shovan Barma India 13 206 1.4× 77 0.7× 58 0.9× 28 0.4× 54 1.1× 48 407
Anurag Nishad India 7 167 1.1× 85 0.7× 9 0.1× 33 0.5× 45 1.0× 13 348

Countries citing papers authored by Foad Ghaderi

Since Specialization
Citations

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

Fields of papers citing papers by Foad Ghaderi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Foad Ghaderi

This figure shows the co-authorship network connecting the top 25 collaborators of Foad Ghaderi. A scholar is included among the top collaborators of Foad Ghaderi 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 Foad Ghaderi. Foad Ghaderi 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.
Charkari, Nasrollah Moghadam, et al.. (2024). A meta-learning based approach for temporal link prediction in multiplex networks. Knowledge-Based Systems. 309. 112803–112803. 1 indexed citations
2.
Ghaderi, Foad, et al.. (2024). Dental Caries diagnosis from bitewing images using convolutional neural networks. BMC Oral Health. 24(1). 211–211. 6 indexed citations
3.
Safaei, Ali, et al.. (2024). Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning. Scientific Reports. 14(1). 14951–14951. 6 indexed citations
4.
Ghaderi, Foad, et al.. (2024). Dynamic Functional Network Connectivity Analysis in Autism Spectrum Disorder: An EEG Study. IEEE Access. 12. 176719–176727.
5.
Ghaderi, Foad, et al.. (2024). A District-Centric Attention Mechanism Approach for Online Ride-Hailing Demand Forecasting. IEEE Access. 12. 141190–141197. 1 indexed citations
6.
Ghaderi, Foad, et al.. (2023). EEG-based brain connectivity analysis in autism spectrum disorder: Unraveling the effects of bumetanide treatment. Biomedical Signal Processing and Control. 86. 105054–105054. 5 indexed citations
7.
Ghaderi, Foad, et al.. (2023). Quadrotor Control for Tracking Moving Target, and Dynamic Obstacle Avoidance Based on Potential Field Method. International Journal of Engineering. 36(10). 1720–1732. 4 indexed citations
8.
Ghaderi, Foad, et al.. (2023). Dental caries diagnosis using neural networks and deep learning: a systematic review. Multimedia Tools and Applications. 83(10). 30423–30466. 11 indexed citations
9.
Charkari, Nasrollah Moghadam, et al.. (2021). Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment. Clinical Neurophysiology. 132(10). 2540–2550. 4 indexed citations
10.
Ghaderi, Foad, et al.. (2020). Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study. SHILAP Revista de lepidopterología. 3 indexed citations
11.
Charkari, Nasrollah Moghadam, et al.. (2019). Unsupervised representation learning based on the deep multi-view ensemble learning. Applied Intelligence. 50(2). 562–581. 16 indexed citations
12.
Ghaderi, Foad, Su Kyoung Kim, & Elsa Andrea Kirchner. (2016). A periodic spatio-spectral filter for event-related potentials. Computers in Biology and Medicine. 79. 286–298. 3 indexed citations
13.
Ghaderi, Foad, Su Kyoung Kim, & Elsa Andrea Kirchner. (2013). Effects of eye artifact removal methods on single trial P300 detection, a comparative study. Journal of Neuroscience Methods. 221. 41–47. 30 indexed citations
14.
Ghaderi, Foad, H. Mohseni, & Saeid Sanei. (2010). A fast second order blind identification method for separation of periodic sources. View. 1572–1576. 2 indexed citations
15.
Makkiabadi, Bahador, Foad Ghaderi, & Saeid Sanei. (2010). A new tensor factorization approach for convolutive blind source separation in time domain. Surrey Research Insight Open Access (The University of Surrey). 900–904. 2 indexed citations
16.
Mohseni, H., Foad Ghaderi, Edward L. Wilding, & Saeid Sanei. (2010). Variational Bayes for Spatiotemporal Identification of Event-Related Potential Subcomponents. IEEE Transactions on Biomedical Engineering. 57(10). 2413–2428. 6 indexed citations
17.
Ghaderi, Foad, Bahador Makkiabadi, J.G. McWhirter, & Saeid Sanei. (2010). Blind source extraction of cyclostationary sources with common cyclic frequencies. View. 86. 4146–4149. 7 indexed citations
18.
Ghaderi, Foad, H. Mohseni, J.G. McWhirter, & Saeid Sanei. (2009). Blind source extraction of periodic signals. View. 86. 377–380. 6 indexed citations
19.
Abolghasemi, Vahid, et al.. (2009). Segmented compressive sensing. View. 630–633. 7 indexed citations
20.
Makkiabadi, Bahador, et al.. (2009). Semi-blind channel estimation in MIMO commumication by tensor factorization. View. 313–316. 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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