Akram Bayat

667 total citations · 1 hit paper
8 papers, 437 citations indexed

About

Akram Bayat is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, Akram Bayat has authored 8 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 2 papers in Human-Computer Interaction. Recurrent topics in Akram Bayat's work include Visual Attention and Saliency Detection (2 papers), Gaze Tracking and Assistive Technology (2 papers) and AI in cancer detection (2 papers). Akram Bayat is often cited by papers focused on Visual Attention and Saliency Detection (2 papers), Gaze Tracking and Assistive Technology (2 papers) and AI in cancer detection (2 papers). Akram Bayat collaborates with scholars based in United States, Malaysia and Iran. Akram Bayat's co-authors include Marc Pomplun, Duc A. Tran, Pratik Shah, Aman Rana, Alarice Lowe, Harrison Tsai, Annacarolina da Silva, Vignesh Shanmugam, Tyler Janovitz and Do Hyong Koh and has published in prestigious journals such as JAMA Network Open and Procedia Computer Science.

In The Last Decade

Akram Bayat

8 papers receiving 420 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Study on Human Activity Recognition Using Accelerometer Data from Smartphones

2014 357 citations Akram Bayat, Marc Pomplun et al. Procedia Computer Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Akram Bayat United States	5	306	152	109	105	44	8	437
Juan Manuel Gálvez Spain	8	362 1.2×	198 1.3×	90 0.8×	139 1.3×	51 1.2×	10	667
Maria Cornacchia United States	5	255 0.8×	116 0.8×	82 0.8×	71 0.7×	47 1.1×	17	390
T.-S. Kim South Korea	9	349 1.1×	158 1.0×	78 0.7×	143 1.4×	100 2.3×	20	575
Michalis Vrigkas Greece	8	393 1.3×	115 0.8×	50 0.5×	205 2.0×	38 0.9×	27	641
François Brémond France	14	676 2.2×	206 1.4×	107 1.0×	278 2.6×	50 1.1×	33	856
Gabriele Civitarese Italy	14	408 1.3×	38 0.3×	141 1.3×	197 1.9×	74 1.7×	41	541
Pau Climent-Pérez Spain	10	465 1.5×	161 1.1×	46 0.4×	191 1.8×	30 0.7×	23	570
Sreenivasan Ramasamy Ramamurthy United States	8	229 0.7×	84 0.6×	63 0.6×	94 0.9×	37 0.8×	20	349
Lukas Köping Germany	6	281 0.9×	116 0.8×	99 0.9×	113 1.1×	87 2.0×	9	385

Countries citing papers authored by Akram Bayat

Since Specialization

Citations

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

Fields of papers citing papers by Akram Bayat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akram Bayat

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

All Works

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8 of 8 papers shown

1.

Bayat, Akram, et al.. (2023). Urban heat island: a primary guide for urban designers. 2(4). 10–23. 2 indexed citations

2.

Bayat, Akram, et al.. (2021). Automated end-to-end deep learning framework for classification and tumor localization from native non-stained pathology images. 6–6. 3 indexed citations

3.

Rana, Aman, Alarice Lowe, Tyler Janovitz, et al.. (2020). Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis. JAMA Network Open. 3(5). e205111–e205111. 52 indexed citations

4.

Bayat, Akram, et al.. (2018). Scene Grammar in Human and Machine Recognition of Objects and Scenes. 2073–20737. 6 indexed citations

5.

Bayat, Akram & Marc Pomplun. (2017). Deriving high-level scene descriptions from deep scene CNN features. 42. 1–6. 4 indexed citations

6.

Bayat, Akram & Marc Pomplun. (2016). The influence of text difficulty level and topic on eye-movement behavior and pupil size during reading. 1–5. 8 indexed citations

7.

Bayat, Akram, Marc Pomplun, & Duc A. Tran. (2014). A Study on Human Activity Recognition Using Accelerometer Data from Smartphones. Procedia Computer Science. 34. 450–457. 357 indexed citations breakdown →

8.

Bayat, Akram, Marc Pomplun, & Duc A. Tran. (2014). The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) A Study on Human Activity Recognition Using Accelerometer Data from Smartphones. 5 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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