Ferhat Uçar

1.2k total citations · 1 hit paper
29 papers, 811 citations indexed

About

Ferhat Uçar is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Ferhat Uçar has authored 29 papers receiving a total of 811 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 12 papers in Electrical and Electronic Engineering and 6 papers in Control and Systems Engineering. Recurrent topics in Ferhat Uçar's work include Power Quality and Harmonics (6 papers), Machine Learning and ELM (5 papers) and Energy Load and Power Forecasting (4 papers). Ferhat Uçar is often cited by papers focused on Power Quality and Harmonics (6 papers), Machine Learning and ELM (5 papers) and Energy Load and Power Forecasting (4 papers). Ferhat Uçar collaborates with scholars based in Türkiye, United States and Norway. Ferhat Uçar's co-authors include Deniz Korkmaz, Beşir Dandıl, Ömer Faruk Alçin, Reza Arghandeh, Hakan Açıkgöz, Sami Ekici, Mert Gürtürk, Turan Gürgenç, Cihan Özel and Muhammet Baykara and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Hydrogen Energy and Energy.

In The Last Decade

Ferhat Uçar

25 papers receiving 769 citations

Hit Papers

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis ... 2020 2026 2022 2024 2020 100 200 300 400 500
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.

  1. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
    2020 547 citations Ferhat Uçar, Deniz Korkmaz Medical Hypotheses profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ferhat Uçar Türkiye 12 468 402 177 106 106 29 811
Almas Abbasi Pakistan 10 153 0.3× 129 0.3× 103 0.6× 186 1.8× 29 0.3× 29 702
Hongbo Zhu China 11 89 0.2× 127 0.3× 96 0.5× 65 0.6× 23 0.2× 40 421
Mohammad Belayet Hossain Australia 8 171 0.4× 140 0.3× 94 0.5× 57 0.5× 31 0.3× 15 372
Amanullah Asraf Bangladesh 4 375 0.8× 277 0.7× 22 0.1× 90 0.8× 56 0.5× 4 553
Md. Zabirul Islam Bangladesh 3 365 0.8× 271 0.7× 22 0.1× 90 0.8× 54 0.5× 5 545
Pandia Rajan Jeyaraj India 13 147 0.3× 193 0.5× 146 0.8× 106 1.0× 23 0.2× 31 690
Warda M. Shaban Egypt 11 187 0.4× 232 0.6× 48 0.3× 58 0.5× 12 0.1× 23 507
Amir Mosavi Hungary 10 106 0.2× 200 0.5× 46 0.3× 115 1.1× 18 0.2× 20 509
M. Srinivas India 12 114 0.2× 156 0.4× 69 0.4× 45 0.4× 10 0.1× 48 438
Hédi Sakli Tunisia 12 109 0.2× 136 0.3× 289 1.6× 93 0.9× 17 0.2× 91 839

Countries citing papers authored by Ferhat Uçar

Since Specialization
Citations

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

Fields of papers citing papers by Ferhat Uçar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ferhat Uçar

This figure shows the co-authorship network connecting the top 25 collaborators of Ferhat Uçar. A scholar is included among the top collaborators of Ferhat Uçar 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 Ferhat Uçar. Ferhat Uçar 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.
2.
Gürtürk, Mert, et al.. (2024). Solar energy technical feasibility comparison: an alternative proposal for the Industry. Energy Efficiency. 17(5). 2 indexed citations
3.
Uçar, Ferhat, et al.. (2024). Development of a Production Process for Biomass-Based Activated Carbon via Hydrothermal Carbonization Method. SHILAP Revista de lepidopterología. 3(3). 326–336. 1 indexed citations
4.
5.
Uçar, Ferhat, et al.. (2023). Computational drug discovery on human immunodeficiency virus with a customized long short‐term memory variational autoencoder deep‐learning architecture. CPT Pharmacometrics & Systems Pharmacology. 13(2). 308–316. 11 indexed citations
6.
Uçar, Ferhat. (2023). A Comprehensive Analysis of Smart Grid Stability Prediction along with Explainable Artificial Intelligence. Symmetry. 15(2). 289–289. 16 indexed citations
7.
Uçar, Ferhat, et al.. (2023). An Intelligent Model for Supercapacitors with a Graphene-Based Fractal Electrode to Investigate the Cyclic Voltammetry. Fractal and Fractional. 7(3). 218–218. 8 indexed citations
8.
Gürtürk, Mert, et al.. (2021). A novel approach to investigate the effects of global warming and exchange rate on the solar power plants. Energy. 239. 122344–122344. 16 indexed citations
9.
Uçar, Ferhat & Deniz Korkmaz. (2021). A novel ship classification network with cascade deep features for line-of-sight sea data. Machine Vision and Applications. 32(3). 13 indexed citations
10.
Uçar, Ferhat. (2020). Deep Learning Approach to Cell Classificatio in Human Peripheral Blood. 383–387. 11 indexed citations
11.
Uçar, Ferhat & Deniz Korkmaz. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses. 140. 109761–109761. 547 indexed citations breakdown →
12.
13.
Gürgenç, Turan, et al.. (2019). A study on the extreme learning machine based prediction of machining times of the cycloidal gears in CNC milling machines. Production Engineering. 13(6). 635–647. 16 indexed citations
14.
Uçar, Ferhat, et al.. (2019). Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks. Energies. 12(8). 1449–1449. 11 indexed citations
15.
Uçar, Ferhat, et al.. (2018). Online power quality events detection using weighted Extreme Learning Machine. 39–43. 4 indexed citations
16.
Uçar, Ferhat, et al.. (2018). Power Quality Event Detection Using a Fast Extreme Learning Machine. Energies. 11(1). 145–145. 48 indexed citations
17.
Uçar, Ferhat, et al.. (2018). Local Binary Patterns and Extreme Learning Machine based Texture Classification of Marbles. 1–6. 1 indexed citations
18.
Alçin, Ömer Faruk, Ferhat Uçar, & Deniz Korkmaz. (2016). Extreme learning machine based robotic arm modeling. 1160–1163. 7 indexed citations
19.
Uçar, Ferhat, et al.. (2015). Classification of power quality events using extreme learning machine. 970–973. 4 indexed citations
20.
Uçar, Ferhat, et al.. (2012). Three Level Inverter Based Shunt Active Power Filter Using Multi-Level Hysteresis Band Current Controller. PRZEGLĄD ELEKTROTECHNICZNY. 227–231. 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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