Hasan Dağ

49 papers receiving 512 citations

Hasan Dağ's Hit Papers

A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models 2024 · 67 citations
670+1+2Years since publication255075100

Peers

Hasan Dağ
Comparison fields: 5 of 93
  • Signal Processing 71
  • Artificial Intelligence 182
  • Computer Vision and Pattern Recognition 102
  • Numerical Analysis 27
  • Computer Networks and Communications 111
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M. Kraetzl Australia
Geppino Pucci Italy
Pedro Alonso Spain
Preyas Popat United States
Mohamad Afendee Mohamed Malaysia
Wangdong Yang China
Qi Lei China
Siu‐Wai Ho Australia
Alain Bretto France
Jochen Gorski Germany
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Countries citing papers authored by Hasan Dağ

Since Specialization
Citations

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

Fields of papers citing papers by Hasan Dağ

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hasan Dağ, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hasan Dağ Line = papers co-authored together Hasan Dağ links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deepfake detection using deep learning methods: A systematic and comprehensive review
Hit paper breakdown →
2023112
2
A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models
Hit paper breakdown →
202467
3 200340
4 202240
5 201836
6 201935
7 199725
8 200221
9 201217
10
Gravitational search algorithm for post-outage bus voltage magnitude calculations
201016
11
Branch outage solution using particle swarm optimization
200814
12 200912
13 20239
14 20229
15 19978
16 20167
17 20197
18
Iterative methods and parallel computation for power systems
19966
19 20126
20 20205

About Hasan Dağ

Hasan Dağ is a scholar working on Electrical and Electronic Engineering, Information Systems, Artificial Intelligence, Computer Networks and Communications and Computational Theory and Mathematics, having authored 59 papers that have together received 566 indexed citations. Recurring topics across this work include Power System Optimization and Stability (14 papers), Optimal Power Flow Distribution (11 papers), Matrix Theory and Algorithms (9 papers), Network Security and Intrusion Detection (8 papers), Numerical methods for differential equations (7 papers), Spam and Phishing Detection (7 papers), Power System Reliability and Maintenance (7 papers) and Advanced Malware Detection Techniques (6 papers). The work is most often cited by research in Signal Processing (71 citations), Artificial Intelligence (182 citations), Computer Vision and Pattern Recognition (102 citations), Numerical Analysis (27 citations) and Computer Networks and Communications (111 citations). Hasan Dağ has collaborated with scholars based in Türkiye, United States and Germany. Frequent co-authors include Nima Jafari Navimipour, Mehmet Ünal, Arash Heidari, Aydoḡan Özdemir, Oğuzhan Ceylan, A. Semlyen, F.L. Alvarado, M.A. Pai, Gürkan Soykan and F.L. Alvarado. Their work appears in journals such as IEEE Transactions on Power Systems, Parallel Computing, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Cognitive Computation and IEEE Access.

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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