Gürsel Serpen

1.3k total citations
66 papers, 886 citations indexed

About

Gürsel Serpen is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Gürsel Serpen has authored 66 papers receiving a total of 886 indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Artificial Intelligence, 23 papers in Computer Networks and Communications and 12 papers in Electrical and Electronic Engineering. Recurrent topics in Gürsel Serpen's work include Neural Networks and Applications (26 papers), Energy Efficient Wireless Sensor Networks (10 papers) and Network Security and Intrusion Detection (9 papers). Gürsel Serpen is often cited by papers focused on Neural Networks and Applications (26 papers), Energy Efficient Wireless Sensor Networks (10 papers) and Network Security and Intrusion Detection (9 papers). Gürsel Serpen collaborates with scholars based in United States and Ghana. Gürsel Serpen's co-authors include Maheshkumar Sabhnani, Jiakai Li, Azadeh Parvin, Steven H. Selman, L.S. Goodenday, Kushal Shah, Krzysztof J. Cios, Cindy Schneider, Diana Shvydka and В. Г. Карпов and has published in prestigious journals such as Nucleic Acids Research, Neurocomputing and Knowledge-Based Systems.

In The Last Decade

Gürsel Serpen

64 papers receiving 814 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gürsel Serpen United States 15 513 449 215 105 97 66 886
M. M. A. Hashem Bangladesh 14 520 1.0× 506 1.1× 266 1.2× 152 1.4× 147 1.5× 70 1.1k
Miao Xie China 17 553 1.1× 669 1.5× 268 1.2× 70 0.7× 157 1.6× 33 1.0k
Thavavel Vaiyapuri Saudi Arabia 19 461 0.9× 535 1.2× 163 0.8× 186 1.8× 163 1.7× 60 1.1k
Jialiang Lu China 16 311 0.6× 487 1.1× 174 0.8× 113 1.1× 162 1.7× 63 1.0k
Mohammed Hasan Ali Iraq 13 324 0.6× 323 0.7× 176 0.8× 118 1.1× 80 0.8× 56 766
Riaz Ullah Khan China 17 407 0.8× 480 1.1× 401 1.9× 127 1.2× 198 2.0× 38 1.0k
P. Deepalakshmi India 19 377 0.7× 560 1.2× 206 1.0× 148 1.4× 273 2.8× 97 1.2k
Murad A. Rassam Yemen 17 411 0.8× 523 1.2× 230 1.1× 81 0.8× 191 2.0× 45 950
S. Velliangiri India 16 378 0.7× 456 1.0× 154 0.7× 183 1.7× 292 3.0× 97 1.1k
Donghwoon Kwon United States 9 585 1.1× 558 1.2× 273 1.3× 60 0.6× 76 0.8× 15 801

Countries citing papers authored by Gürsel Serpen

Since Specialization
Citations

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

Fields of papers citing papers by Gürsel Serpen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gürsel Serpen

This figure shows the co-authorship network connecting the top 25 collaborators of Gürsel Serpen. A scholar is included among the top collaborators of Gürsel Serpen 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 Gürsel Serpen. Gürsel Serpen 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.
Serpen, Gürsel, et al.. (2024). Building an intrusion detection system on UNSW‐NB15: Reducing the margin of error to deal with data overlap and imbalance. Concurrency and Computation Practice and Experience. 36(25). 1 indexed citations
2.
Serpen, Gürsel, et al.. (2023). UNSW‐NB15 computer security dataset: Analysis through visualization. Security and Privacy. 7(1). 25 indexed citations
3.
Serpen, Gürsel, et al.. (2012). Global-Local Hybrid Ensemble Classifier for KDD 2004 Cup Particle Physics Dataset. International Journal of Machine Learning and Computing. 231–234. 1 indexed citations
4.
Serpen, Gürsel, et al.. (2012). Hybrid random subsample classifier ensemble for high dimensional data sets. International Journal of Hybrid Intelligent Systems. 9(2). 91–103. 2 indexed citations
5.
Serpen, Gürsel, et al.. (2012). Performance of global–local hybrid ensemble versus boosting and bagging ensembles. International Journal of Machine Learning and Cybernetics. 4(4). 301–317. 17 indexed citations
6.
Shepard, Samuel S., et al.. (2012). Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models. Nucleic Acids Research. 40(11). 4765–4773. 1 indexed citations
7.
Li, Jiakai, et al.. (2010). Bayes Net Classifiers For Prediction Of Renal Graft Status And Survival Period. Zenodo (CERN European Organization for Nuclear Research). 12 indexed citations
8.
Serpen, Gürsel, et al.. (2009). Large Experiment and Evaluation Tool for WEKA Classifiers.. 340–346. 11 indexed citations
9.
Serpen, Gürsel, et al.. (2008). Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey. Artificial Intelligence and Law. 16(3). 245–276. 8 indexed citations
10.
Serpen, Gürsel, et al.. (2007). A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis. Computers in Biology and Medicine. 38(2). 204–220. 22 indexed citations
11.
Serpen, Gürsel, et al.. (2005). AN ADAPTIVE CONSTRAINT SATISFACTION NETWORK. 1. 163–163. 1 indexed citations
12.
Serpen, Gürsel, et al.. (2004). Theoretical Exploration on Local Stability of Simul taneous Recurrent Neural Network Dynamics for Static Combinatorial Optimization. 1 indexed citations
13.
Sabhnani, Maheshkumar & Gürsel Serpen. (2003). Formulation of a Heuristic Rule for Misuse and Anomaly Detection for U2R Attacks in Solaris Operating System Environment.. Security and Management. 390–396. 8 indexed citations
14.
Sabhnani, Maheshkumar & Gürsel Serpen. (2003). Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context.. 209–215. 186 indexed citations
15.
Sabhnani, Maheshkumar & Gürsel Serpen. (2003). KDD Feature Set Complaint Heuristic Rules for R2L Attack Detection.. Security and Management. 1030. 310–316. 19 indexed citations
16.
Serpen, Gürsel, et al.. (2003). Simultaneous recurrent neural network trained with non-recurrent backpropagation algorithm for static optimisation. Neural Computing and Applications. 12(1). 1–9. 22 indexed citations
17.
Serpen, Gürsel, et al.. (2003). Stability of simultaneous recurrent neural network dynamics for static optimization. 1. 2023–2028. 2 indexed citations
18.
Serpen, Gürsel, et al.. (2002). PASSPHRASE AUTHENTICATION BASED ON TYPING STYLE THROUGH AN ART 2 NEURAL NETWORK. International Journal of Computational Intelligence and Applications. 2(2). 131–152. 10 indexed citations
19.
Serpen, Gürsel, et al.. (2000). Determination of weights for relaxation recurrent neural networks. Neurocomputing. 34(1-4). 145–168. 12 indexed citations
20.
Serpen, Gürsel, et al.. (1994). Analysis of the relationship between weight parameters and stability of solutions in Hopfield networks form dynamic systems viewpoint. Neural, Parallel & Scientific Computations archive. 2(3). 361–372. 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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