Alain B. Zemkoho

870 total citations
35 papers, 500 citations indexed

About

Alain B. Zemkoho is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Control and Systems Engineering. According to data from OpenAlex, Alain B. Zemkoho has authored 35 papers receiving a total of 500 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 11 papers in Numerical Analysis and 10 papers in Control and Systems Engineering. Recurrent topics in Alain B. Zemkoho's work include Optimization and Variational Analysis (17 papers), Advanced Optimization Algorithms Research (11 papers) and Optimization and Mathematical Programming (10 papers). Alain B. Zemkoho is often cited by papers focused on Optimization and Variational Analysis (17 papers), Advanced Optimization Algorithms Research (11 papers) and Optimization and Mathematical Programming (10 papers). Alain B. Zemkoho collaborates with scholars based in United Kingdom, Germany and United States. Alain B. Zemkoho's co-authors include Stephan Dempe, Boris S. Mordukhovich, Nazih Abderrazzak Gadhi, Shenglong Zhou, Andreas Fischer, Charles Kamhoua, Laurent Njilla, Stefano Coniglio, Lateef Olakunle Jolaoso and Paul R. Roberts and has published in prestigious journals such as IEEE Access, Mathematical Programming and Computer Networks.

In The Last Decade

Alain B. Zemkoho

29 papers receiving 484 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alain B. Zemkoho United Kingdom 12 321 200 167 77 71 35 500
Yo Ishizuka Japan 10 364 1.1× 328 1.6× 226 1.4× 106 1.4× 33 0.5× 19 630
B. Bank India 7 495 1.5× 214 1.1× 336 2.0× 133 1.7× 97 1.4× 12 743
Gui-Hua Lin China 16 508 1.6× 310 1.6× 370 2.2× 215 2.8× 16 0.2× 73 775
Daoli Zhu China 11 250 0.8× 179 0.9× 138 0.8× 26 0.3× 54 0.8× 39 592
Andrew Eberhard Australia 12 156 0.5× 101 0.5× 109 0.7× 62 0.8× 19 0.3× 71 431
Dingju Zhu China 10 314 1.0× 117 0.6× 171 1.0× 36 0.5× 49 0.7× 21 421
Patrice Marcotte Canada 19 599 1.9× 368 1.8× 329 2.0× 63 0.8× 55 0.8× 44 1.1k
Altannar Chinchuluun United States 9 265 0.8× 204 1.0× 167 1.0× 46 0.6× 26 0.4× 21 461
I‎. ‎M‎. Stancu-Minasian Romania 17 547 1.7× 714 3.6× 336 2.0× 346 4.5× 17 0.2× 43 1.0k
Refail Kasımbeyli Türkiye 13 256 0.8× 60 0.3× 219 1.3× 70 0.9× 20 0.3× 40 480

Countries citing papers authored by Alain B. Zemkoho

Since Specialization
Citations

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

Fields of papers citing papers by Alain B. Zemkoho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alain B. Zemkoho. 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 Alain B. Zemkoho. The network helps show where Alain B. Zemkoho may publish in the future.

Co-authorship network of co-authors of Alain B. Zemkoho

This figure shows the co-authorship network connecting the top 25 collaborators of Alain B. Zemkoho. A scholar is included among the top collaborators of Alain B. Zemkoho 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 Alain B. Zemkoho. Alain B. Zemkoho 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.
Jolaoso, Lateef Olakunle, et al.. (2025). Scholtes Relaxation Method for Pessimistic Bilevel Optimization. Set-Valued and Variational Analysis. 33(2).
2.
Zemkoho, Alain B., et al.. (2024). Adaptive learning-based hybrid recommender system for deception in Internet of Thing. Computer Networks. 255. 110853–110853. 2 indexed citations
3.
Jolaoso, Lateef Olakunle, Patrick Mehlitz, & Alain B. Zemkoho. (2024). A fresh look at nonsmooth Levenberg–Marquardt methods with applications to bilevel optimization. Optimization. 74(12). 2745–2792. 3 indexed citations
4.
Coniglio, Stefano, et al.. (2024). Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening. Indian Pacing and Electrophysiology Journal. 24(4). 192–199. 2 indexed citations
5.
Fliege, Jörg, et al.. (2024). Trajectory optimization of unmanned aerial vehicles in the electromagnetic environment. Optimization and Engineering. 26(1). 159–198. 1 indexed citations
6.
Zemkoho, Alain B., et al.. (2024). Two-Layer Deception Model Based on Signaling Games Against Cyber Attacks on Cyber-Physical Systems. IEEE Access. 12. 171559–171570.
7.
Zemkoho, Alain B., et al.. (2024). Bayesian Game for Cyber Deception Against Remote Attack on Automotive Systems. ePrints Soton (University of Southampton). 387–393.
8.
Delanerolle, Gayathri, et al.. (2023). Synthetic data & the future of Women’s Health: A synergistic relationship. International Journal of Medical Informatics. 179. 105238–105238.
9.
Coniglio, Stefano, et al.. (2023). Correlation analysis of deep learning methods in S‐ICD screening. Annals of Noninvasive Electrocardiology. 28(4). e13056–e13056. 2 indexed citations
10.
Zemkoho, Alain B., et al.. (2023). AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices. ePrints Soton (University of Southampton). 1–6. 11 indexed citations
11.
Coniglio, Stefano, et al.. (2023). Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator. Annals of Operations Research. 328(1). 309–335. 1 indexed citations
12.
Zemkoho, Alain B.. (2022). A Basic Time Series Forecasting Course with Python. Operations Research Forum. 4(1). 5 indexed citations
13.
Coniglio, Stefano, et al.. (2022). Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure. Annals of Noninvasive Electrocardiology. 28(1). e13028–e13028. 3 indexed citations
14.
Coniglio, Stefano, et al.. (2022). Deep learning-based insights on T:R ratio behaviour during prolonged screening for S-ICD eligibility. Journal of Interventional Cardiac Electrophysiology. 68(7). 1387–1397. 3 indexed citations
15.
Zemkoho, Alain B. & Shenglong Zhou. (2021). Theoretical and numerical comparison of the Karush–Kuhn–Tucker and value function reformulations in bilevel optimization. Computational Optimization and Applications. 78(2). 625–674. 12 indexed citations
16.
Dempe, Stephan & Alain B. Zemkoho. (2020). Bilevel optimization: advances and next challenges. Springer eBooks. 29 indexed citations
17.
Dempe, Stephan, Boris S. Mordukhovich, & Alain B. Zemkoho. (2012). Necessary optimality conditions in pessimistic bilevel programming. Optimization. 63(4). 505–533. 62 indexed citations
18.
Dempe, Stephan, Boris S. Mordukhovich, & Alain B. Zemkoho. (2012). Sensitivity Analysis for Two-Level Value Functions with Applications to Bilevel Programming. SIAM Journal on Optimization. 22(4). 1309–1343. 31 indexed citations
19.
Dempe, Stephan & Alain B. Zemkoho. (2011). On the Karush–Kuhn–Tucker reformulation of the bilevel optimization problem. Nonlinear Analysis. 75(3). 1202–1218. 55 indexed citations
20.
Dempe, Stephan & Alain B. Zemkoho. (2010). The Generalized Mangasarian-Fromowitz Constraint Qualification and Optimality Conditions for Bilevel Programs. Journal of Optimization Theory and Applications. 148(1). 46–68. 47 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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