World's Best Scientists 2026 revealed!
Mohamed A. El-Sharkawi

Mohamed A. El-Sharkawi

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
45
Citations
12154
World Ranking
3465
National Ranking
1278

Computer Science

D-Index
41
Citations
12175
World Ranking
8616
National Ranking
3692

Mohamed A. El-Sharkawi publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Mohamed A. El-Sharkawi sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 186 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Mohamed A. El-Sharkawi D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Mohamed A. El-Sharkawi sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 45 D-Index — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 1995 - IEEE Fellow For contributions to the application neural networks to power systems analysis.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Electrical engineering

The scientist’s investigation covers issues in Artificial neural network, Electric power system, Artificial intelligence, Data mining and Control engineering. His Artificial neural network research is multidisciplinary, incorporating perspectives in Nonlinear programming and Adaptive algorithm. His Power flow study, which is part of a larger body of work in Electric power system, is frequently linked to Data security, bridging the gap between disciplines.

His primary area of study in Artificial intelligence is in the field of Perceptron. His research in Data mining intersects with topics in Stability and Electrical network. His studies deal with areas such as DC motor, Nonlinear system, Control theory and Reference model as well as Control engineering.

His most cited work include:

  • Electric load forecasting using an artificial neural network (1113 citations)
  • Optimal Charging Strategies for Unidirectional Vehicle-to-Grid (555 citations)
  • Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services (366 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial neural network, Artificial intelligence, Electric power system, Control engineering and Control theory. Mohamed A. El-Sharkawi usually deals with Artificial neural network and limits it to topics linked to Data mining and Feature selection. Mohamed A. El-Sharkawi has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.

His research on Electric power system also deals with topics like

  • Stability and related Support vector machine,
  • Decision tree that intertwine with fields like Mathematical optimization. His studies in Control engineering integrate themes in fields like Control system, DC motor and Electronic speed control. His Control theory study incorporates themes from Induction motor, AC power and Rotor.

He most often published in these fields:

  • Artificial neural network (35.00%)
  • Artificial intelligence (30.00%)
  • Electric power system (27.14%)

What were the highlights of his more recent work (between 2007-2018)?

  • Wind power (8.57%)
  • Mathematical optimization (12.14%)
  • Electric power system (27.14%)

In recent papers he was focusing on the following fields of study:

Wind power, Mathematical optimization, Electric power system, Simulation and Control engineering are his primary areas of study. His Wind power research incorporates themes from Stochastic programming, Electricity and Smart grid. Mohamed A. El-Sharkawi interconnects Control theory and Automatic control in the investigation of issues within Electric power system.

His biological study spans a wide range of topics, including Reliability engineering, System on a chip, Demand response and Vehicle-to-grid. His research on Evolutionary computation concerns the broader Artificial intelligence. His work on Artificial neural network, Perceptron and Quadratic classifier as part of general Artificial intelligence research is often related to Gaussian, thus linking different fields of science.

Between 2007 and 2018, his most popular works were:

  • Optimal Charging Strategies for Unidirectional Vehicle-to-Grid (555 citations)
  • Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services (366 citations)
  • Modern heuristic optimization techniques :: theory and applications to power systems (321 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Electrical engineering

Mohamed A. El-Sharkawi focuses on Mathematical optimization, Particle swarm optimization, Demand response, Vehicle-to-grid and Simulation. His study in Artificial intelligence extends to Mathematical optimization with its themes. His work in the fields of Particle swarm optimization, such as Multi-swarm optimization, intersects with other areas such as Operating cost.

His Demand response study combines topics in areas such as Electric vehicle, Load regulation and System on a chip. His Vehicle-to-grid research includes elements of Scheduling and Operations research. His Work research overlaps with other disciplines such as Discount points, Profit maximization, Flexibility, Reliability engineering and Utility system.

Best Publications

  • Electric load forecasting using an artificial neural network

    D.C. Park;M.A. El-Sharkawi;R.J. Marks;L.E. Atlas

  • Modern heuristic optimization techniques :: theory and applications to power systems

    Kwang Y. Lee;Mohamed A. El-Sharkawi

  • Optimal Charging Strategies for Unidirectional Vehicle-to-Grid

    Eric Sortomme;Mohamed A El-Sharkawi

  • Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services

    E. Sortomme;M. A. El-Sharkawi

  • Pareto Multi Objective Optimization

    P. Ngatchou;Anahita Zarei;M.A. El-Sharkawi

  • Modern Heuristic Optimization Techniques

    Kwang Y. Lee;Mohamed A. El-Sharkawi

  • Support vector machines for transient stability analysis of large-scale power systems

    L.S. Moulin;A.P.A. da Silva;M.A. El-Sharkawi;R.J. Marks

  • Identification and control of a DC motor using back-propagation neural networks

    S. Weerasooriya;M.A. El-Sharkawi

  • Optimal Combined Bidding of Vehicle-to-Grid Ancillary Services

    E. Sortomme;M. A. El-Sharkawi

  • Swarm intelligence for routing in communication networks

    I. Kassabalidis;M.A. El-Sharkawi;R.J. Marks;P. Arabshahi

  • Optimal Power Flow for a System of Microgrids with Controllable Loads and Battery Storage

    E. Sortomme;M. A. El-Sharkawi

  • Power System Security Assessment Using Neural Networks: Feature Selection Using Fisher Discrimination

    C.A. Jensen;M.A. El-Sharkawi;Ii. R.J. Marks

  • A performance comparison of trained multilayer perceptrons and trained classification trees

    L. Atlas;J. Connor;D. Park;M. El-Sharkawi

  • Minimum power broadcast trees for wireless networks: integer programming formulations

    A. K. Das;R. J. Marks;M. El-Sharkawi;P. Arabshahi

  • An adaptively trained neural network

    D.C. Park;M.A. El-Sharkawi;R.J. Marks

  • Dynamic security border identification using enhanced particle swarm optimization

    I.N. Kassabalidis;M.A. El-Sharkawi;R.J. Marks;L.S. Moulin

  • Large scale dynamic security screening and ranking using neural networks

    Y. Mansour;A.Y. Chang;J. Tamby;E. Vaahedi

  • Dynamic security contingency screening and ranking using neural networks

    Y. Mansour;E. Vaahedi;M.A. El-Sharkawi

  • Preliminary results on using artificial neural networks for security assessment (of power systems)

    M. Aggoune;M.A. El-Sharkawi;D.C. Park;M.J. Dambourg

  • Inversion of feedforward neural networks: algorithms and applications

    C.A. Jensen;R.D. Reed;R.J. Marks;M.A. El-Sharkawi

Frequent Co-Authors

Robert J. Marks
Robert J. Marks Baylor University
Kwang Y. Lee
Kwang Y. Lee Baylor University
Istvan Erlich
Istvan Erlich University of Duisburg-Essen
Les Atlas
Les Atlas University of Washington
Shyh-Jier Huang
Shyh-Jier Huang National Cheng Kung University
Osama A. Mohammed
Osama A. Mohammed Florida International University
Paul M. Frank
Paul M. Frank University of Duisburg-Essen
Stavros A. Papathanassiou
Stavros A. Papathanassiou National Technical University of Athens
David G. Dorrell
David G. Dorrell University of Turku
Akira Chiba
Akira Chiba Tokyo Institute of Technology

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