World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8226
World Ranking
7956
National Ranking
30

Naif Alajlan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Naif Alajlan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 155 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Naif Alajlan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Naif Alajlan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Naif Alajlan is affiliated with King Saud University in Saudi Arabia and has contributed extensively to research in the fields of Computer Science and Engineering. The majority of their work focuses on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Mechanics of Materials, and Materials Chemistry.

The topics most frequently addressed in Alajlan's research include:

  • Remote-Sensing Image Classification
  • Advanced Image and Video Retrieval Techniques
  • Domain Adaptation and Few-Shot Learning
  • Model Reduction and Neural Networks
  • Numerical methods in engineering
  • Biometric Identification and Security
  • Digital Media Forensic Detection

Naif Alajlan has published numerous papers in a variety of journals. Notable recent papers include:

  • Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (2021, European Journal of Mechanics - A/Solids)
  • Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021, IEEE Access)
  • Parametric deep energy approach for elasticity accounting for strain gradient effects (2021, Computer Methods in Applied Mechanics and Engineering)
  • Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media (2022, Engineering With Computers)
  • Computational Modeling of Flexoelectricity-A Review (2020, Energies)

The researcher collaborates frequently with several coauthors, including:

  • Yakoub Bazi
  • Timon Rabczuk
  • Haikel Alhichri
  • Xiaoying Zhuang
  • Nassim Ammour

Alajlan's work appears regularly in multiple publication venues, notably:

  • Applied Sciences
  • Engineering With Computers
  • Remote Sensing
  • Computers, materials & continua/Computers, materials & continua (Print)
  • IEEE Geoscience and Remote Sensing Letters

Best Publications

  • Deep learning approach for active classification of electrocardiogram signals

    M.M. Al Rahhal;Yakoub Bazi;Haikel AlHichri;Naif Alajlan

  • Artificial neural network methods for the solution of second order boundary value problems

    Cosmin Anitescu;Elena Atroshchenko;Naif Alajlan;Timon Rabczuk

  • Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

    Xiaoying Zhuang;Xiaoying Zhuang;Hongwei Guo;Naif Alajlan;Hehua Zhu

  • Shape retrieval using triangle-area representation and dynamic space warping

    Naif Alajlan;Ibrahim El Rube;Mohamed S. Kamel;George Freeman

  • Deep Learning Approach for Car Detection in UAV Imagery

    Nassim Ammour;Haikel Salem Alhichri;Yakoub Bazi;Bilel Benjdira

  • Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

    Haikel Alhichri;Asma S. Alswayed;Yakoub Bazi;Nassim Ammour

  • Sensitivity and uncertainty analysis for flexoelectric nanostructures

    Khader M. Hamdia;Hamid Ghasemi;Xiaoying Zhuang;Naif Alajlan

  • A wavelet optimization approach for ECG signal classification

    Abdelhamid Daamouche;Latifa Hamami;Naif Alajlan;Farid Melgani

  • Parametric deep energy approach for elasticity accounting for strain gradient effects

    Vien Minh Nguyen-Thanh;Cosmin Anitescu;Naif Alajlan;Timon Rabczuk

  • Geometry-Based Image Retrieval in Binary Image Databases

    N. Alajlan;M.S. Kamel;G.H. Freeman

  • Approximate reasoning with generalized orthopair fuzzy sets

    Ronald R. Yager;Naif Alajlan

  • Domain Adaptation Network for Cross-Scene Classification

    Esam Othman;Yakoub Bazi;Farid Melgani;Haikel Alhichri

  • Using convolutional features and a sparse autoencoder for land-use scene classification

    Esam Othman;Yakoub Bazi;Naif Alajlan;Haikel Alhichri

  • Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

    Yakoub Bazi;Naif Alajlan;Farid Melgani;Haikel AlHichri

  • Detail preserving impulsive noise removal

    Naif Alajlan;Mohamed Kamel;Ed Jernigan

  • Efficient Framework for Palm Tree Detection in UAV Images

    Salim Malek;Yakoub Bazi;Naif Alajlan;Haikel AlHichri

  • Aspects of generalized orthopair fuzzy sets

    Ronald R. Yager;Naif Alajlan;Yakoub Bazi

  • Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

    Naif Alajlan;Yakoub Bazi;Farid Melgani;Ronald R. Yager

  • Mechanical responses of pristine and defective C3N nanosheets studied by molecular dynamics simulations

    A.H.N. Shirazi;R. Abadi;M. Izadifar;N. Alajlan

  • Land-Use Classification With Compressive Sensing Multifeature Fusion

    Mohamed L. Mekhalfi;Farid Melgani;Yakoub Bazi;Naif Alajlan

  • Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification

    Yakoub Bazi;Mohamad Mahmoud Al Rahhal;Haikel Al-Hichri;Naif Alajlan

Frequent Co-Authors

Yakoub Bazi
Yakoub Bazi King Saud University
Ronald R. Yager
Ronald R. Yager Iona University
Timon Rabczuk
Timon Rabczuk Bauhaus University, Weimar
Farid Melgani
Farid Melgani University of Trento
Xiaoying Zhuang
Xiaoying Zhuang University of Hannover
Mohamed S. Kamel
Mohamed S. Kamel University of Waterloo
Hung Nguyen-Xuan
Hung Nguyen-Xuan Ho Chi Minh City University of Technology
Hehua Zhu
Hehua Zhu Tongji University
Paul Fieguth
Paul Fieguth University of Waterloo
Harold S. Park
Harold S. Park Boston University

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