World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
14819
World Ranking
8587
National Ranking
263

Iqbal H. Sarker 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 Iqbal H. Sarker 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: 132 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

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

Iqbal H. Sarker 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 Iqbal H. Sarker 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Iqbal H. Sarker is affiliated with Edith Cowan University in Australia and works primarily in the field of Computer Science. Their research spans several subfields, including Artificial Intelligence, Information Systems, Computer Networks and Communications, Signal Processing, as well as Sociology and Political Science.

The scientist's work covers a variety of research topics, notably:

  • Network Security and Intrusion Detection
  • Advanced Malware Detection Techniques
  • Anomaly Detection Techniques and Applications
  • Misinformation and Its Impacts
  • Sentiment Analysis and Opinion Mining
  • Spam and Phishing Detection
  • Topic Modeling

Among recent publications are:

  • Machine Learning: Algorithms, Real-World Applications and Research Directions, 2021, SN Computer Science
  • Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions, 2021, SN Computer Science
  • AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems, 2022, SN Computer Science
  • Cybersecurity Data Science: An Overview from Machine Learning Perspective, 2020, Journal Of Big Data
  • AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions, 2021, SN Computer Science

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • Preprints.org
  • SN Computer Science
  • IEEE Access
  • Annals of Data Science

Iqbal H. Sarker often collaborates with a group of recurring co-authors, including Mohammed Moshiul Hoque, Helge Janicke, Md Musfique Anwar, Muhammad Nazrul Islam, and Λέανδρος Μαγλαράς.

Best Publications

  • Machine Learning: Algorithms, Real-World Applications and Research Directions

    Iqbal H. Sarker

  • Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

    Iqbal H. Sarker;Iqbal H. Sarker

  • Cybersecurity data science: an overview from machine learning perspective

    Iqbal H. Sarker;Iqbal H. Sarker;A. S. M. Kayes;Shahriar Badsha;Hamed AlQahtani

  • Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

    Iqbal H. Sarker;Iqbal H. Sarker

  • AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

    Iqbal H. Sarker;Iqbal H. Sarker;Hasan Furhad;Raza Nowrozy

  • IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model

    Iqbal H. Sarker;Yoosef B. Abushark;Fawaz Alsolami;Asif Irshad Khan

  • Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

    Unknown

  • Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

    Iqbal H. Sarker;Iqbal H. Sarker;A. S. M. Kayes;Paul A. Watters

  • Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

    Iqbal H. Sarker;Iqbal H. Sarker

  • BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

    Iqbal H. Sarker;Iqbal H. Sarker;Alan Colman;Jun Han;Asif Irshad Khan

  • Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects

    Unknown

  • Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

    Iqbal H. Sarker;Mohammed Moshiul Hoque;Kafil Uddin;Tawfeeq Alsanoosy

  • Cyber intrusion detection using machine learning classification techniques

    Hamed Alqahtani;Iqbal H. Sarker;Asra Kalim;Syed Md. Minhaz Hossain;Syed Md. Minhaz Hossain

  • AquaVision: Automating the detection of waste in water bodies using deep transfer learning

    Harsh Panwar;P.K. Gupta;Mohammad Khubeb Siddiqui;Ruben Morales-Menendez

  • Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus

    Md. Faisal Faruque;Asaduzzaman;Iqbal H. Sarker

  • CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks

    Iqbal H. Sarker;Iqbal H. Sarker

  • Context-aware rule learning from smartphone data: survey, challenges and future directions

    Iqbal H. Sarker;Iqbal H. Sarker

  • A machine learning based robust prediction model for real-life mobile phone data

    Iqbal H. Sarker;Iqbal H. Sarker

  • A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues.

    A. S. M. Kayes;Rudri Kalaria;Iqbal H. Sarker;Md. Saiful Islam

  • A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic

    Muhammad Nazrul Islam;Toki Tahmid Inan;Suzzana Rafi;Syeda Sabrina Akter

  • Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

    Iqbal H. Sarker;Alan Colman;Muhammad Ashad Kabir;Jun Han

  • AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

    Iqbal H. Sarker

Frequent Co-Authors

Jun Han
Jun Han Swinburne University of Technology
Paul A. Watters
Paul A. Watters La Trobe University
Khaled Salah
Khaled Salah Khalifa University
S. M. Riazul Islam
S. M. Riazul Islam University of Aberdeen
A. K. M. Najmul Islam
A. K. M. Najmul Islam Lappeenranta University of Technology
Pietro Liò
Pietro Liò University of Cambridge
Jun Han
Jun Han Swinburne University of Technology
Mohammad Hammoudeh
Mohammad Hammoudeh King Fahd University of Petroleum and Minerals

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