World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
8953
World Ranking
5922
National Ranking
2673

Andrew H. Sung 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 Andrew H. Sung 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: 196 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

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

Andrew H. Sung 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 Andrew H. Sung 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Andrew H. Sung is affiliated with the University of Southern Mississippi in the United States. Their research spans several key areas within computer science and medicine, with a focus on advanced techniques in malware detection, network security, and artificial intelligence applications.

Their recent publications include the following:

  • Deepfake Detection: A Systematic Literature Review, 2022, IEEE Access
  • Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection, 2020, Vietnam Journal of Computer Science
  • Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction, 2023, arXiv (Cornell University)
  • Machine Unlearning using a Multi-GAN based Model, 2024, arXiv (Cornell University)
  • Machine unlearning using a Multi-GaN based model, 2024, AIP conference proceedings

The scientist's frequent collaborators include Md. Shohel Rana, Amartya Hatua, Trung T. Nguyen, Mohammad Nur Nobi, and Beddhu Murali.

The main fields of study for Andrew H. Sung are computer science and medicine. Within computer science, they have contributed notably to these subfields:

  • Artificial Intelligence
  • Signal Processing
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

The topics covered in their research encompass a range of technological domains and methodologies, including:

  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection
  • Neural Networks and Applications
  • Digital Media Forensic Detection
  • Generative Adversarial Networks and Image Synthesis
  • Anomaly Detection Techniques and Applications
  • Software Testing and Debugging Techniques

Their work has appeared in multiple venues, indicating a focus on both theoretical and applied research. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Access
  • Vietnam Journal of Computer Science
  • AIP conference proceedings
  • Computers, Materials & Continua (Print)

Best Publications

  • Intrusion detection using neural networks and support vector machines

    S. Mukkamala;G. Janoski;A. Sung

  • Identifying important features for intrusion detection using support vector machines and neural networks

    A.H. Sung;S. Mukkamala

  • Intrusion detection using an ensemble of intelligent paradigms

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • Static analyzer of vicious executables (SAVE)

    A.H. Sung;J. Xu;P. Chavez;S. Mukkamala

  • Detection of Phishing Attacks: A Machine Learning Approach

    Ram B. Basnet;Srinivas Mukkamala;Andrew H. Sung

  • Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques.

    Srinivas Mukkamala;Andrew H. Sung

  • Ranking importance of input parameters of neural networks

    A.H. Sung

  • Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines

    Srinivas Mukkamala;Andrew H. Sung

  • Polymorphic malicious executable scanner by API sequence analysis

    J.-Y. Xu;A.H. Sung;P. Chavez;S. Mukkamala

  • Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

    Qingzhong Liu;Andrew H. Sung;Zhongxue Chen;Jianyun Xu

  • Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis

    Qingzhong Liu;A.H. Sung;Mengyu Qiao

  • Predicting injection profiles using ANFIS

    Mingzhen Wei;Baojun Bai;Andrew H. Sung;Qingzhong Liu

  • Modeling intrusion detection systems using linear genetic programming approach

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • Intrusion Detection Using Ensemble of Soft Computing Paradigms

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • The feature selection and intrusion detection problems

    Andrew H. Sung;Srinivas Mukkamala

  • Image complexity and feature mining for steganalysis of least significant bit matching steganography

    Qingzhong Liu;Andrew H. Sung;Bernardete Ribeiro;Mingzhen Wei

  • A comparative study of techniques for intrusion detection

    S. Mukkamala;A.H. Sung

  • Feature Selection for Intrusion Detection using Neural Networks and Support Vector Machines

    Andrew H. Sung;Srinivas Mukkamala

  • Intrusion Detection Systems Using Adaptive Regression Spines

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham;Vitorino Ramos

  • Computationally intelligent agents for distributed intrusion detection system and method of practicing same

    Andrew H. Sung;Srinivas Mukkamala;Jean-Louis Lassez

  • Gene selection and classification for cancer microarray data based on machine learning and similarity measures

    Qingzhong Liu;Andrew H Sung;Zhongxue Chen;Jianzhong Liu

  • Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

    Qingzhong Liu;Andrew H. Sung;Zhongxue Chen;Jianzhong Liu

Frequent Co-Authors

Ajith Abraham
Ajith Abraham Sai University
Xudong Huang
Xudong Huang Harvard University
Xiao Qin
Xiao Qin Auburn University
Bruce M. Psaty
Bruce M. Psaty University of Washington
Gil Atzmon
Gil Atzmon Albert Einstein College of Medicine
Laurence T. Yang
Laurence T. Yang St. Francis Xavier University
Baojun Bai
Baojun Bai Missouri University of Science and Technology
Nicholas J. Schork
Nicholas J. Schork Translational Genomics Research Institute
Pui-Yan Kwok
Pui-Yan Kwok University of California, San Francisco
Alexander P. Reiner
Alexander P. Reiner University of Washington

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