World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
5103
World Ranking
12158
National Ranking
138

Ruchika Malhotra 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 Ruchika Malhotra 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: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Ruchika Malhotra 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 Ruchika Malhotra 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Ruchika Malhotra is a researcher affiliated with Delhi Technological University in India, with a primary focus on computer science. Their work spans several subfields including information systems, software, artificial intelligence, computer vision and pattern recognition, and computer networks and communications.

The researcher has contributed extensively to topics related to software engineering, with a notable emphasis on software engineering research, software reliability and analysis, software system performance and reliability, software testing and debugging techniques, imbalanced data classification techniques, vehicle license plate recognition, and handwritten text recognition techniques.

Frequent publication venues for Malhotra include the International Journal of Systems Assurance Engineering and Management, AIP conference proceedings, the 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Soft Computing, and Cluster Computing. These venues reflect a consistent engagement with both theoretical and applied aspects of computing.

Key recent papers authored or co-authored by Ruchika Malhotra include:

  • "Recent advances in deep learning models: a systematic literature review," 2023, Multimedia Tools and Applications
  • "Shifting from traditional engineering education towards competency-based approach: The most recommended approach-review," 2023, Education and Information Technologies
  • "An alumni-based collaborative model to strengthen academia and industry partnership: The current challenges and strengths," 2022, Education and Information Technologies
  • "Software defect prediction using hybrid techniques: a systematic literature review," 2023, Soft Computing
  • "License Plate Recognition System using Yolov5 and CNN," 2022, 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)

Among frequent collaborators are Marouane Kessentini, Kusum Lata, Shweta Meena, Maru Tesfaye Addis, and Priya Singh, indicating active cooperation across multiple research efforts.

In addition to articles, Malhotra has contributed to book publications, including a volume titled "High Performance Computing, Smart Devices and Networks," published by Springer Science+Business Media in 2023.

Best Publications

  • A systematic review of machine learning techniques for software fault prediction

    Ruchika Malhotra

  • Empirical Study of Object-Oriented Metrics

    K. K. Aggarwal;Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Empirical validation of object-oriented metrics for predicting fault proneness models

    Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality

    Ruchika Malhotra;Ankita Jain

  • Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study

    K. K. Aggarwal;Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Software reuse metrics for object-oriented systems

    K.K. Aggarwal;Y. Singh;A. Kaur;R. Malhotra

  • An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data

    Ruchika Malhotra;Shine Kamal

  • Empirical Research in Software Engineering: Concepts, Analysis, and Applications

    Ruchika Malhotra

  • Comparative analysis of statistical and machine learning methods for predicting faulty modules

    Ruchika Malhotra

  • Application of Random Forest in Predicting Fault-Prone Classes

    A. Kaur;R. Malhotra

  • Techniques for text classification: Literature review and current trends

    Rajni Jindal;Ruchika Malhotra;Abha Jain

  • Investigation of relationship between object-oriented metrics and change proneness

    Ruchika Malhotra;Megha Khanna

  • Comparative analysis of regression and machine learning methods for predicting fault proneness models

    Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Soft Computing Approaches for Prediction of Software Maintenance Effort

    Arvinder Kaur;Kamaldeep Kaur;Ruchika Malhotra

  • Software Effort Prediction using Statistical and Machine Learning Methods

    Ruchika Malhotra;Ankita Jain

  • Application of Artificial Neural Network for Predicting Maintainability Using Object-Oriented Metrics

    K. K. Aggarwal;Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • An empirical study for software change prediction using imbalanced data

    Ruchika Malhotra;Megha Khanna

  • Software Fault Proneness Prediction Using Support Vector Machines

    Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems

    K. K. Aggarwal;Yogesh Singh;Arvinder Kaur;Ruchika Malhotra

  • Software Maintainability: Systematic Literature Review and Current Trends

    Ruchika Malhotra;Anuradha Chug

  • Empirical validation of object-oriented metrics for predicting fault proneness at different severity levels using support vector machines

    Ruchika Malhotra;Arvinder Kaur;Yogesh Singh

  • An empirical framework for defect prediction using machine learning techniques with Android software

    Ruchika Malhotra

Frequent Co-Authors

Yogesh Singh
Yogesh Singh University of Delhi
S.C. Kaushik
S.C. Kaushik Indian Institute of Technology Delhi

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