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Computer Science

D-Index
34
Citations
5103
World Ranking
12160
National Ranking
138

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

S.C. Kaushik
S.C. Kaushik Indian Institute of Technology Delhi

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