shallu juneja

@cse.mait.ac.in

Assistant Professor, CSE
MAIT affiliated to Guru Gobind Singh Indraprastha University , Delhi

shallu juneja


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https://researchid.co/shallujuneja
Ms. Shallu Juneja is an academician with sixteen years of experience in research, development and teaching in computer science and engineering. Her research interests include software fault prediction, software evolution, operating system, model-driven development, data science and machine learning. Ms. Juneja has published around 17 research papers in reputed international journals and conferences. She has authored a book titled ‘Network Technologies’. She was awarded the ‘Best Paper Award 2022’ in ADSSS-2022. She is serving as reviewer for various conferences and journals from reputed publishers like Elsevier, Scopus indexed journals, IEEE etc. She has received Letter of Appreciation for hard work, commitment and excellent team work for the award of NBA Accreditation at MAIT in 2019 etc.

EDUCATION

Ph.D. Pursuing (CSE), M.Tech. (CSE), B.E. (CSE)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science Applications, Information Systems, Software
7

Scopus Publications

15

Scholar Citations

3

Scholar h-index

Scopus Publications

  • CRF_LSTM_DO: automated software bug detection deep learning framework
    Shallu Juneja, Gurjit Singh Bhathal, Brahmaleen Kaur Sidhu
    International Journal of Information Technology Singapore, 2025
  • Bio-Inspired Optimization Algorithm in Machine Learning and Practical Applications
    Shallu Juneja, Harsh Taneja, Ashish Patel, Yogesh Jadhav, Anita Saroj
    SN Computer Science, 2024
  • Development of optimised software fault prediction model using machine learning
    Shallu Juneja, Gurjit Singh Bhathal, Brahmaleen K. Sidhu
    Intelligent Decision Technologies, 2024
    Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony Optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.
  • Sentiment Analysis for Citizen Feedback in Smart Cities with XLNet-BiLSTM: Delhi Metro as a Case Study★
    Ceur Workshop Proceedings, 2024
  • Current Trends and Literature Review of Machine Learning Models for Predicting Software Fault Based onTextual and Numeric Data
    Shallu Juneja, Gurjit Singh Bhathal, Brahmaleen K. Sidhu
    Aip Conference Proceedings, 2023
  • Analysis and Study of Bug Classification Quintessence and Techniques for Forecasting Software Faults
    Shallu Juneja, Gurjit Singh Bhathal, Brahmaleen K. Sidhu
    Lecture Notes in Networks and Systems, 2023
  • Art-based rendering of digital images using texture transfer algorithm
    International Journal of Scientific and Technology Research, 2020

RECENT SCHOLAR PUBLICATIONS

  • CRF_LSTM_DO: automated software bug detection deep learning framework
    S Juneja, GS Bhathal, BK Sidhu
    International Journal of Information Technology, 1-8 , 2025
    2025.0
    Citations: 1
  • Bio-inspired optimization algorithm in machine learning and practical applications
    S Juneja, H Taneja, A Patel, Y Jadhav, A Saroj
    SN Computer Science 5 (8), 1081 , 2024
    2024.0
    Citations: 4
  • Development of optimised software fault prediction model using machine learning
    S Juneja, GS Bhathal, BK Sidhu
    Intelligent Decision Technologies 18 (2), 1355-1376 , 2024
    2024.0
    Citations: 1
  • Current trends and literature review of machine learning models for predicting software fault based on textual and numeric data
    S Juneja, GS Bhathal, BK Sidhu
    AIP Conference Proceedings 2916 (1), 030007 , 2023
    2023.0
  • SummarizeAI-Summarization of the podcasts
    D Khanna, R Bhushan, K Goel, S Juneja
    Proceedings of the International Conference on Innovative Computing … , 2023
    2023.0
    Citations: 4
  • Analysis and Study of Bug Classification Quintessence and Techniques for Forecasting Software Faults
    S Juneja, GS Bhathal, BK Sidhu
    International Conference on Data Analytics & Management, 495-511 , 2023
    2023.0
  • Analyzing and rating greenness of nature-inspired algorithms
    K Garg, C Jindal, S Kumar, S Juneja
    Proceedings of the International Conference on Innovative Computing … , 2022
    2022.0
    Citations: 4
  • Comparing Classification Models for Predicting Liver Diseases
    M Wadhwa, S Juneja
    International Journal of Computer Science and Mobile Computing 7 (4), 135-140 , 2018
    2018.0
    Citations: 1
  • Computational Analysis of RNA Nucleotide Sequences
    S Juneja, D Mukherjee, S Garg

MOST CITED SCHOLAR PUBLICATIONS

  • Bio-inspired optimization algorithm in machine learning and practical applications
    S Juneja, H Taneja, A Patel, Y Jadhav, A Saroj
    SN Computer Science 5 (8), 1081 , 2024
    2024.0
    Citations: 4
  • SummarizeAI-Summarization of the podcasts
    D Khanna, R Bhushan, K Goel, S Juneja
    Proceedings of the International Conference on Innovative Computing … , 2023
    2023.0
    Citations: 4
  • Analyzing and rating greenness of nature-inspired algorithms
    K Garg, C Jindal, S Kumar, S Juneja
    Proceedings of the International Conference on Innovative Computing … , 2022
    2022.0
    Citations: 4
  • CRF_LSTM_DO: automated software bug detection deep learning framework
    S Juneja, GS Bhathal, BK Sidhu
    International Journal of Information Technology, 1-8 , 2025
    2025.0
    Citations: 1
  • Development of optimised software fault prediction model using machine learning
    S Juneja, GS Bhathal, BK Sidhu
    Intelligent Decision Technologies 18 (2), 1355-1376 , 2024
    2024.0
    Citations: 1
  • Comparing Classification Models for Predicting Liver Diseases
    M Wadhwa, S Juneja
    International Journal of Computer Science and Mobile Computing 7 (4), 135-140 , 2018
    2018.0
    Citations: 1
  • Current trends and literature review of machine learning models for predicting software fault based on textual and numeric data
    S Juneja, GS Bhathal, BK Sidhu
    AIP Conference Proceedings 2916 (1), 030007 , 2023
    2023.0
  • Analysis and Study of Bug Classification Quintessence and Techniques for Forecasting Software Faults
    S Juneja, GS Bhathal, BK Sidhu
    International Conference on Data Analytics & Management, 495-511 , 2023
    2023.0
  • Computational Analysis of RNA Nucleotide Sequences
    S Juneja, D Mukherjee, S Garg