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Academic Experts
Dr. Akansha Singh

Biography

Dr. Akansha Singh is currently serving as an Assistant Professor (Senior Grade) in the Department of Computer Science and Engineering at Jaypee Institute of Information Technology (JIIT), Noida. She earned her Ph.D. in Computer Science and Engineering from Guru Gobind Singh Indraprastha University (GGSIPU), Delhi, in April 2025. She received her B.Tech. and M.Tech. degrees in Computer Science and Engineering from GGSIPU in 2017 and 2020, respectively. She was awarded the Gold Medal for securing the highest academic performance in her M.Tech and qualified the UGC-NET examination in both 2020 and 2021. Before joining JIIT, Dr. Singh worked as an Assistant Professor at KIET Group of Institutions, Ghaziabad. She has 12 research publications to her credit, comprising international conferences and Web of Science–indexed journals. Her research contributions reflect her deep interest in developing intelligent, data-driven solutions for real-world problems. Her primary research areas include Machine Learning, Deep Learning, Image Processing, Data Mining, Computational Metaheuristics, and Bioinformatics. Her doctoral research focused on optimizing machine learning models using hybrid metaheuristic algorithms for disease prediction and multi-class classification problems.

She has received recognition in the form of two Best Paper Awards at both National and International conferences. Additionally, she was awarded the Short Term Research Fellowship (STRF) by GGSIPU and the STEM Fellowship, further affirming her dedication to research excellence. She has reviewed more than 20 SCI/SCIE indexed papers in reputed journals such as IEEE Access, Springer Nature, BMC etc.

Research Highlights

Worked on diverse machine learning models and addressed key issues like overfitting, model bias, and generalization.

  • Studied and implemented various metaheuristic algorithms for feature selection and hyperparameter tuning.
  • Addressed core problems in metaheuristics such as convergence issues, center bias, and local minima, proposing enhanced strategies.
  • Proposed multi-optimization-based ML frameworks, integrating optimization at multiple stages of the pipeline.
  • Explored effective ways of embedding optimization techniques into end-to-end ML workflows.
  • Designed AutoML-inspired models focusing on automated selection and tuning for performance improvement.
  • Tackled major data mining challenges, including class imbalance, missing data, and bias correction.
  • Applied deep learning models, especially CNNs, for processing and analyzing image data.
Areas of Interest
  • Machine Learning
  • Deep Learning
  • Data Mining
  • Computational Metaheuristics
  • Image Processing
Publications

[1] A. Singh, N. Prakash, and A. Jain, “A review on multiclassification of chronic disease based on machine learning and meta‑heuristic techniques,” Data Mining and Knowledge Discovery, Wiley, vol. 15, no. 3, pp.e70030, 2025, doi: https://doi.org/10.1002/widm.70030.
[2] A. Singh, N. Prakash, and A. Jain, “Flose: Flowerwork‑based stacked ensemble framework for classification of chronic diseases,” Evolutionary Intelligence, Springer, vol. 18, no. 84, pp. 1-25, 2025, doi: https://doi.org/10.1007/s12065-025-01072-4
[3] A. Singh, N. Prakash, and A. Jain, “A comparative study of metaheuristic-based machine learning classifiers using non-parametric tests for the detection of COPD severity grade,” J. Inf. Optim. Sci., vol. 44, pp. 1097–1114, 2023.
[4] A. Singh, N. Prakash, and A. Jain, “Particle swarm optimization based random forest framework for the classification of chronic diseases,” IEEE Access, vol. 11, pp. 133931–133946, 2023.
[5] A. Singh and A. Payal, “CAD diagnosis by predicting stenosis in arteries using data mining process,” Intell. Decis. Technol., vol. 15, pp. 59–68, 2021.