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- School School of Engineering and Technology
- Department/Programme Computer Science
- Email manisha.saini@bmu.edu.in
Manisha Saini holds the position of Assistant Professor-III in the School of Engineering and Technology. Her expertise lies in the fields of Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Generative AI, and Natural Language Processing. Throughout her career, Manisha has made significant contributions to both academia and industry. She has served as an Assistant Professor at several institutions, including GD Goenka University and Manav Rachna University. In her most recent roles, she worked as an Artificial Intelligence Researcher at Tech5 India Pvt. Ltd and as an Artificial Intelligence Research Engineer at LENS Corp AI Pvt Ltd. Prior to that, she served as a Senior Computer Vision Engineer at Tooliqa Innovations LLP in Gurgaon, India.
Manisha actively participates in the academic community as a reviewer for multiple international journals, including IEEE Access, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Medical Imaging, among others. She achieved a 96% percentile in the 2016 Graduate Aptitude Test in Engineering (GATE). Manisha’s dedication and excellence in research were recognized with the Research Excellence Award from Delhi Technological University for three consecutive years (2021, 2022, and 2023). Additionally, she has received the Best Mentor and Dedicated Faculty Award for the 2019-20 academic year at the School of Engineering, GD Goenka University. With over 8 years of combined experience in teaching, research, and industry, she brings diversity and expertise to teaching with a focus on practical applications.
- Journal:
Case Studies
Journal:
- Saini, Manisha, and Seba Susan. “Vggin-net: Deep transfer network for imbalanced breast cancer dataset.” IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022). (SCIE indexed journal with an impact factor of 4.5) (https://dl.acm.org/doi/abs/10.1109/TCBB.2022.3163277)
- Saini, Manisha, and Seba Susan. “Deep transfer with minority data augmentation for imbalanced breast cancer dataset.” Applied Soft Computing 97 (2020): 106759. (SCIE indexed journal with an impact factor of 8.7) (https://www.sciencedirect.com/science/article/abs/pii/S1568494620306979)
- Saini, Manisha, and Seba Susan. “Bag-of-Visual-Words codebook generation using deep features for effective classification of imbalanced multi-class image datasets.” Multimedia Tools and Applications 80 (2021): 20821-20847. (SCIE indexed journal with an impact factor of 3.6) (https://link.springer.com/article/10.1007/s11042-021-10612-w)
- Saini, Manisha, and Seba Susan. “Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.” Computers in Biology and Medicine 149 (2022): 105989. (SCIE indexed journal with an impact factor of 6.69) (https://www.sciencedirect.com/science/article/abs/pii/S0010482522007090)
- Saini, Manisha, and Seba Susan. “Tackling class imbalance in computer vision: A contemporary review”, Artificial Intelligence Review. (2023): 1-57. (SCIE indexed journal with an impact factor of 12.0) (https://link.springer.com/article/10.1007/s10462-023-10557-6)
- Saini, Manisha, and Rita Chhikara. “DWT feature based blind image steganalysis using neural network classifier.” International Journal of Engineering Research and Technology 4, no. 04 (2015).
- Saini, Manisha, and Rita Chhikara. “Performance evaluation of DCT and DWT features for blind image steganalysis using neural networks.” International Journal of Computer Applications 114, no. 5 (2015).
Conferences:
- Saini, Manisha, and Seba Susan. “Cervical Cancer Screening on Multi-class Imbalanced Cervigram Dataset using Transfer Learning.” In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-6. IEEE, 2022. (Taizhou, China) (https://ieeexplore.ieee.org/abstract/document/9980238)
- Saini, Manisha, and Seba Susan. “Data augmentation of minority class with transfer learning for classification of imbalanced breast cancer dataset using inception-V3.” In Pattern Recognition and Image Analysis: 9th Iberian Conference, IbPRIA 2019, Madrid, Spain, July 1–4, 2019, Proceedings, Part I 9, pp. 409-420. Springer International Publishing, 2019. (Madrid, Spain) (https://link.springer.com/chapter/10.1007/978-3-030-31332-6_36)
- Saini, Manisha, and Seba Susan. “Comparison of deep learning, data augmentation and bag-of-visual-words for classification of imbalanced image datasets.” In Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part I 2, pp. 561-571. Springer Singapore, 2019. (https://link.springer.com/chapter/10.1007/978-981-13-9181-1_49)
- Saini, Manisha, and Rita Chhikara., “Discrete wavelet transformation features for blind image steganalysis” in Proceedings of 2nd International Conference on Recent Developments in Science, Engineering and Technology (REDSET 2015), pp. 85-89, October 2015. (Awarded as best paper of the session).
- Saini, Manisha, and Rita Chhikara. “Performance evaluation of features extracted from DWT domain.” In Software Engineering: Proceedings of CSI 2015, pp. 257-265. Springer Singapore, 2019. (https://link.springer.com/chapter/10.1007/978-981-10-8848-3_25)