VLDB 2026 Research / reviewers in the wild / expert
Mukesh Saraswat
dblp:53/10620
· DBLP profile ↗
16ranked-venue papers
1as first author
11since 2021 · last 2025
0000-0002-3427-9105ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 1 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 6 · 5 since 2021Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Enhanced bag of features using logarithmic spiral HGSO and probability based fuzzy Gaussian mixture model for RGB-D object classification
Nandakishor Yadav, Mukesh Saraswat |
Multim. Tools Appl. | 2 |
| 2024 | Energy efficient multi-criterion binary grey wolf optimizer based clustering for heterogeneous wireless sensor networks
Raju Pal, Mukesh Saraswat, Sandeep Kumar 0001, Anand Nayyar, Pushpendra Kumar Rajput |
Soft Comput. | 2 |
| 2023 | From classical to soft computing based watermarking techniques: A comprehensive review
Roop Singh, Mukesh Saraswat, Alaknanda Ashok, Himanshu Mittal, Ashish K. Tripathi 0001, Avinash Chandra Pandey, Raju Pal |
Future Gener. Comput. Syst. | 2 |
| 2023 | High embedding capacity based color image watermarking scheme using SBBO in RDWT domain
Roop Singh, Alaknanda Ashok, Mukesh Saraswat |
Multim. Tools Appl. | 3 |
| 2023 | A new modified-unet deep learning model for semantic segmentation
Twinkle Tiwari, Mukesh Saraswat |
Multim. Tools Appl. | 2 |
| 2023 | Automatic multilevel image thresholding segmentation using hybrid bio-inspired algorithm and artificial neural network for histopathology images
Surbhi Vijh, Mukesh Saraswat |
Multim. Tools Appl. | 2 |
| 2022 | A new intrusion detection method for cyber-physical system in emerging industrial IoT
Himanshu Mittal, Ashish K. Tripathi 0001, Avinash Chandra Pandey, Mohammad Dahman Alshehri, Mukesh Saraswat, Raju Pal |
Comput. Commun. | 5 |
| 2022 | A novel fuzzy clustering based method for image segmentation in RGB-D images
Nandakishor Yadav, Mukesh Saraswat |
Eng. Appl. Artif. Intell. | 2 |
| 2022 | A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
Himanshu Mittal, Avinash Chandra Pandey, Mukesh Saraswat, Raju Pal, Garv Modwel |
Multim. Tools Appl. | 3 |
| 2021 | A new complete color normalization method for H&E stained histopatholgical images
Surbhi Vijh, Mukesh Saraswat |
Appl. Intell. | 2 |
| 2021 | A New Fuzzy Cluster Validity Index for Hyperellipsoid or Hyperspherical Shape Close Clusters With Distant CentroidsabstractDetermining the correct number of clusters is essential for efficient clustering and cluster validity indices are widely used for the same. Generally, the effectiveness of a cluster validity index relies on two factors: first, separation, defined by the distance between a pair of cluster centroids or a pair of data points belonging to different clusters and second, compactness, which is determined in terms of the distance between a data point and a centroid or between a pair of data points belonging to the same cluster. However, the existing cluster validity indices for centroid-based clustering are unreliable when the clusters are too close, but corresponding centroids are distant. To mitigate this, a new cluster validity index, Saraswat-and-Mittal index, has been proposed in this article for hyperellipsoid or hyperspherical shape close clusters with distant centroids, generated by fuzzy c-means. The proposed index computes compactness in terms of the distance between data points and corresponding centroids, whereas the distance between data points of disjoint clusters defines separation. These parameters benefit the proposed index in the analysis of close clusters with distinct centroids efficiently. The performance of the proposed index is validated against ten state-of-the-art cluster validity indices on artificial, UCI, and image datasets, clustered by the fuzzy c-means. Himanshu Mittal, Mukesh Saraswat |
IEEE Trans. Fuzzy Syst. | 2 |
| 2020 | Optimised robust watermarking technique using CKGSA in DCT-SVD domainabstractDigital watermarking embeds a watermark to minimise the problem of illegal copying and disseminating multimedia contents. However, the existing techniques do not maintain the imperceptibility and robustness simultaneously. To achieve the same, this study proposes an optimised robust watermarking technique using chaotic kbest gravitational search algorithm. The chaotic kbest gravitational search algorithm is used to obtain the optimal values of embedding factors. The efficacy of the proposed technique has been experimented on a standard images and compared with the six recent state‐of‐the‐art techniques in terms of imperceptibility and robustness. The experimental results validate that the proposed technique outperforms the other considered techniques. Roop Singh, Alaknanda Ashok, Mukesh Saraswat |
IET Image Process. | 3 |
| 2019 | Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization
Raju Pal, Mukesh Saraswat |
Appl. Intell. | 2 |
| 2018 | An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm
Himanshu Mittal, Mukesh Saraswat |
Eng. Appl. Artif. Intell. | 2 |
| 2017 | Twitter sentiment analysis using hybrid cuckoo search method
Avinash Chandra Pandey, Dharmveer Singh Rajpoot, Mukesh Saraswat |
Inf. Process. Manag. | 3 |
| 2014 | Supervised leukocyte segmentation in tissue images using multi-objective optimization technique
Mukesh Saraswat, K. V. Arya |
Eng. Appl. Artif. Intell. | 1 |