Mukesh Saini

1.5k total citations · 1 hit paper
65 papers, 979 citations indexed

About

Mukesh Saini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Mukesh Saini has authored 65 papers receiving a total of 979 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 17 papers in Computer Networks and Communications. Recurrent topics in Mukesh Saini's work include Video Surveillance and Tracking Methods (17 papers), Anomaly Detection Techniques and Applications (11 papers) and Video Analysis and Summarization (8 papers). Mukesh Saini is often cited by papers focused on Video Surveillance and Tracking Methods (17 papers), Anomaly Detection Techniques and Applications (11 papers) and Video Analysis and Summarization (8 papers). Mukesh Saini collaborates with scholars based in India, Canada and Singapore. Mukesh Saini's co-authors include Abdulmotaleb El Saddik, Kazi Masudul Alam, Neeraj Goel, Pradeep K. Atrey, Mohan Kankanhalli, Sharad Mehrotra, Abdulhameed Alelaiwi, Wei Tsang Ooi, Shuicheng Yan and Raghudeep Gadde and has published in prestigious journals such as IEEE Access, Sensors and ACM Computing Surveys.

In The Last Decade

Mukesh Saini

62 papers receiving 939 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Toward Social Internet of Vehicles: Concept, Architecture, and Applications

2015 289 citations Mukesh Saini, Abdulmotaleb El Saddik et al. IEEE Access profile →

Peers

Mukesh Saini

EEE

CNC

CVPR

AI

IS

Ken Sakamura Japan

Noboru Koshizuka Japan

Teddy Mantoro Indonesia

Genggeng Liu China

Hussein Zedan United Kingdom

Muhammad Umar Aftab Pakistan

Rajalakshmi Krishnamurthi India

Chyi‐Ren Dow Taiwan

Raquel Lacuesta Spain

L. Jegatha Deborah India

Ken Sakamura Japan

Mukesh Saini

482 ×1.4

EEE

512 ×1.5

CNC

253 ×0.8

CVPR

193 ×1.0

AI

294 ×1.8

IS

Citations per year, relative to Mukesh Saini Mukesh Saini (= 1×) peers Ken Sakamura

Countries citing papers authored by Mukesh Saini

Since Specialization

Citations

This map shows the geographic impact of Mukesh Saini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mukesh Saini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mukesh Saini more than expected).

Fields of papers citing papers by Mukesh Saini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mukesh Saini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mukesh Saini. The network helps show where Mukesh Saini may publish in the future.

Co-authorship network of co-authors of Mukesh Saini

This figure shows the co-authorship network connecting the top 25 collaborators of Mukesh Saini. A scholar is included among the top collaborators of Mukesh Saini based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mukesh Saini. Mukesh Saini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Goel, Neeraj, et al.. (2025). PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 10120–10130.

2.

Goel, Neeraj, et al.. (2024). SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network. European Journal of Agronomy. 161. 127333–127333. 10 indexed citations

3.

Atrey, Pradeep K., et al.. (2024). Concept drift challenge in multimedia anomaly detection: A case study with facial datasets. Signal Processing Image Communication. 123. 117100–117100. 3 indexed citations

4.

Goel, Neeraj, et al.. (2024). PA-RDFKNet: Unifying Plant Age Estimation through RGB-Depth Fusion and Knowledge Distillation. 2(2). 226–235. 2 indexed citations

5.

Saini, Mukesh, et al.. (2024). Artificial Intelligence Driven Fraud Detection in SAP for Retail and Healthcare. International Journal of Science and Research (IJSR). 13(11). 312–315.

6.

Goyal, Pooja, et al.. (2024). A custom deep learning model for abiotic stress classification in maize in uncontrolled environments. Computers and Electronics in Agriculture. 230. 109865–109865. 1 indexed citations

7.

Saini, Mukesh, et al.. (2023). Mapi-Pro: An Energy Efficient Memory Mapping Technique for Intermittent Computing. ACM Transactions on Architecture and Code Optimization. 20(4). 1–25. 1 indexed citations

8.

Saini, Mukesh, et al.. (2023). Towards Digital Twin of Crops for Growth Modelling using Virtual Reality. 1–5. 2 indexed citations

9.

Saini, Mukesh, et al.. (2023). An Efficient NVM-Based Architecture for Intermittent Computing Under Energy Constraints. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 31(6). 725–737. 2 indexed citations

10.

Goyal, Pooja, et al.. (2023). A deep learning approach for early detection of drought stress in maize using proximal scale digital images. Neural Computing and Applications. 36(4). 1899–1913. 14 indexed citations

11.

Saini, Mukesh, et al.. (2023). Multimedia datasets for anomaly detection: a review. Multimedia Tools and Applications. 83(19). 56785–56835. 3 indexed citations

12.

Saini, Mukesh, et al.. (2023). Efficient placement and migration policies for an STT-RAM based hybrid L1 cache for intermittently powered systems. Design Automation for Embedded Systems. 27(4). 303–331. 1 indexed citations

13.

Singh, Simrandeep, et al.. (2022). An Experimental Study of the Concept Drift Challenge in Farm Intrusion Detection using Audio. 1–8. 2 indexed citations

14.

Saini, Mukesh, et al.. (2022). An Adaptive Framework for Anomaly Detection in Time-Series Audio-Visual Data. IEEE Access. 10. 36188–36199. 8 indexed citations

15.

Saini, Mukesh, Abdulhameed Alelaiwi, & Abdulmotaleb El Saddik. (2015). How Close are We to Realizing a Pragmatic VANET Solution? A Meta-Survey. ACM Computing Surveys. 48(2). 1–40. 88 indexed citations

16.

Saini, Mukesh, et al.. (2014). Personalized ambient environments using multiple social networks. 1–6. 1 indexed citations

17.

Saini, Mukesh, Pradeep K. Atrey, Sharad Mehrotra, & Mohan Kankanhalli. (2012). W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video. Multimedia Tools and Applications. 68(1). 135–158. 49 indexed citations

18.

Saini, Mukesh, Xiangyu Wang, Pradeep K. Atrey, & Mohan Kankanhalli. (2011). Dynamic workload assignment in video surveillance systems. National University of Singapore. 8 indexed citations

19.

Saini, Mukesh, et al.. (2010). Privacy modeling for video data publication. National University of Singapore. 60–65. 22 indexed citations

20.

Mahapatra, Dwarikanath, Mukesh Saini, & Ying Sun. (2008). Illumination invariant tracking in office environments using neurobiology-saliency based particle filter. 953–956. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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