Ankush Manocha

675 total citations
40 papers, 444 citations indexed

About

Ankush Manocha is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Ankush Manocha has authored 40 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Networks and Communications, 9 papers in Computer Vision and Pattern Recognition and 9 papers in Media Technology. Recurrent topics in Ankush Manocha's work include Remote-Sensing Image Classification (9 papers), IoT and Edge/Fog Computing (9 papers) and Context-Aware Activity Recognition Systems (7 papers). Ankush Manocha is often cited by papers focused on Remote-Sensing Image Classification (9 papers), IoT and Edge/Fog Computing (9 papers) and Context-Aware Activity Recognition Systems (7 papers). Ankush Manocha collaborates with scholars based in India and Saudi Arabia. Ankush Manocha's co-authors include Munish Bhatia, Sandeep K. Sood, Ramandeep Singh, Tariq Ahamed Ahanger, Gulshan Kumar, Amit Sharma, Prabal Verma, Abdullah Alqahtani, Faheem Masoodi and Alwi M. Bamhdi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computer and IEEE Internet of Things Journal.

In The Last Decade

Ankush Manocha

39 papers receiving 429 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ankush Manocha India 13 98 96 91 66 66 40 444
Muhammad Attique Khan Saudi Arabia 14 75 0.8× 66 0.7× 134 1.5× 50 0.8× 33 0.5× 76 492
Jianwei Ding China 16 68 0.7× 245 2.6× 208 2.3× 78 1.2× 115 1.7× 50 694
Guilu Wu China 8 85 0.9× 46 0.5× 120 1.3× 33 0.5× 27 0.4× 34 500
Sibghat Ullah Bazai Pakistan 10 55 0.6× 46 0.5× 88 1.0× 46 0.7× 30 0.5× 42 442
Shaofu Lin China 12 39 0.4× 185 1.9× 213 2.3× 51 0.8× 61 0.9× 73 774
Xiaohui Huang China 11 52 0.5× 95 1.0× 179 2.0× 70 1.1× 57 0.9× 48 470
Baojun Qiao China 11 54 0.6× 39 0.4× 143 1.6× 17 0.3× 26 0.4× 31 401
Emanuele Ragnoli Ireland 9 72 0.7× 19 0.2× 25 0.3× 83 1.3× 53 0.8× 25 344
Jiangjiang Wu China 12 66 0.7× 97 1.0× 76 0.8× 45 0.7× 44 0.7× 36 423
Xiaoxia Wang China 11 36 0.4× 51 0.5× 141 1.5× 25 0.4× 36 0.5× 53 620

Countries citing papers authored by Ankush Manocha

Since Specialization
Citations

This map shows the geographic impact of Ankush Manocha'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 Ankush Manocha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankush Manocha more than expected).

Fields of papers citing papers by Ankush Manocha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ankush Manocha. 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 Ankush Manocha. The network helps show where Ankush Manocha may publish in the future.

Co-authorship network of co-authors of Ankush Manocha

This figure shows the co-authorship network connecting the top 25 collaborators of Ankush Manocha. A scholar is included among the top collaborators of Ankush Manocha 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 Ankush Manocha. Ankush Manocha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Manocha, Ankush, Sandeep K. Sood, & Munish Bhatia. (2024). Federated learning-inspired smart ECG classification: an explainable artificial intelligence approach. Multimedia Tools and Applications. 84(19). 21673–21696. 2 indexed citations
2.
Manocha, Ankush, et al.. (2024). Small water body extraction in remote sensing with enhanced CNN architecture. Applied Soft Computing. 169. 112544–112544. 5 indexed citations
3.
Manocha, Ankush, et al.. (2024). Enhancing sensor data reliability in structural health monitoring systems using digital twin technology. Structural Concrete. 26(5). 5919–5935. 1 indexed citations
4.
Manocha, Ankush, Munish Bhatia, & Gulshan Kumar. (2024). Smart monitoring solution for dengue infection control: A digital twin-inspired approach. Computer Methods and Programs in Biomedicine. 257. 108459–108459. 4 indexed citations
5.
Manocha, Ankush, Sandeep K. Sood, & Munish Bhatia. (2024). Edge intelligence-assisted smart healthcare solution for health pandemic: a federated environment approach. Cluster Computing. 27(5). 5611–5630. 7 indexed citations
6.
7.
Bhatia, Munish, Tariq Ahamed Ahanger, & Ankush Manocha. (2023). Artificial intelligence based real-time earthquake prediction. Engineering Applications of Artificial Intelligence. 120. 105856–105856. 32 indexed citations
8.
Manocha, Ankush, et al.. (2023). A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing. SHILAP Revista de lepidopterología. 56. 3002–3002. 1 indexed citations
9.
Manocha, Ankush, Sandeep K. Sood, & Munish Bhatia. (2023). Digital-Twin-Assisted Academic Environment Monitoring for Anxiety Disorder. IEEE Internet of Things Journal. 11(8). 13563–13570. 4 indexed citations
10.
Manocha, Ankush, Sandeep K. Sood, & Munish Bhatia. (2023). IoT-digital twin-inspired smart irrigation approach for optimal water utilization. Sustainable Computing Informatics and Systems. 41. 100947–100947. 23 indexed citations
11.
Manocha, Ankush, Sandeep K. Sood, & Munish Bhatia. (2023). Artificial intelligence-assisted water quality index determination for healthcare. Artificial Intelligence Review. 56(S2). 2893–2915. 9 indexed citations
12.
Manocha, Ankush, et al.. (2022). Dew computing-assisted cognitive Intelligence-inspired smart environment for diarrhea prediction. Computing. 104(11). 2511–2540. 2 indexed citations
13.
Bhatia, Munish, Ankush Manocha, Tariq Ahamed Ahanger, & Abdullah Alqahtani. (2022). Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control. Artificial Intelligence in Medicine. 127. 102288–102288. 17 indexed citations
14.
Manocha, Ankush & Munish Bhatia. (2022). A novel deep fusion strategy for COVID-19 prediction using multimodality approach. Computers & Electrical Engineering. 103. 108274–108274. 8 indexed citations
15.
Manocha, Ankush, et al.. (2022). Mapping of water bodies from sentinel-2 images using deep learning-based feature fusion approach. Neural Computing and Applications. 5 indexed citations
16.
Manocha, Ankush, et al.. (2021). Fog-inspired water resource analysis in urban areas from satellite images. Ecological Informatics. 64. 101385–101385. 5 indexed citations
17.
Manocha, Ankush, et al.. (2021). Analysis on change detection techniques for remote sensing applications: A review. Ecological Informatics. 63. 101310–101310. 85 indexed citations
18.
Manocha, Ankush, Gulshan Kumar, Munish Bhatia, & Amit Sharma. (2020). Video-assisted smart health monitoring for affliction determination based on fog analytics. Journal of Biomedical Informatics. 109. 103513–103513. 10 indexed citations
19.
Manocha, Ankush, Ramandeep Singh, & Munish Bhatia. (2019). Cognitive Intelligence Assisted Fog-Cloud Architecture for Generalized Anxiety Disorder (GAD) Prediction. Journal of Medical Systems. 44(1). 7–7. 16 indexed citations
20.
Manocha, Ankush & Ramandeep Singh. (2019). An intelligent monitoring system for indoor safety of individuals suffering from Autism Spectrum Disorder (ASD). Journal of Ambient Intelligence and Humanized Computing. 14(12). 15793–15808. 5 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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2026