Padmavati Khandnor

711 total citations
28 papers, 444 citations indexed

About

Padmavati Khandnor is a scholar working on Computer Networks and Communications, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Padmavati Khandnor has authored 28 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Networks and Communications, 6 papers in Cognitive Neuroscience and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Padmavati Khandnor's work include Energy Efficient Wireless Sensor Networks (9 papers), Mobile Ad Hoc Networks (6 papers) and EEG and Brain-Computer Interfaces (6 papers). Padmavati Khandnor is often cited by papers focused on Energy Efficient Wireless Sensor Networks (9 papers), Mobile Ad Hoc Networks (6 papers) and EEG and Brain-Computer Interfaces (6 papers). Padmavati Khandnor collaborates with scholars based in India. Padmavati Khandnor's co-authors include Trilok Chand, Ashima Khosla, Trilok Chand Aseri, Neelam Goel, Neelesh Kumar, Rajesh Bhatia, Rohit Goyal, Sandeep Harit, Ashima Goyal and Megha Sharma and has published in prestigious journals such as Expert Systems with Applications, Engineering Applications of Artificial Intelligence and Journal of Neural Engineering.

In The Last Decade

Padmavati Khandnor

26 papers receiving 428 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Padmavati Khandnor India 10 204 74 69 67 64 28 444
Manuel Gil-Martín Spain 9 139 0.7× 54 0.7× 77 1.1× 77 1.1× 47 0.7× 35 497
Ankit A. Bhurane India 10 252 1.2× 52 0.7× 18 0.3× 95 1.4× 104 1.6× 38 452
Seyyed Abed Hosseini Iran 12 326 1.6× 17 0.2× 85 1.2× 79 1.2× 103 1.6× 51 543
Chen‐Wen Yen Taiwan 12 101 0.5× 60 0.8× 41 0.6× 27 0.4× 24 0.4× 40 419
Alessandro Puiatti Switzerland 10 189 0.9× 84 1.1× 26 0.4× 21 0.3× 13 0.2× 43 529
Qunxi Dong China 15 345 1.7× 63 0.9× 92 1.3× 24 0.4× 75 1.2× 48 670
Maurizio Campolo Italy 8 257 1.3× 18 0.2× 63 0.9× 42 0.6× 94 1.5× 15 465
S. Pravin Kumar India 10 171 0.8× 35 0.5× 113 1.6× 141 2.1× 68 1.1× 39 382
Haitham M. Al-Angari United Arab Emirates 11 189 0.9× 153 2.1× 52 0.8× 46 0.7× 268 4.2× 20 702
Suparerk Janjarasjitt Thailand 11 242 1.2× 54 0.7× 24 0.3× 89 1.3× 42 0.7× 33 551

Countries citing papers authored by Padmavati Khandnor

Since Specialization
Citations

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

Fields of papers citing papers by Padmavati Khandnor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Padmavati Khandnor

This figure shows the co-authorship network connecting the top 25 collaborators of Padmavati Khandnor. A scholar is included among the top collaborators of Padmavati Khandnor 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 Padmavati Khandnor. Padmavati Khandnor 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.
Goel, Neelam, et al.. (2025). DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer. Discover Oncology. 16(1). 614–614.
2.
Khandnor, Padmavati, et al.. (2024). Identification of autism spectrum disorder using electroencephalography and machine learning: a review. Journal of Neural Engineering. 21(6). 61006–61006. 3 indexed citations
3.
Khandnor, Padmavati, et al.. (2024). Identifying autism using EEG: unleashing the power of feature selection and machine learning. Biomedical Physics & Engineering Express. 10(3). 35013–35013. 3 indexed citations
4.
Khandnor, Padmavati, et al.. (2024). Enhancing pap smear image classification: integrating transfer learning and attention mechanisms for improved detection of cervical abnormalities. Biomedical Physics & Engineering Express. 10(6). 65031–65031. 2 indexed citations
5.
Goel, Neelam, et al.. (2024). Advances in AI-based genomic data analysis for cancer survival prediction. Multimedia Tools and Applications. 84(14). 14139–14166. 3 indexed citations
6.
Sharma, Megha, et al.. (2023). Deep Learning Models for Classification of Deciduous and Permanent Teeth From Digital Panoramic Images. Cureus. 15(12). e49937–e49937. 2 indexed citations
7.
Khosla, Ashima, Padmavati Khandnor, & Trilok Chand. (2022). A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach. Concurrency and Computation Practice and Experience. 34(13). 6 indexed citations
8.
Khosla, Ashima, Padmavati Khandnor, & Trilok Chand. (2021). EEG‐based automatic multi‐class classification of epileptic seizure types using recurrence plots. Expert Systems. 39(5). 16 indexed citations
9.
Khandnor, Padmavati, et al.. (2020). A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson’s Disease. International Journal of Data Science and Analytics. 11(1). 69–83. 31 indexed citations
10.
Khosla, Ashima, Padmavati Khandnor, & Trilok Chand. (2020). A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Journal of Applied Biomedicine. 40(2). 649–690. 172 indexed citations
11.
12.
Goel, Neelam, et al.. (2020). TCGA: A multi-genomics material repository for cancer research. Materials Today Proceedings. 28. 1492–1495. 8 indexed citations
13.
Khandnor, Padmavati, et al.. (2017). A survey of activity recognition process using inertial sensors and smartphone sensors. 607–612. 9 indexed citations
14.
Goyal, Rohit & Padmavati Khandnor. (2017). Routing protocols based on sink mobility in wireless sensor networks — A review. 2131–2135. 1 indexed citations
15.
Khandnor, Padmavati & Trilok Chand Aseri. (2017). Reactive energy efficient heterogeneous cluster based (R-EEHC) routing protocol for WSN. 1442–1445. 5 indexed citations
16.
Khandnor, Padmavati, et al.. (2017). Structure and structure-free data aggregation protocols in wireless sensor networks-a review. 2136–2140. 2 indexed citations
17.
Sharma, Ajay K., Padmavati Khandnor, & Sandeep Harit. (2016). Tree based heuristic algorithms for maximizing network lifetime: A review. 3. 810–813. 1 indexed citations
18.
Khandnor, Padmavati, et al.. (2016). Watermarking schemes for secure data aggregation in wireless sensor networks: A review paper. 11. 3093–3098. 7 indexed citations
19.
Khandnor, Padmavati, et al.. (2016). Enhanced mobility based clustering protocol for wireless sensor networks. 6. 687–692. 4 indexed citations
20.
Khandnor, Padmavati, et al.. (2015). A survey on two-factor user authentication schemes in Wireless Sensor Networks. 12. 1077–1081. 1 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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