Ashima Khosla

404 total citations
8 papers, 267 citations indexed

About

Ashima Khosla is a scholar working on Cognitive Neuroscience, Cardiology and Cardiovascular Medicine and Signal Processing. According to data from OpenAlex, Ashima Khosla has authored 8 papers receiving a total of 267 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cognitive Neuroscience, 2 papers in Cardiology and Cardiovascular Medicine and 2 papers in Signal Processing. Recurrent topics in Ashima Khosla's work include EEG and Brain-Computer Interfaces (4 papers), ECG Monitoring and Analysis (2 papers) and Functional Brain Connectivity Studies (2 papers). Ashima Khosla is often cited by papers focused on EEG and Brain-Computer Interfaces (4 papers), ECG Monitoring and Analysis (2 papers) and Functional Brain Connectivity Studies (2 papers). Ashima Khosla collaborates with scholars based in India. Ashima Khosla's co-authors include Trilok Chand, Padmavati Khandnor, Mahesh Prakash, Devendra Kumar Chouhan, Neelesh Kumar and Trilok Chand Aseri and has published in prestigious journals such as Biomedical Signal Processing and Control, Journal of Applied Biomedicine and International Journal of Imaging Systems and Technology.

In The Last Decade

Ashima Khosla

8 papers receiving 263 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ashima Khosla India	6	195	63	54	35	29	8	267
Trilok Chand India	7	199 1.0×	65 1.0×	54 1.0×	36 1.0×	45 1.6×	19	373
Nannapas Banluesombatkul Thailand	3	209 1.1×	39 0.6×	57 1.1×	42 1.2×	25 0.9×	6	259
Elnaz Lashgari United States	4	191 1.0×	52 0.8×	39 0.7×	50 1.4×	41 1.4×	8	289
Jae-Hwan Kang South Korea	10	220 1.1×	51 0.8×	36 0.7×	58 1.7×	37 1.3×	25	276
Chamandeep Kaur India	7	243 1.2×	61 1.0×	42 0.8×	71 2.0×	28 1.0×	14	336
Chong Liu China	10	216 1.1×	34 0.5×	35 0.6×	36 1.0×	20 0.7×	30	298
Padmavati Khandnor India	10	204 1.0×	64 1.0×	57 1.1×	67 1.9×	48 1.7×	28	444
Hubert Banville Canada	10	285 1.5×	48 0.8×	52 1.0×	62 1.8×	38 1.3×	16	373
Diana Cogan United States	6	255 1.3×	112 1.8×	56 1.0×	85 2.4×	47 1.6×	6	325
Eunjin Jeon South Korea	9	242 1.2×	29 0.5×	68 1.3×	31 0.9×	37 1.3×	19	320

Countries citing papers authored by Ashima Khosla

Since Specialization

Citations

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

Fields of papers citing papers by Ashima Khosla

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashima Khosla

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

All Works

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8 of 8 papers shown

1.

Khosla, Ashima, et al.. (2024). Machine learning approach for predicting state transitions via shank acceleration data during freezing of gait in Parkinson’s disease. Biomedical Signal Processing and Control. 92. 106053–106053. 5 indexed citations

2.

Khosla, Ashima, et al.. (2023). Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture. International Journal of Imaging Systems and Technology. 33(3). 1028–1047. 11 indexed citations

3.

Khosla, Ashima, et al.. (2023). A Deep Learning Approach for Development of Web Application for Automated Knee OA Classification. 1–8. 1 indexed citations

4.

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

5.

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

6.

Khosla, Ashima, Padmavati Khandnor, & Trilok Chand. (2021). Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Journal of Applied Biomedicine. 42(1). 108–142. 54 indexed citations

7.

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

8.

Khosla, Ashima & Trilok Chand Aseri. (2018). Comparative Analysis of Objective Functions in Routing Protocol for Low Power and Lossy Networks. 4(3). 556–562. 2 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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