Raghavendra Kumar

429 total citations
9 papers, 272 citations indexed

About

Raghavendra Kumar is a scholar working on Signal Processing, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Raghavendra Kumar has authored 9 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Signal Processing, 6 papers in Artificial Intelligence and 5 papers in Management Science and Operations Research. Recurrent topics in Raghavendra Kumar's work include Time Series Analysis and Forecasting (6 papers), Stock Market Forecasting Methods (5 papers) and Forecasting Techniques and Applications (2 papers). Raghavendra Kumar is often cited by papers focused on Time Series Analysis and Forecasting (6 papers), Stock Market Forecasting Methods (5 papers) and Forecasting Techniques and Applications (2 papers). Raghavendra Kumar collaborates with scholars based in India, South Korea and United Kingdom. Raghavendra Kumar's co-authors include Pardeep Kumar, Yugal Kumar, Mamta Mittal, Lê Hoàng Sơn, Manju Khari, Jyotir Moy Chatterjee, Francisco Chiclana, Sung Wook Baik, Anjali Jain and Arun Kumar Tripathi and has published in prestigious journals such as Knowledge-Based Systems, Neural Computing and Applications and Multimedia Tools and Applications.

In The Last Decade

Raghavendra Kumar

9 papers receiving 264 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Raghavendra Kumar India	7	86	73	62	42	35	9	272
Chengming Qi China	10	71 0.8×	85 1.2×	38 0.6×	28 0.7×	10 0.3×	38	345
Salahadin Mohammed Saudi Arabia	7	170 2.0×	91 1.2×	99 1.6×	31 0.7×	28 0.8×	27	344
Mustafa Akpınar Türkiye	10	117 1.4×	76 1.0×	198 3.2×	19 0.5×	28 0.8×	37	317
Sonia Sonia India	10	39 0.5×	84 1.2×	36 0.6×	60 1.4×	15 0.4×	61	341
Donald K. Wedding United States	5	105 1.2×	94 1.3×	63 1.0×	11 0.3×	23 0.7×	12	304
Sifan Wu China	6	129 1.5×	68 0.9×	59 1.0×	13 0.3×	11 0.3×	19	284
Haoyu Zhang China	4	256 3.0×	66 0.9×	136 2.2×	18 0.4×	16 0.5×	7	388
Tung‐Kuang Wu Taiwan	9	75 0.9×	80 1.1×	64 1.0×	16 0.4×	11 0.3×	25	277
Radha Mohan Pattanayak India	8	82 1.0×	53 0.7×	30 0.5×	32 0.8×	9 0.3×	14	200

Countries citing papers authored by Raghavendra Kumar

Since Specialization

Citations

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

Fields of papers citing papers by Raghavendra Kumar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Raghavendra Kumar

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

All Works

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9 of 9 papers shown

Kumar, Pardeep & Raghavendra Kumar. (2023). A hybrid framework for time series trends: embedding social network’s sentiments and optimized stacked LSTM using evolutionary algorithm. Multimedia Tools and Applications. 83(12). 34691–34714. 1 indexed citations

Kumar, Raghavendra, Pardeep Kumar, & Yugal Kumar. (2022). Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm. Neural Computing and Applications. 34(21). 18421–18437. 16 indexed citations

Kumar, Raghavendra, Pardeep Kumar, & Yugal Kumar. (2021). Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting. Multimedia Tools and Applications. 81(24). 34595–34614. 24 indexed citations

Kumar, Raghavendra, Pardeep Kumar, & Yugal Kumar. (2021). Analysis of Financial Time Series Forecasting using Deep Learning Model. 877–881. 19 indexed citations

Kumar, Raghavendra, Pardeep Kumar, & Yugal Kumar. (2021). Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms. International Journal of Information Technology. 14(1). 359–373. 41 indexed citations

Kumar, Raghavendra, et al.. (2021). COVID-19 Outbreak: An Epidemic Analysis using Time Series Prediction Model. 1090–1094. 24 indexed citations

Kumar, Raghavendra, Pardeep Kumar, & Yugal Kumar. (2020). Time Series Data Prediction using IoT and Machine Learning Technique. Procedia Computer Science. 167. 373–381. 62 indexed citations

Sơn, Lê Hoàng, Francisco Chiclana, Raghavendra Kumar, et al.. (2018). ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization. Knowledge-Based Systems. 154. 68–80. 84 indexed citations

Khari, Manju, et al.. (2018). Automatic Generation of Synsets for Wordnet of Hindi Language. RePEc: Research Papers in Economics. 7(2). 31–47. 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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