Ashish Kumar Shakya

560 total citations · 1 hit paper
11 papers, 278 citations indexed

About

Ashish Kumar Shakya is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering and Ocean Engineering. According to data from OpenAlex, Ashish Kumar Shakya has authored 11 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Electrical and Electronic Engineering, 4 papers in Control and Systems Engineering and 3 papers in Ocean Engineering. Recurrent topics in Ashish Kumar Shakya's work include Energy Harvesting in Wireless Networks (3 papers), Fluid Dynamics Simulations and Interactions (3 papers) and Underwater Vehicles and Communication Systems (3 papers). Ashish Kumar Shakya is often cited by papers focused on Energy Harvesting in Wireless Networks (3 papers), Fluid Dynamics Simulations and Interactions (3 papers) and Underwater Vehicles and Communication Systems (3 papers). Ashish Kumar Shakya collaborates with scholars based in India and Nepal. Ashish Kumar Shakya's co-authors include G. N. Pillai, Sohom Chakrabarty, Satyendra Pratap Singh, Shailendra Singh, Leeladhar Malviya, Shailendra Singh, Satyendra Pratap Singh and Parul Gupta and has published in prestigious journals such as Expert Systems with Applications and IFAC-PapersOnLine.

In The Last Decade

Ashish Kumar Shakya

8 papers receiving 270 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.

Reinforcement learning algorithms: A brief survey

2023 234 citations Ashish Kumar Shakya, G. N. Pillai et al. Expert Systems with Applications profile →

Peers

Ashish Kumar Shakya

EEE

CSE

AI

AE

CNC

M. F. Ahmad Malaysia

Feng Huang China

Kuo-Hsien Hsia Taiwan

Yongjian Yang China

Junhong Xu United States

Abdullah A. Algethami Saudi Arabia

Mohammed Mestari Morocco

Adrián González Mexico

Dejun Mu China

Dawid Zydek United States

M. F. Ahmad Malaysia

Ashish Kumar Shakya

76 ×0.9

EEE

68 ×0.9

CSE

155 ×2.3

AI

25 ×0.6

AE

24 ×0.7

CNC

Citations per year, relative to Ashish Kumar Shakya Ashish Kumar Shakya (= 1×) peers M. F. Ahmad

Countries citing papers authored by Ashish Kumar Shakya

Since Specialization

Citations

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

Fields of papers citing papers by Ashish Kumar Shakya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashish Kumar Shakya

This figure shows the co-authorship network connecting the top 25 collaborators of Ashish Kumar Shakya. A scholar is included among the top collaborators of Ashish Kumar Shakya 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 Ashish Kumar Shakya. Ashish Kumar Shakya 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:

11 of 11 papers shown

1.

Shakya, Ashish Kumar, G. N. Pillai, & Sohom Chakrabarty. (2024). A Reinforcement Learning Based Super Twisting Controller for a Rotary Slosh Control Problem. 1–6.

2.

Shakya, Ashish Kumar, G. N. Pillai, & Sohom Chakrabarty. (2023). Learning Based Adaptation of Proportional Derivative Controller for a Novel Rotary Slosh Dynamics Model. 1–8.

3.

Shakya, Ashish Kumar, G. N. Pillai, & Sohom Chakrabarty. (2023). Reinforcement learning algorithms: A brief survey. Expert Systems with Applications. 231. 120495–120495. 234 indexed citations breakdown →

4.

Chakrabarty, Sohom, et al.. (2023). Stabilizing a Collaborative Twin Quadrotor System for Hovering with a Cable-Suspended Extended Payload. 1–6.

5.

Shakya, Ashish Kumar, et al.. (2022). Enhancement of WRF Model Using CUDA. 1(1). 16–22. 1 indexed citations

6.

Shakya, Ashish Kumar, et al.. (2022). Deep Reinforcement Learning based Super Twisting Controller for Liquid Slosh Control Problem. IFAC-PapersOnLine. 55(1). 734–739. 5 indexed citations

7.

Gupta, Parul, et al.. (2019). 2x2 Wideband Array MIMO Antenna for 5G Spectral Band. 1–4. 1 indexed citations

8.

Malviya, Leeladhar, et al.. (2018). 5.8 GHz Wlan MIMO Antenna with Power Divider Arms. 1–4. 1 indexed citations

9.

Malviya, Leeladhar, et al.. (2018). Wide-Band Meander Line MIMO Antenna For Wireless Applications. 1663–1667. 8 indexed citations

10.

Singh, Shailendra, et al.. (2017). Frequency Regulation of Micro-grid Connected Hybrid Power System with SMES. 2(1). 17 indexed citations

11.

Singh, Shailendra, et al.. (2016). Frequency stability analysis of hybrid power system based on solar PV with SMEs unit. 5–11. 11 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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