Rajesh Kumar Aggarwal

1.7k total citations
64 papers, 1.1k citations indexed

About

Rajesh Kumar Aggarwal is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rajesh Kumar Aggarwal has authored 64 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 39 papers in Signal Processing and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rajesh Kumar Aggarwal's work include Speech Recognition and Synthesis (38 papers), Speech and Audio Processing (36 papers) and Music and Audio Processing (30 papers). Rajesh Kumar Aggarwal is often cited by papers focused on Speech Recognition and Synthesis (38 papers), Speech and Audio Processing (36 papers) and Music and Audio Processing (30 papers). Rajesh Kumar Aggarwal collaborates with scholars based in India, United States and France. Rajesh Kumar Aggarwal's co-authors include Tarik Yousef, Mayank Dave, Mohit Dua, Vijay Verma, Mantosh Biswas, Virender Kadyan, Ankit Kumar, Archana Mantri, Ankita Jain and Kuldeep Kumar and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Project Management and Journal of money credit and banking.

In The Last Decade

Rajesh Kumar Aggarwal

63 papers receiving 996 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rajesh Kumar Aggarwal India 19 574 514 277 202 156 64 1.1k
Yuanzhi Li United States 14 397 0.7× 74 0.1× 117 0.4× 27 0.1× 52 0.3× 49 756
Yukun Ma Singapore 17 1.2k 2.2× 158 0.3× 12 0.0× 73 0.4× 91 0.6× 45 1.7k
Kinjal Chaudhari India 11 194 0.3× 74 0.1× 13 0.0× 142 0.7× 143 0.9× 15 720
Yee Ling Boo Australia 7 306 0.5× 35 0.1× 73 0.3× 41 0.2× 35 0.2× 22 522
Özgür Şimşek United States 17 498 0.9× 30 0.1× 16 0.1× 80 0.4× 43 0.3× 36 898
Linyi Yang China 10 687 1.2× 64 0.1× 14 0.1× 28 0.1× 34 0.2× 24 1.3k
Lixin Cui China 16 355 0.6× 42 0.1× 40 0.1× 74 0.4× 12 0.1× 50 666
Shuhan Yuan United States 15 594 1.0× 134 0.3× 17 0.1× 53 0.3× 19 0.1× 40 978
Khaled H. Alyoubi Saudi Arabia 13 186 0.3× 73 0.1× 9 0.0× 70 0.3× 59 0.4× 38 577

Countries citing papers authored by Rajesh Kumar Aggarwal

Since Specialization
Citations

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

Fields of papers citing papers by Rajesh Kumar Aggarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rajesh Kumar Aggarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Rajesh Kumar Aggarwal. A scholar is included among the top collaborators of Rajesh Kumar Aggarwal 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 Rajesh Kumar Aggarwal. Rajesh Kumar Aggarwal 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.
Aggarwal, Rajesh Kumar, et al.. (2024). Novel Human Activity Recognition by graph engineered ensemble deep learning model. 27. 100253–100253. 3 indexed citations
2.
Kumar, Ankit & Rajesh Kumar Aggarwal. (2021). An Investigation of Multilingual TDNN-BLSTM Acoustic Modeling forHindi Speech Recognition. International Journal of Sensors Wireless Communications and Control. 12(1). 19–31. 2 indexed citations
3.
Kumar, Ankit & Rajesh Kumar Aggarwal. (2020). Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling. SHILAP Revista de lepidopterología. 30(1). 165–179. 18 indexed citations
4.
Aggarwal, Rajesh Kumar, et al.. (2019). A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition. SHILAP Revista de lepidopterología. 29(1). 1261–1274. 76 indexed citations
5.
Aggarwal, Rajesh Kumar, et al.. (2019). A comparative analysis of pooling strategies for convolutional neural network based Hindi ASR. Journal of Ambient Intelligence and Humanized Computing. 11(2). 675–691. 17 indexed citations
6.
Verma, Vijay & Rajesh Kumar Aggarwal. (2019). A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations. International Journal of Information Technology and Computer Science. 11(6). 37–49. 8 indexed citations
7.
Aggarwal, Rajesh Kumar, et al.. (2019). Steganography using HTML Web Pages as a Carrier: A Survey. SSRN Electronic Journal. 5 indexed citations
8.
Verma, Vijay & Rajesh Kumar Aggarwal. (2019). Accuracy Assessment of Similarity Measures in Collaborative Recommendations Using CF4J Framework. International Journal of Modern Education and Computer Science. 11(5). 41–53. 4 indexed citations
9.
Dua, Mohit, Rajesh Kumar Aggarwal, & Mantosh Biswas. (2018). Optimizing Integrated Features for Hindi Automatic Speech Recognition System. Journal of Intelligent Systems. 29(1). 959–976. 8 indexed citations
10.
Dua, Mohit, Rajesh Kumar Aggarwal, & Mantosh Biswas. (2018). Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling. Journal of Intelligent Systems. 29(1). 327–344. 21 indexed citations
11.
Dua, Mohit, Rajesh Kumar Aggarwal, & Mantosh Biswas. (2018). Performance evaluation of Hindi speech recognition system using optimized filterbanks. Engineering Science and Technology an International Journal. 21(3). 389–398. 22 indexed citations
12.
Aggarwal, Rajesh Kumar, et al.. (2018). Augmented Handwritten Devanagari Digit Recognition Using Convolutional Autoencoder. 323. 574–580. 3 indexed citations
13.
Dua, Mohit, Rajesh Kumar Aggarwal, & Mantosh Biswas. (2017). Discriminative Training using Heterogeneous Feature Vector for Hindi Automatic Speech Recognition System. 158–162. 14 indexed citations
14.
Aggarwal, Rajesh Kumar, et al.. (2016). Object detection in adverse situations. 1 indexed citations
15.
Aggarwal, Rajesh Kumar, et al.. (2016). Hybrid architecture for robust speech recognition system. 1–7. 4 indexed citations
16.
Mamta, ­, et al.. (2014). Automatic Speech Recognition: A Survey. 3(1). 10 indexed citations
17.
Dua, Mohit, Virender Kadyan, Rajesh Kumar Aggarwal, & Sumeet Dua. (2012). Punjabi speech to text system for connected words. 206–209. 13 indexed citations
18.
Aggarwal, Rajesh Kumar & Mayank Dave. (2011). Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I). International Journal of Speech Technology. 14(4). 297–308. 27 indexed citations
19.
Aggarwal, Rajesh Kumar & Mayank Dave. (2008). Implementing a Speech Recognition System Interface for Indian Languages. International Joint Conference on Natural Language Processing. 105–112. 11 indexed citations
20.
Aggarwal, Rajesh Kumar, et al.. (2006). Access to Computer Technologies at Home Improves Wages in the Marketplace. Journal of international technology and information management. 15(3). 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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