Niraj Thapa

444 total citations
12 papers, 315 citations indexed

About

Niraj Thapa is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Niraj Thapa has authored 12 papers receiving a total of 315 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 Computer Networks and Communications. Recurrent topics in Niraj Thapa's work include Network Security and Intrusion Detection (5 papers), Machine Learning in Bioinformatics (4 papers) and Advanced Malware Detection Techniques (4 papers). Niraj Thapa is often cited by papers focused on Network Security and Intrusion Detection (5 papers), Machine Learning in Bioinformatics (4 papers) and Advanced Malware Detection Techniques (4 papers). Niraj Thapa collaborates with scholars based in United States, Japan and Germany. Niraj Thapa's co-authors include Kaushik Roy, Zhipeng Liu, Robert H. Newman, Balakrishna Gokaraju, Hiroto Saigo, Dukka B. KC, Dukka B. KC, Xiaohong Yuan, Sajad Khorsandroo and Anna Yu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Sensors.

In The Last Decade

Niraj Thapa

12 papers receiving 301 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Niraj Thapa United States	10	155	144	107	105	24	12	315
Runqing Yang China	7	154 1.0×	98 0.7×	136 1.3×	22 0.2×	104 4.3×	9	259
Zhiyang Fang China	8	157 1.0×	95 0.7×	164 1.5×	31 0.3×	76 3.2×	17	239
Yanbin Wang China	10	55 0.4×	63 0.4×	40 0.4×	69 0.7×	57 2.4×	22	216
Azade Nazi United States	8	127 0.8×	103 0.7×	91 0.9×	28 0.3×	49 2.0×	20	247
Peter Lichodzijewski Canada	9	110 0.7×	262 1.8×	55 0.5×	20 0.2×	22 0.9×	17	297
David Hardin United States	9	44 0.3×	96 0.7×	21 0.2×	8 0.1×	18 0.8×	35	209
Boris Čule Belgium	8	28 0.2×	106 0.7×	69 0.6×	44 0.4×	75 3.1×	22	203
J.T.L. Wang United States	10	69 0.4×	116 0.8×	88 0.8×	89 0.8×	106 4.4×	20	282
George Katsirelos France	10	91 0.6×	121 0.8×	12 0.1×	73 0.7×	6 0.3×	18	249

Countries citing papers authored by Niraj Thapa

Since Specialization

Citations

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

Fields of papers citing papers by Niraj Thapa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Niraj Thapa

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

All Works

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12 of 12 papers shown

Thapa, Niraj, et al.. (2023). Presentation attack detection: an analysis of spoofing in the wild (SiW) dataset using deep learning models. SHILAP Revista de lepidopterología. 3(1). 1 indexed citations

Thapa, Niraj, Hamid D. Ismail, Doina Caragea, et al.. (2021). DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites. Frontiers in Cell and Developmental Biology. 9. 662983–662983. 14 indexed citations

Thapa, Niraj, et al.. (2021). A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites. Scientific Reports. 11(1). 12550–12550. 13 indexed citations

Liu, Zhipeng, Niraj Thapa, Kaushik Roy, et al.. (2021). Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset. Sensors. 21(14). 4834–4834. 39 indexed citations

Thapa, Niraj, et al.. (2021). Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models. Electronics. 10(15). 1747–1747. 14 indexed citations

Thapa, Niraj, et al.. (2020). DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Molecular Omics. 16(5). 448–454. 23 indexed citations

Thapa, Niraj, et al.. (2020). DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics. 21(S3). 63–63. 55 indexed citations

Thapa, Niraj, et al.. (2020). RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites. Computational and Structural Biotechnology Journal. 18. 852–860. 18 indexed citations

Liu, Zhipeng, et al.. (2020). Anomaly Based Intrusion Detection for IoT with Machine Learning. 1–6. 15 indexed citations

10.

Thapa, Niraj, et al.. (2020). Anomaly Detection on IoT Network Intrusion Using Machine Learning. 1–5. 47 indexed citations

11.

Thapa, Niraj, Zhipeng Liu, Dukka B. KC, Balakrishna Gokaraju, & Kaushik Roy. (2020). Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems. Future Internet. 12(10). 167–167. 68 indexed citations

12.

Bharadi, Vinayak Ashok, et al.. (2018). Multi-Instance Iris Recognition. Nottingham Trent University's Institutional Repository (Nottingham Trent Repository). 1–6. 8 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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