Ajeet Ram Pathak

947 total citations · 1 hit paper
19 papers, 550 citations indexed

About

Ajeet Ram Pathak is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ajeet Ram Pathak has authored 19 papers receiving a total of 550 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Information Systems and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ajeet Ram Pathak's work include Advanced Text Analysis Techniques (4 papers), Sentiment Analysis and Opinion Mining (3 papers) and Complex Network Analysis Techniques (3 papers). Ajeet Ram Pathak is often cited by papers focused on Advanced Text Analysis Techniques (4 papers), Sentiment Analysis and Opinion Mining (3 papers) and Complex Network Analysis Techniques (3 papers). Ajeet Ram Pathak collaborates with scholars based in India, Norway and Denmark. Ajeet Ram Pathak's co-authors include Manjusha Pandey, Siddharth Swarup Rautaray, Keshav K. Singh, Abhishek Raut, Anne C. Elster, Vandana Jagtap, Mohit Kumar and Ira Assent and has published in prestigious journals such as IEEE Access, Applied Soft Computing and Cluster Computing.

In The Last Decade

Ajeet Ram Pathak

17 papers receiving 512 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.

Application of Deep Learning for Object Detection

2018 337 citations Ajeet Ram Pathak, Manjusha Pandey et al. Procedia Computer Science profile →

Peers

Ajeet Ram Pathak

CVPR

AI

IS

SPS

MT

Baohua Qiang China

Simant Prakoonwit United Kingdom

Najla Al-Nabhan Saudi Arabia

Feng Ding China

My Abdelouahed Sabri Morocco

Ben Tan Hong Kong

Junsheng Wu China

Faliang Huang China

P. Prabu India

Baohua Qiang China

Ajeet Ram Pathak

178 ×0.8

CVPR

147 ×0.8

AI

92 ×1.3

IS

31 ×0.5

SPS

54 ×1.0

MT

Citations per year, relative to Ajeet Ram Pathak Ajeet Ram Pathak (= 1×) peers Baohua Qiang

Countries citing papers authored by Ajeet Ram Pathak

Since Specialization

Citations

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

Fields of papers citing papers by Ajeet Ram Pathak

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ajeet Ram Pathak. 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 Ajeet Ram Pathak. The network helps show where Ajeet Ram Pathak may publish in the future.

Co-authorship network of co-authors of Ajeet Ram Pathak

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

All Works

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19 of 19 papers shown

1.

Pathak, Ajeet Ram. (2025). Highlights on Utilizing Machine Learning for High Performance Computing Systems. Procedia Computer Science. 258. 1242–1253.

2.

Kumar, Mohit, et al.. (2024). FaN-REMs: Fair and Normalized Retrieval Evaluation Metrics for Learning Retrieval Systems. IEEE Access. 12. 195370–195395. 1 indexed citations

3.

Assent, Ira, et al.. (2022). GPU-FAST-PROCLUS: A Fast GPU-parallelized Approach to Projected Clustering. Duo Research Archive (University of Oslo). 1 indexed citations

4.

Pathak, Ajeet Ram & Anne C. Elster. (2022). Applying Transfer Learning to Traffic Surveillance Videos for Accident Detection. 1–7. 3 indexed citations

5.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2021). Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing. 108. 107440–107440. 81 indexed citations

6.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2021). Deep learning-based detection and prediction of trending topics from streaming data. International Journal of Reasoning-based Intelligent Systems. 13(2). 59–59.

7.

Pathak, Ajeet Ram, et al.. (2020). Analysis of Techniques for Rumor Detection in Social Media. Procedia Computer Science. 167. 2286–2296. 44 indexed citations

8.

Pathak, Ajeet Ram, et al.. (2020). Personality analysis through handwriting recognition. Journal of Discrete Mathematical Sciences and Cryptography. 23(1). 19–33. 12 indexed citations

9.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2019). Adaptive Model for Dynamic and Temporal Topic Modeling from Big Data using Deep Learning Architecture. International Journal of Intelligent Systems and Applications. 11(6). 13–27. 12 indexed citations

10.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2019). Approaches of enhancing interoperations among high performance computing and big data analytics via augmentation. Cluster Computing. 23(2). 953–988. 9 indexed citations

11.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2019). Adaptive Framework for Deep Learning Based Dynamic and Temporal Topic Modeling from Big Data. Recent Patents on Engineering. 14(3). 394–402. 4 indexed citations

12.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2019). Empirical evaluation of deep learning models for sentiment analysis. Journal of Statistics and Management Systems. 22(4). 741–752. 6 indexed citations

13.

Pathak, Ajeet Ram. (2019). Social media data #bitcoin #Ethereum # facebook. Data Archiving and Networked Services (DANS). 1. 1 indexed citations

14.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2018). Application of Deep Learning for Object Detection. Procedia Computer Science. 132. 1706–1717. 337 indexed citations breakdown →

15.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2018). Deep Learning Approaches for Detecting Objects from Images: A Review. Advances in intelligent systems and computing. 491–499. 18 indexed citations

16.

Pathak, Ajeet Ram, Manjusha Pandey, & Siddharth Swarup Rautaray. (2018). Construing the big data based on taxonomy, analytics and approaches. Iran Journal of Computer Science. 1(4). 237–259. 14 indexed citations

17.

Jagtap, Vandana, et al.. (2014). Augmented Execution in Mobile Cloud Computing: A Survey. 2. 237–244. 3 indexed citations

18.

Pathak, Ajeet Ram, et al.. (2014). A secure threshold secret sharing framework for database outsourcing. 5. 1642–1649. 2 indexed citations

19.

Pathak, Ajeet Ram, et al.. (2013). Survey of Confidentiality and Integrity in Outsourced Databases. International Journal of Scientific Engineering and Technology. 2(3). 122–128. 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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