Chandreyee Chowdhury

1.8k total citations
96 papers, 1.0k citations indexed

About

Chandreyee Chowdhury is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chandreyee Chowdhury has authored 96 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Electrical and Electronic Engineering, 48 papers in Computer Networks and Communications and 24 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chandreyee Chowdhury's work include Indoor and Outdoor Localization Technologies (25 papers), Context-Aware Activity Recognition Systems (24 papers) and IoT and Edge/Fog Computing (20 papers). Chandreyee Chowdhury is often cited by papers focused on Indoor and Outdoor Localization Technologies (25 papers), Context-Aware Activity Recognition Systems (24 papers) and IoT and Edge/Fog Computing (20 papers). Chandreyee Chowdhury collaborates with scholars based in India, United Kingdom and Pakistan. Chandreyee Chowdhury's co-authors include Priya Roy, Sarmistha Neogy, Sarbani Roy, Nauman Aslam, Joy Dutta, Sanghamitra Bandyopadhyay, Suparna Biswas, Asif Iqbal Middya, Anindita Saha and Ujjwal Maulik and has published in prestigious journals such as Expert Systems with Applications, Sensors and Applied Soft Computing.

In The Last Decade

Chandreyee Chowdhury

89 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chandreyee Chowdhury India 17 480 377 234 160 146 96 1.0k
Xiaonan Guo United States 17 402 0.8× 263 0.7× 282 1.2× 188 1.2× 193 1.3× 49 970
Óscar Belmonte Spain 19 605 1.3× 244 0.6× 225 1.0× 63 0.4× 232 1.6× 52 1.1k
Qingchang Zhu Singapore 9 557 1.2× 224 0.6× 336 1.4× 122 0.8× 145 1.0× 10 920
Fan Ye United States 20 931 1.9× 773 2.1× 176 0.8× 102 0.6× 228 1.6× 96 1.8k
Nobuo Kawaguchi Japan 17 327 0.7× 188 0.5× 360 1.5× 63 0.4× 143 1.0× 143 1.1k
Victor Bahl United States 13 756 1.6× 777 2.1× 199 0.9× 59 0.4× 126 0.9× 35 1.2k
Alessandro E. C. Redondi Italy 21 549 1.1× 612 1.6× 506 2.2× 80 0.5× 74 0.5× 92 1.3k
Jochen Seitz Germany 17 626 1.3× 479 1.3× 110 0.5× 71 0.4× 153 1.0× 107 1.1k
José R. Casar Spain 16 397 0.8× 283 0.8× 385 1.6× 66 0.4× 87 0.6× 92 1.1k
Kumbesan Sandrasegaran Australia 22 1.3k 2.8× 1.1k 2.9× 193 0.8× 107 0.7× 114 0.8× 140 1.8k

Countries citing papers authored by Chandreyee Chowdhury

Since Specialization
Citations

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

Fields of papers citing papers by Chandreyee Chowdhury

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chandreyee Chowdhury

This figure shows the co-authorship network connecting the top 25 collaborators of Chandreyee Chowdhury. A scholar is included among the top collaborators of Chandreyee Chowdhury 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 Chandreyee Chowdhury. Chandreyee Chowdhury 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.
Chowdhury, Chandreyee, et al.. (2025). Design of an end-to-end recommendation system for crowdsourced road monitoring applications based on machine learning. Multimedia Tools and Applications. 84(30). 37191–37213.
2.
Roy, Priya & Chandreyee Chowdhury. (2024). A region-wise indoor localization system based on unsupervised learning and ant colony optimization technique. Applied Soft Computing. 157. 111509–111509. 4 indexed citations
3.
Neogy, Sarmistha, et al.. (2024). Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach. Expert Systems with Applications. 257. 125063–125063. 2 indexed citations
4.
Neogy, Sarmistha, et al.. (2023). A hybrid tuple selection pipeline for smartphone based Human Activity Recognition. Expert Systems with Applications. 217. 119536–119536. 12 indexed citations
5.
Sadhukhan, Pampa, Chandreyee Chowdhury, Sara Paiva, et al.. (2023). IRT-SD-SLE: An Improved Real-Time Step Detection and Step Length Estimation Using Smartphone Accelerometer. IEEE Sensors Journal. 23(24). 30858–30868. 4 indexed citations
6.
Saraswat, Mukesh, et al.. (2023). Proceedings of International Conference on Data Science and Applications. Lecture notes in networks and systems. 2 indexed citations
7.
Chowdhury, Chandreyee, et al.. (2023). Novel Data Transmission Schemes for Inter-WBAN Networks Using Markov Decision Process. Wireless Personal Communications. 131(2). 897–919. 1 indexed citations
8.
Chowdhury, Chandreyee, et al.. (2023). Characteristic analysis of fingerprint datasets from a pragmatic view of indoor localization using machine learning approaches. The Journal of Supercomputing. 79(16). 18507–18546. 5 indexed citations
9.
Saha, Anindita, et al.. (2023). Designing a Meta Learning Classifier for Sensor-Enabled Healthcare Applications. SN Computer Science. 5(1).
10.
Chowdhury, Chandreyee, et al.. (2022). Designing Data Validation Framework for Crowd-Sourced Road Monitoring Applications. Journal of The Institution of Engineers (India) Series B. 103(4). 1083–1096. 2 indexed citations
11.
Saha, Anindita, et al.. (2022). A human activity recognition framework for grossly labeled smartphone sensing data through combining genetic algorithm with multiple instance multiple label learning. Multimedia Tools and Applications. 81(17). 24887–24911. 4 indexed citations
12.
Neogy, Sarmistha, et al.. (2021). A novel feature based ensemble learning model for indoor localization of smartphone users. Engineering Applications of Artificial Intelligence. 107. 104538–104538. 25 indexed citations
13.
Chowdhury, Chandreyee, et al.. (2018). An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †. Information. 9(4). 94–94. 29 indexed citations
14.
Chowdhury, Chandreyee, et al.. (2018). Designing Transmission Strategies for Enhancing Communications in Medical IoT Using Markov Decision Process. Sensors. 18(12). 4450–4450. 22 indexed citations
15.
Dutta, Joy, et al.. (2017). Towards Smart City. 1–6. 56 indexed citations
16.
17.
Chowdhury, Chandreyee, et al.. (2016). Crash failure immune offloading framework. 10. 1–6. 1 indexed citations
18.
Chowdhury, Chandreyee, et al.. (2016). Design of cluster-chain based WSN for energy efficiency. 52. 150–154. 2 indexed citations
19.
Chowdhury, Chandreyee, et al.. (2014). Developing Secured MANET Using Trust. 183–186. 1 indexed citations
20.
Neogy, Sarmistha, et al.. (2012). Reputation based trust management system for MANET. 376–381. 17 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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