Lachit Dutta

1.1k total citations · 1 hit paper
27 papers, 732 citations indexed

About

Lachit Dutta is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering and Cognitive Neuroscience. According to data from OpenAlex, Lachit Dutta has authored 27 papers receiving a total of 732 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Biomedical Engineering, 8 papers in Electrical and Electronic Engineering and 7 papers in Cognitive Neuroscience. Recurrent topics in Lachit Dutta's work include Advanced Chemical Sensor Technologies (8 papers), Muscle activation and electromyography studies (7 papers) and EEG and Brain-Computer Interfaces (7 papers). Lachit Dutta is often cited by papers focused on Advanced Chemical Sensor Technologies (8 papers), Muscle activation and electromyography studies (7 papers) and EEG and Brain-Computer Interfaces (7 papers). Lachit Dutta collaborates with scholars based in India, Malta and China. Lachit Dutta's co-authors include Pranjal Barman, Brian Azzopardi, Anamika Kalita, Sushanta Bordoloi, Anil Hazarika, Manabendra Bhuyan, Nillohit Mukherjee, Biplob Mondal, Dambarudhar Mohanta and Hiranmay Saha and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Scientific Reports and IEEE Transactions on Industrial Electronics.

In The Last Decade

Lachit Dutta

22 papers receiving 701 citations

Hit Papers

Renewable energy integration with electric vehicle techno... 2023 2026 2024 2025 2023 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lachit Dutta India 12 396 215 153 84 68 27 732
Liyuan Wang China 9 453 1.1× 130 0.6× 200 1.3× 87 1.0× 23 0.3× 38 790
Turki Alsuwian Saudi Arabia 18 338 0.9× 106 0.5× 54 0.4× 54 0.6× 64 0.9× 49 773
R. Neelaveni India 11 326 0.8× 98 0.5× 86 0.6× 105 1.3× 34 0.5× 54 575
K. A. Mamun Fiji 20 522 1.3× 116 0.5× 62 0.4× 130 1.5× 54 0.8× 57 1.2k
Yuh‐Chung Hu Taiwan 17 258 0.7× 49 0.2× 179 1.2× 145 1.7× 87 1.3× 49 846
B. S. K. K. Ibrahim Malaysia 15 181 0.5× 161 0.7× 246 1.6× 32 0.4× 48 0.7× 74 834
Yi‐Chung Chen Taiwan 16 285 0.7× 40 0.2× 101 0.7× 48 0.6× 58 0.9× 91 781
M. M. Tripathi India 21 1.1k 2.8× 117 0.5× 139 0.9× 57 0.7× 262 3.9× 112 1.4k
Muhammad Tanveer Riaz Pakistan 17 371 0.9× 90 0.4× 115 0.8× 89 1.1× 48 0.7× 69 757

Countries citing papers authored by Lachit Dutta

Since Specialization
Citations

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

Fields of papers citing papers by Lachit Dutta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lachit Dutta

This figure shows the co-authorship network connecting the top 25 collaborators of Lachit Dutta. A scholar is included among the top collaborators of Lachit Dutta 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 Lachit Dutta. Lachit Dutta 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.
Dutta, Lachit, et al.. (2025). Tiny ML based crop recommendation system for precision agriculture 5.0. Smart Agricultural Technology. 12. 101247–101247.
2.
Sarma, Kandarpa Kumar, et al.. (2025). Leveraging federated learning and edge computing for pandemic-resilient healthcare. Scientific Reports. 15(1). 20497–20497. 1 indexed citations
3.
Sarma, Kandarpa Kumar, et al.. (2024). Analysis of data of COVID lockdown period: Comorbidity and fatality rates in a few districts of Assam, India. Data in Brief. 57. 110974–110974.
4.
Das, Arunava, et al.. (2024). On-site fish freshness monitoring using smartphone. Microsystem Technologies. 31(3). 829–843.
5.
Dutta, Lachit, et al.. (2024). Modern Thyroid Cancer Diagnosis: A Review of AI-Powered Algorithms for Detection and Classification. INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING.
6.
Dutta, Lachit, et al.. (2024). An IoT-Enabled Smart pH Monitoring and Dispensing System for Precision Agriculture Application. Agricultural Research. 13(2). 309–318. 6 indexed citations
7.
Barman, Prasenjit, Lachit Dutta, & Brian Azzopardi. (2023). Electric vehicle battery supply chain and critical materials: a brief survey of state of the art. IET conference proceedings.. 2022(25). 470–477.
8.
Barman, Pranjal, Lachit Dutta, & Brian Azzopardi. (2023). Electric Vehicle Battery Supply Chain and Critical Materials: A Brief Survey of State of the Art. Energies. 16(8). 3369–3369. 40 indexed citations
9.
Bhuyan, Manabendra, et al.. (2023). Model-Based Derivation of Electrical Parameters and Impedance Study of Two-Electrode Copper Nanoparticle-Based Pencil-Drawn Glucose Sensor. IEEE Transactions on Industrial Informatics. 20(2). 2897–2906. 1 indexed citations
10.
Hazarika, Anil, et al.. (2022). A fully handwritten-on-paper copper nanoparticle ink-based electroanalytical sweat glucose biosensor fabricated using dual-step pencil and pen approach. Analytica Chimica Acta. 1227. 340257–340257. 23 indexed citations
11.
Hazarika, Anil, et al.. (2019). Real-Time Implementation of a Multidomain Feature Fusion Model Using Inherently Available Large Sensor Data. IEEE Transactions on Industrial Informatics. 15(12). 6231–6239. 13 indexed citations
12.
Hazarika, Anil, et al.. (2018). An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification. International Journal of Multimedia Information Retrieval. 7(3). 173–186. 15 indexed citations
13.
Hazarika, Anil, et al.. (2018). F-SVD based algorithm for variability and stability measurement of bio-signals, feature extraction and fusion for pattern recognition. Biomedical Signal Processing and Control. 47. 26–40. 14 indexed citations
14.
Dutta, Lachit, Anil Hazarika, & Manabendra Bhuyan. (2017). Direct interfacing circuit‐based e‐nose for gas classification and its uncertainty estimation. IET Circuits Devices & Systems. 12(1). 63–72. 7 indexed citations
15.
Hazarika, Anil, et al.. (2017). Multi-view learning for classification of EMG template. 467–471. 3 indexed citations
16.
Dutta, Lachit, et al.. (2017). A Novel Low-Cost Hand-Held Tea Flavor Estimation System. IEEE Transactions on Industrial Electronics. 65(6). 4983–4990. 42 indexed citations
17.
Hazarika, Anil, et al.. (2016). Two-fold feature extraction technique for biomedical signals classification. 1–4. 7 indexed citations
18.
Dutta, Lachit, Anil Hazarika, & Manabendra Bhuyan. (2016). Comparison of direct interfacing and ADC based system for gas identification using E-Nose. 15–19. 2 indexed citations
19.
Hazarika, Anil, et al.. (2016). Fusion of projected feature for classification of EMG patterns. 69–74. 10 indexed citations
20.
Mondal, Biplob, et al.. (2014). Effect of Annealing Temperature on the Morphology and Sensitivity of the Zinc Oxide Nanorods-Based Methane Senor. Acta Metallurgica Sinica (English Letters). 27(4). 593–600. 13 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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