Jivan S. Parab

579 total citations
34 papers, 336 citations indexed

About

Jivan S. Parab is a scholar working on Analytical Chemistry, Biomedical Engineering and Biophysics. According to data from OpenAlex, Jivan S. Parab has authored 34 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Analytical Chemistry, 10 papers in Biomedical Engineering and 8 papers in Biophysics. Recurrent topics in Jivan S. Parab's work include Spectroscopy and Chemometric Analyses (17 papers), Spectroscopy Techniques in Biomedical and Chemical Research (8 papers) and Advanced Chemical Sensor Technologies (6 papers). Jivan S. Parab is often cited by papers focused on Spectroscopy and Chemometric Analyses (17 papers), Spectroscopy Techniques in Biomedical and Chemical Research (8 papers) and Advanced Chemical Sensor Technologies (6 papers). Jivan S. Parab collaborates with scholars based in India, Netherlands and United Kingdom. Jivan S. Parab's co-authors include Madhusudan G. Lanjewar, G. M. Naik, Pranay P. Morajkar, Rajanish K. Kamat, U. Snekhalatha and Irwin Nazareth and has published in prestigious journals such as Journal of Applied Physics, Food Chemistry and Review of Scientific Instruments.

In The Last Decade

Jivan S. Parab

31 papers receiving 322 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jivan S. Parab India 11 115 90 60 47 38 34 336
Jie Hao China 12 210 1.8× 106 1.2× 28 0.5× 48 1.0× 48 1.3× 23 388
Habil Kalkan Türkiye 9 119 1.0× 67 0.7× 87 1.4× 49 1.0× 29 0.8× 36 317
Kamini G. Panchbhai India 9 93 0.8× 39 0.4× 98 1.6× 48 1.0× 20 0.5× 17 275
Madhusudan G. Lanjewar India 15 201 1.7× 100 1.1× 187 3.1× 98 2.1× 58 1.5× 33 577
Marsyita Hanafi Malaysia 11 212 1.8× 61 0.7× 215 3.6× 21 0.4× 27 0.7× 55 497
Arlindo Rodrigues Galvão Filho Brazil 8 254 2.2× 81 0.9× 53 0.9× 42 0.9× 67 1.8× 50 420
Md. Toukir Ahmed United States 11 184 1.6× 68 0.8× 45 0.8× 35 0.7× 37 1.0× 25 305
Xinhao Yang China 14 244 2.1× 110 1.2× 48 0.8× 17 0.4× 94 2.5× 41 624
Chenhao Cui China 7 226 2.0× 129 1.4× 81 1.4× 29 0.6× 34 0.9× 15 566
Jiayi Zhu China 15 106 0.9× 100 1.1× 68 1.1× 30 0.6× 62 1.6× 31 628

Countries citing papers authored by Jivan S. Parab

Since Specialization
Citations

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

Fields of papers citing papers by Jivan S. Parab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jivan S. Parab. 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 Jivan S. Parab. The network helps show where Jivan S. Parab may publish in the future.

Co-authorship network of co-authors of Jivan S. Parab

This figure shows the co-authorship network connecting the top 25 collaborators of Jivan S. Parab. A scholar is included among the top collaborators of Jivan S. Parab 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 Jivan S. Parab. Jivan S. Parab 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.
Lanjewar, Madhusudan G., Pranay P. Morajkar, & Jivan S. Parab. (2025). Robust method for detecting metanil yellow in turmeric: Integrating Vis-NIR spectroscopy and machine learning. Journal of Food Composition and Analysis. 142. 107409–107409. 1 indexed citations
2.
Parab, Jivan S., et al.. (2025). Detection and quantification of formaldehyde adulteration in cow and buffalo milk using UV–Vis-NIR spectroscopy with machine learning. Food Chemistry. 492(Pt 2). 145485–145485. 1 indexed citations
3.
Lanjewar, Madhusudan G., et al.. (2024). Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning. Journal of Food Composition and Analysis. 136. 106700–106700. 14 indexed citations
4.
Lanjewar, Madhusudan G., et al.. (2024). Hybrid methods for detection of starch in adulterated turmeric from colour images. Multimedia Tools and Applications. 83(25). 65789–65814. 2 indexed citations
5.
Lanjewar, Madhusudan G., Jivan S. Parab, & Rajanish K. Kamat. (2024). Machine learning based technique to predict the water adulterant in milk using portable near infrared spectroscopy. Journal of Food Composition and Analysis. 131. 106270–106270. 14 indexed citations
6.
Lanjewar, Madhusudan G., Pranay P. Morajkar, & Jivan S. Parab. (2023). Portable system to detect starch adulteration in turmeric using NIR spectroscopy. Food Control. 155. 110095–110095. 36 indexed citations
7.
Parab, Jivan S., et al.. (2023). Python Programming Recipes for IoT Applications. 1 indexed citations
8.
Parab, Jivan S., et al.. (2023). Efficient Deep Learning model for de-husked Areca nut classification. Journal of Applied and Natural Science. 15(4). 1529–1540. 1 indexed citations
9.
Lanjewar, Madhusudan G., Pranay P. Morajkar, & Jivan S. Parab. (2023). Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy. Food Additives & Contaminants Part A. 40(9). 1131–1146. 7 indexed citations
10.
Lanjewar, Madhusudan G., et al.. (2022). Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone. Multimedia Tools and Applications. 82(19). 29883–29912. 8 indexed citations
11.
Lanjewar, Madhusudan G., et al.. (2022). Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images. Multimedia Tools and Applications. 82(8). 12699–12717. 30 indexed citations
12.
Parab, Jivan S., et al.. (2021). Backpropagation Neural Network-Based Machine Learning Model for Prediction of Blood Urea and Glucose in CKD Patients. IEEE Journal of Translational Engineering in Health and Medicine. 9. 1–8. 24 indexed citations
13.
Parab, Jivan S., et al.. (2021). Back-Propagation Neural Network (BP-NN) model for the detection of borer pest attack. Journal of Physics Conference Series. 1921(1). 12079–12079. 5 indexed citations
14.
Parab, Jivan S., et al.. (2021). Improving hemoglobin estimation accuracy through standardizing of light-emitting diode power. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering. 12(1). 219–219. 5 indexed citations
15.
Parab, Jivan S., et al.. (2018). Estimation of glucose using fixed wavelength NIR light sources. 1 indexed citations
16.
Parab, Jivan S., et al.. (2016). Error analysis in soil urea prediction based on RF spectroscopy. 244–246.
17.
Parab, Jivan S., et al.. (2016). Influence of PCA components on glucose prediction using non-invasive technique. 37. 473–476. 1 indexed citations
18.
Parab, Jivan S., et al.. (2015). RF spectroscopy technique for soil nutrient analysis. 1–4. 4 indexed citations
19.
Parab, Jivan S., et al.. (2010). Noninvasive glucometer model using partial least square regression technique for human blood matrix. Journal of Applied Physics. 107(10). 15 indexed citations
20.
Parab, Jivan S., et al.. (2008). Practical Aspects of Embedded System Design using Microcontrollers. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 9 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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