Prabal Kumar Majumdar

542 total citations
24 papers, 419 citations indexed

About

Prabal Kumar Majumdar is a scholar working on Polymers and Plastics, Industrial and Manufacturing Engineering and Management Science and Operations Research. According to data from OpenAlex, Prabal Kumar Majumdar has authored 24 papers receiving a total of 419 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Polymers and Plastics, 9 papers in Industrial and Manufacturing Engineering and 5 papers in Management Science and Operations Research. Recurrent topics in Prabal Kumar Majumdar's work include Textile materials and evaluations (19 papers), Industrial Vision Systems and Defect Detection (8 papers) and Multi-Criteria Decision Making (5 papers). Prabal Kumar Majumdar is often cited by papers focused on Textile materials and evaluations (19 papers), Industrial Vision Systems and Defect Detection (8 papers) and Multi-Criteria Decision Making (5 papers). Prabal Kumar Majumdar collaborates with scholars based in India, Canada and Spain. Prabal Kumar Majumdar's co-authors include Abhijit Majumdar, Bijan Sarkar, Bijan Sarkar, Debamalya Banerjee, Anindya Ghosh, Prabir Ray, Núria López, Shijing Sun, Long Qi and John R. Kitchin and has published in prestigious journals such as Nature Catalysis, Experimental Thermal and Fluid Science and Textile Research Journal.

In The Last Decade

Prabal Kumar Majumdar

22 papers receiving 379 citations

Peers

Prabal Kumar Majumdar

IME

MSOR

Sarbjeet Singh Sweden

Zoran Stjepanović Slovenia

Anna Chatzimichali United Kingdom

Muhammad Atif Shahzad Saudi Arabia

Aleksandra Sretenović Serbia

R.T. Durai Prabhakaran India

Balasubramanian Murugesan India

Ching‐Kao Chang Taiwan

P. V. Mohanram India

Kodo Ito Japan

Sarbjeet Singh Sweden

Prabal Kumar Majumdar

110 ×0.4

20 ×0.1

IME

25 ×0.3

24 ×0.3

22 ×0.4

MSOR

Citations per year, relative to Prabal Kumar Majumdar Prabal Kumar Majumdar (= 1×) peers Sarbjeet Singh

Countries citing papers authored by Prabal Kumar Majumdar

Since Specialization

Citations

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

Fields of papers citing papers by Prabal Kumar Majumdar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prabal Kumar Majumdar. 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 Prabal Kumar Majumdar. The network helps show where Prabal Kumar Majumdar may publish in the future.

Co-authorship network of co-authors of Prabal Kumar Majumdar

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

All Works

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20 of 20 papers shown

Bennett, Jeffrey A., Long Qi, Shijing Sun, et al.. (2025). Autonomous catalysis research with human–AI–robot collaboration. Nature Catalysis. 8(11). 1135–1145.

Majumdar, Prabal Kumar, et al.. (2015). Effect of blend composition on tensile properties of blended Dref-III yarns. Indian Journal of Fibre & Textile Research (IJFTR). 40(1). 36–42.

Mukhopadhyay, Asis, et al.. (2014). Mechanics of filled jute bag. Journal of the Textile Institute. 106(2). 206–211. 1 indexed citations

Majumdar, Abhijit, et al.. (2014). Selection of Handloom Fabrics for Summer Clothing Using Multi-Criteria Decision Making Techniques. Journal of Natural Fibers. 12(1). 61–71. 27 indexed citations

Majumdar, Prabal Kumar, et al.. (2014). Production of Engineered Fabrics Using Artificial Neural Network–Genetic Algorithm Hybrid Model. Journal of The Institution of Engineers (India) Series E. 96(2). 159–165. 7 indexed citations

Majumdar, Prabal Kumar, et al.. (2013). Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models. Journal of The Institution of Engineers (India) Series E. 94(1). 29–36. 4 indexed citations

Majumdar, Abhijit, et al.. (2012). Comparative analysis of regression and ANN models for predicting drape coefficient of handloom fabrics. 1 indexed citations

Majumdar, Abhijit, et al.. (2010). Soft Computing Applications in Fabrics and Clothing: A Comprehensive Review. Research Journal of Textile and Apparel. 14(1). 1–17. 11 indexed citations

Ray, Prabir, et al.. (2008). Effect of punch density, depth of needle penetration and mass per unit area on compressional behaviour of jute needle-punched nonwoven fabrics using central composite rotatable experimental design. 3 indexed citations

10.

Majumdar, Prabal Kumar, et al.. (2008). Tensile deformation of jute needle-punched nonwoven geotextiles under compressive load. 1 indexed citations

11.

Majumdar, Abhijit, Bijan Sarkar, & Prabal Kumar Majumdar. (2006). A new algorithm of cot ton fibre select ion and laydown using TOPSIS method of multi-cr iteria decision making. 9 indexed citations

12.

Majumdar, Abhijit, Prabal Kumar Majumdar, & Bijan Sarkar. (2006). An investigation on yarn engineering using artificial neural networks. Journal of the Textile Institute. 97(5). 429–434. 17 indexed citations

13.

Ray, Prabir, et al.. (2005). Effect of dynamic loading on jute-based needle-punched nonwoven fabrics. Indian Journal of Fibre & Textile Research. 30(4). 389–395. 5 indexed citations

14.

Majumdar, Abhijit, Prabal Kumar Majumdar, & Bijan Sarkar. (2005). Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. Journal of the Textile Institute. 96(1). 55–60. 59 indexed citations

15.

Majumdar, Abhijit, Prabal Kumar Majumdar, & Bijan Sarkar. (2005). DETERMINATION OF THE TECHNOLOGICAL VALUE OF COTTON FIBRE: A COMPARATIVE STUDY OF THE TRADITIONAL AND MULTIPLE-CRITERIA DECISION-MAKING APPROACHES. Autex Research Journal. 5(2). 71–80. 30 indexed citations

16.

Majumdar, Abhijit, Bijan Sarkar, & Prabal Kumar Majumdar. (2005). Determination of quality value of cotton fibre using hybrid AHP-TOPSIS method of multi-criteria decision-making. Journal of the Textile Institute. 96(5). 303–309. 50 indexed citations

17.

Majumdar, Abhijit, Prabal Kumar Majumdar, & Bijan Sarkar. (2005). Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns. 13 indexed citations

18.

Majumdar, Prabal Kumar & Abhijit Majumdar. (2004). Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models. Textile Research Journal. 74(7). 652–655. 98 indexed citations

19.

Majumdar, Abhijit, Bijan Sarkar, & Prabal Kumar Majumdar. (2004). Application of analytic hierarchy process for the selection of cotton fibers. Fibers and Polymers. 5(4). 297–302. 28 indexed citations

20.

Majumdar, Abhijit, Prabal Kumar Majumdar, & Bijan Sarkar. (2004). Prediction of single yarn tenacity of ring-and rotor-spun yarns from HVI results using artificial neural networks. 5 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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