M. A. Rafe Biswas

1.0k total citations · 1 hit paper
16 papers, 812 citations indexed

About

M. A. Rafe Biswas is a scholar working on Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment and Automotive Engineering. According to data from OpenAlex, M. A. Rafe Biswas has authored 16 papers receiving a total of 812 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 7 papers in Renewable Energy, Sustainability and the Environment and 6 papers in Automotive Engineering. Recurrent topics in M. A. Rafe Biswas's work include Fuel Cells and Related Materials (8 papers), Electrocatalysts for Energy Conversion (7 papers) and Advanced Battery Technologies Research (6 papers). M. A. Rafe Biswas is often cited by papers focused on Fuel Cells and Related Materials (8 papers), Electrocatalysts for Energy Conversion (7 papers) and Advanced Battery Technologies Research (6 papers). M. A. Rafe Biswas collaborates with scholars based in United States, United Kingdom and Saudi Arabia. M. A. Rafe Biswas's co-authors include Nelson Fumo, Melvin Robinson, Tabbi Wilberforce, W. E. Lear and O.D. Crisalle and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and International Journal of Hydrogen Energy.

In The Last Decade

M. A. Rafe Biswas

14 papers receiving 797 citations

Hit Papers

align trajectories sort by overperformance

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.

Regression analysis for prediction of residential energy consumption

2015 480 citations Nelson Fumo, M. A. Rafe Biswas Renewable and Sustainable Energy Reviews profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. A. Rafe Biswas United States	7	476	402	207	170	90	16	812
Ahmad Sukri Ahmad Malaysia	6	448 0.9×	412 1.0×	145 0.7×	179 1.1×	111 1.2×	16	831
Fanyue Qian China	18	559 1.2×	231 0.6×	98 0.5×	118 0.7×	65 0.7×	49	868
A. D’Amico Italy	13	276 0.6×	434 1.1×	198 1.0×	279 1.6×	80 0.9×	23	832
Merih Aydınalp Köksal Türkiye	14	455 1.0×	588 1.5×	361 1.7×	271 1.6×	55 0.6×	33	997
Baran Yildiz Australia	9	609 1.3×	305 0.8×	138 0.7×	94 0.6×	117 1.3×	17	785
Shalika Walker Netherlands	9	394 0.8×	189 0.5×	149 0.7×	96 0.6×	182 2.0×	27	598
Yunyang Ye United States	16	438 0.9×	693 1.7×	164 0.8×	327 1.9×	75 0.8×	48	1.1k
Xiaoyun Zhao Sweden	9	334 0.7×	636 1.6×	151 0.7×	303 1.8×	77 0.9×	25	929
Mathieu Bourdeau France	4	383 0.8×	484 1.2×	150 0.7×	207 1.2×	55 0.6×	5	705

Countries citing papers authored by M. A. Rafe Biswas

Since Specialization

Citations

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

Fields of papers citing papers by M. A. Rafe Biswas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. A. Rafe Biswas. 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 M. A. Rafe Biswas. The network helps show where M. A. Rafe Biswas may publish in the future.

Co-authorship network of co-authors of M. A. Rafe Biswas

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

All Works

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16 of 16 papers shown

Biswas, M. A. Rafe, et al.. (2024). Simulated Laboratory-Based Learning In A Thermal Fluid Laboratory Course. Papers on Engineering Education Repository (American Society for Engineering Education). 1 indexed citations

Biswas, M. A. Rafe, et al.. (2024). Modeling of Dry Reforming of Methane Using Artificial Neural Networks. SHILAP Revista de lepidopterología. 5(4). 800–818. 1 indexed citations

Biswas, M. A. Rafe, et al.. (2024). Improve Technical Communication Using Scaffolding Method in Mechanical Engineering Courses. Papers on Engineering Education Repository (American Society for Engineering Education).

Biswas, M. A. Rafe & Tabbi Wilberforce. (2023). Dynamic thermal model development of direct methanol fuel cell. International Journal of Thermofluids. 17. 100294–100294. 6 indexed citations

Biswas, M. A. Rafe, et al.. (2023). Prediction of Transient Hydrogen Flow of Proton Exchange Membrane Electrolyzer Using Artificial Neural Network. SHILAP Revista de lepidopterología. 4(3). 542–555. 8 indexed citations

Wilberforce, Tabbi, et al.. (2022). Power and Voltage Modelling of a Proton-Exchange Membrane Fuel Cell Using Artificial Neural Networks. Energies. 15(15). 5587–5587. 6 indexed citations

Wilberforce, Tabbi & M. A. Rafe Biswas. (2022). A study into Proton Exchange Membrane Fuel Cell power and voltage prediction using Artificial Neural Network. Energy Reports. 8. 12843–12852. 20 indexed citations

Biswas, M. A. Rafe. (2020). Model Development of Solid Oxide Fuel Cell Thermal Performance Using Artificial Neural Network. 1 indexed citations

Biswas, M. A. Rafe & Melvin Robinson. (2017). Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network. Journal of Electrochemical Energy Conversion and Storage. 14(3). 13 indexed citations

10.

Biswas, M. A. Rafe, et al.. (2017). Automated Testing of Orion Cockpit Displays using EggPlant Functional and Python Programming.

11.

Biswas, M. A. Rafe, Melvin Robinson, & Nelson Fumo. (2016). Prediction of residential building energy consumption: A neural network approach. Energy. 117. 84–92. 240 indexed citations

12.

Biswas, M. A. Rafe & Melvin Robinson. (2015). Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network. 4 indexed citations

13.

Fumo, Nelson & M. A. Rafe Biswas. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews. 47. 332–343. 480 indexed citations breakdown →

14.

Biswas, M. A. Rafe, et al.. (2015). Comprehensive mass transport modeling technique for the cathode side of an open-cathode direct methanol fuel cell. International Journal of Hydrogen Energy. 40(25). 8137–8159. 12 indexed citations

15.

Biswas, M. A. Rafe, et al.. (2015). Validation of Neural Network Model for Residential Energy Consumption.. 3 indexed citations

16.

Biswas, M. A. Rafe, et al.. (2014). Systematic approach for modeling methanol mass transport on the anode side of direct methanol fuel cells. International Journal of Hydrogen Energy. 39(15). 8009–8025. 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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