R
Ripon K. Chakrabortty
Researcher at University of New South Wales
Publications - 183
Citations - 3356
Ripon K. Chakrabortty is an academic researcher from University of New South Wales. The author has contributed to research in topics: Computer science & Supply chain. The author has an hindex of 17, co-authored 123 publications receiving 960 citations. Previous affiliations of Ripon K. Chakrabortty include Australian Defence Force Academy & Rajshahi University of Engineering & Technology.
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Journal ArticleDOI
A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection
TL;DR: A novel framework is elaborated which combines AHP and TOPSIS with a spherical fuzzy set, which is effective in handling uncertainty in decision making and leads to robust and competitive results compared with state-of-the-art multi-criteria decision-making (MCDM) approaches.
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A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy
TL;DR: A hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation is proposed and the ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations.
Proceedings ArticleDOI
Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems
TL;DR: An improved optimization algorithm is proposed that uses the benefits of multiple differential evolution operators, with more emphasis placed on the best-performing operator, with its results outperforming both single operator-based and different state-of-the-art algorithms.
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A new hybrid multi-criteria decision-making approach for location selection of sustainable offshore wind energy stations: A case study
TL;DR: A new hybrid methodology for the selection of offshore wind power station location combining the Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)-II methods in the neutrosophic environment is proposed.
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FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection
TL;DR: A novel dual-path deep-learning architecture for FSS that permits intensive knowledge exchange between paths with a trivial increase in computational complexity is proposed and is extended to multi-class labeling for various types of lung infections.