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Nihad Karim Chowdhury
Researcher at University of Chittagong
Publications - 20
Citations - 469
Nihad Karim Chowdhury is an academic researcher from University of Chittagong. The author has contributed to research in topics: Intelligent transportation system & Computer science. The author has an hindex of 10, co-authored 18 publications receiving 328 citations. Previous affiliations of Nihad Karim Chowdhury include University of Manitoba & Chonbuk National University.
Papers
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Journal ArticleDOI
A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries
Bo Li,Yijuan Lu,Chunyuan Li,Afzal Godil,Tobias Schreck,Masaki Aono,Martin Burtscher,Qiang Chen,Nihad Karim Chowdhury,Bin Fang,Hongbo Fu,Takahiko Furuya,Haisheng Li,Jianzhuang Liu,Henry Johan,Ryuichi Kosaka,Hitoshi Koyanagi,Ryutarou Ohbuchi,Atsushi Tatsuma,Yajuan Wan,Chaoli Zhang,Changqing Zou +21 more
TL;DR: A more comprehensive comparison of twenty-six 3D shape retrieval methods is performed by evaluating them on the common benchmark, compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods.
Journal ArticleDOI
PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images
TL;DR: Wang et al. as mentioned in this paper proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest X-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet).
Journal ArticleDOI
ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19
TL;DR: An ensemble of deep convolutional neural networks based on EfficientNet, named ECOVNet, is proposed to detect COVID-19 using a large chest X-ray data set to ameliorate classification performance and generalization in the related task of classifyingchest X-rays.
Book ChapterDOI
Modified K-means clustering for travel time prediction based on historical traffic data
TL;DR: The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).
Journal ArticleDOI
Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method
Nihad Karim Chowdhury,Muhammad Ashad Kabir,Md. Muhtadir Rahman,Sheikh Mohammed Shariful Islam +3 more
TL;DR: In this article , an ensemble-based multi-criteria decision-making (MCDM) method was proposed for selecting top performance machine learning technique(s) for COVID-19 cough classification.