Nihad Karim Chowdhury

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The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database that contains different types of models, such as generic, articulated, CAD and architecture models. The track is based on a new comprehensive 3D shape benchmark, which contains 8,987 triangle meshes that are classified(More)
Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports(More)
Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data(More)
Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this study we proposed a new method to predict travel times using Artificial Neural Network model(More)
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