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J. Hossen

Researcher at Multimedia University

Publications -  48
Citations -  357

J. Hossen is an academic researcher from Multimedia University. The author has contributed to research in topics: Computer science & Big data. The author has an hindex of 10, co-authored 40 publications receiving 248 citations. Previous affiliations of J. Hossen include Valeo & University of Memphis.

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A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics

TL;DR: Data quality troubles which may occur in big data processing to understand clearly why an organization requires data cleaning are examined, followed by data quality criteria (dimensions used to indicate data quality) and cleaning tools available in market are summarized.
Proceedings ArticleDOI

DeepTrailerAssist: Deep Learning Based Trailer Detection, Tracking and Articulation Angle Estimation on Automotive Rear-View Camera

TL;DR: This work presents all the trailer assist use cases in detail and proposes a deep learning based solution for trailer perception problems using a proprietary dataset comprising of 11 different trailer types to achieve a reasonable detection accuracy.
Journal ArticleDOI

A Comprehensive Review of Efficient Ray-Tracing Techniques for Wireless Communication

TL;DR: Outlines to decrease the computational time and to increase accuracy are the main focus of ray tracing techniques presented to remove the limitation of the uniform geometrical theory of diffraction (UTD) and geometric optics (GO).
Journal ArticleDOI

Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

TL;DR: In this article, the potentials of an aircraft model without and with winglet attached with NACA wing No. 65-3-218 were analyzed based on the longitudinal aerodynamic characteristics analyzing for the aircraft model tested in low subsonic wind tunnel, the lift coefficient (C L ) and drag coefficient ( C D ) were investigated respectively.
Journal Article

Dynamic Clustering Estimation of Tool Flank Wear in Turning Process using SVD Models of the Emitted Sound Signals

TL;DR: In this paper, the authors used SVD features with the Fuzzy C means classification on the emitted sound signal of a fresh tool (0 mm flank wear) and a slightly worn tool ( 0.2 -0.25 mm flank worn) during turning process were recorded separately using a high sensitive microphone.