Abbas Cheddad
I received my M.Sc in Computer Science in 2005 where I secured a fellowship award from Universiti Teknologi Malaysia. My work back then was dedicated to face segmentation and feature extraction, using Voronoi Triangulation, which generated multiple conference papers and a manuscript in the Pattern Recognition journal, Elsevier. I did my PhD at the Faculty of Computing and Engineering at the University of Ulster in Northern Ireland. My PhD’s focus was on strengthening steganography in digital images. My thesis, completed in 2009, was a worthwhile contribution to knowledge in the subject area and also produced two successful international patent applications. Current ongoing research with University of Ulster involves the commercialisation and continued development of the Steganoflage algorithm to support our spin-out company, HidInImage Ltd. My research interests fall into the general theme of computer vision. A special interest, however, is granted to the practical applications of image processing (e.g., medical imaging, 3D reconstruction, pattern recognition, biometrics, steganography). I have in records 12 international, peer reviewed, Journal papers, 26 peer reviewed international conference papers, 3 patents applications, a book and a book chapter. I worked as a post-doc at Umeå Centre for Molecular Medicine (UCMM) Umeå University, Sweden and I spent several years working at the department of Medical Epidemiology and Biostatistics at Karolinska Institute in Stockholm. I am currently an Associate Professor at the department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
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Papers by Abbas Cheddad
More info at==> https://ardisdataset.github.io/MiniDDSM
computer simulated experiments. However, high-fidelity simulations can
take significant time to compute. It is impractical to explore design space
by only conducting simulations because of time constraints. Hence, surrogate
modelling is used to approximate the original simulations. Since
simulations are expensive to conduct, generally, the sample size is limited
in aerospace engineering applications. This limited sample size, and also
non-linearity and high dimensionality of data make it difficult to generate
accurate and robust surrogate models. The aim of this paper is to
explore the applicability of Random Forests (RF) to construct surrogate
models to support design space exploration. RF generates meta-models
or ensembles of decision trees, and it is capable of fitting highly nonlinear
data given quite small samples. To investigate the applicability of
RF, this paper presents an approach to construct surrogate models using
RF. This approach includes hyper-parameter tuning to improve the performance
of the RF's model, to extract design parameters' importance
and if-then rules from the RF's models for better understanding of design
space. To demonstrate the approach using RF, quantitative experiments
are conducted with datasets of Turbine Rear Structure use-case from an
aerospace industry and results are presented.
The data sets are freely available from:
https://ardisdataset.github.io/ARDIS/
handwritten texts that exhibit a variety of perceptual
environment complexities. The cursive and connected nature of
text lines on one hand and the presence of artefacts and noise
on the other hand hinder achieving plausible results using
current image processing algorithm. In this paper, we present
a new algorithm which we termed QTE (Query by Text
Example) that allows for training-free and binarisation-free
pattern spotting in scanned handwritten historical documents.
Our algorithm gives promising results on a subset of our
database revealing ~83% success rate in locating word patterns
supplied by the user.
More info at==> https://ardisdataset.github.io/MiniDDSM
computer simulated experiments. However, high-fidelity simulations can
take significant time to compute. It is impractical to explore design space
by only conducting simulations because of time constraints. Hence, surrogate
modelling is used to approximate the original simulations. Since
simulations are expensive to conduct, generally, the sample size is limited
in aerospace engineering applications. This limited sample size, and also
non-linearity and high dimensionality of data make it difficult to generate
accurate and robust surrogate models. The aim of this paper is to
explore the applicability of Random Forests (RF) to construct surrogate
models to support design space exploration. RF generates meta-models
or ensembles of decision trees, and it is capable of fitting highly nonlinear
data given quite small samples. To investigate the applicability of
RF, this paper presents an approach to construct surrogate models using
RF. This approach includes hyper-parameter tuning to improve the performance
of the RF's model, to extract design parameters' importance
and if-then rules from the RF's models for better understanding of design
space. To demonstrate the approach using RF, quantitative experiments
are conducted with datasets of Turbine Rear Structure use-case from an
aerospace industry and results are presented.
The data sets are freely available from:
https://ardisdataset.github.io/ARDIS/
handwritten texts that exhibit a variety of perceptual
environment complexities. The cursive and connected nature of
text lines on one hand and the presence of artefacts and noise
on the other hand hinder achieving plausible results using
current image processing algorithm. In this paper, we present
a new algorithm which we termed QTE (Query by Text
Example) that allows for training-free and binarisation-free
pattern spotting in scanned handwritten historical documents.
Our algorithm gives promising results on a subset of our
database revealing ~83% success rate in locating word patterns
supplied by the user.