Dr Ahmet Orun

Job: Lecturer (External Assoc. Professor)

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence

Address: De Montfort University, The Gateway, Leicester, LE1 9BH UK

T: N/A

E: aorun@dmu.ac.uk

W: www.cci.dmu.ac.uk

 

Publications and outputs 

  • Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas
    Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas Orun, A.; Elizondo, David; Goodyer, E.; Paluszczyszyn, D. Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system The file attached to this record is the author's final peer reviewed version.
  • Low-Cost Automatic Ambient Assisted Living system
    Low-Cost Automatic Ambient Assisted Living system Malekmohamadi, Hossein; Moemeni, A.; Orun, A.; Kumar, J. The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF). The file attached to this record is the author's final peer reviewed version.
  • Micro-structural analysis of tablet surface layers by intelligent laser speckle classification (ILSC) technique: An application in the study of both surface defects and subsurface granule structures
    Micro-structural analysis of tablet surface layers by intelligent laser speckle classification (ILSC) technique: An application in the study of both surface defects and subsurface granule structures Orun, A.; Smith, Geoff Purpose : As a consequence of the latest developments in laser technologies it is now possible to develop a low-cost and accurate tablet inspection system by the unification of optical and artificial intelligence methods. Method: The functionality of the proposed system is based on a sequence of texture analysis of laser speckle images (using laser sources of 650 nm and 808 nm : VIS/IR) followed by the optimization of texture parameters using Bayesian Networks (BN). Results: In the first part of this work, a Bayesian inference method was used to detect micro-scale tablet defects that are generated “progressively” during production whereas in the second part a Bayesian classifier method was used to discriminate between tablets made from different granule sizes. In part two, it was shown that (i) the comparatively higher energy (5mW) IR laser light generates different speckle effects than the lower energy visible (Red 3mW) by interacting with deeper sub-surface of the tablets and (ii) by using multi-classifier systems (MCS) to fuse the Bayesian classifiers from both types of speckle images it was possible to achieve a higher discrimination power (88% classification accuracy) for distinguishing between tablets made from different granule sizes than one can achieve from a single image type. Conclusion: It is suggested that this unified method has the potential to provide for a comprehensive analysis of both tablet quality attributes, on the one hand, and failure modes, on the other, that might be used in the development of a low cost tablet inspection system. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution
    An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution Elizondo, David; Orun, A. Traffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis. CCI Group has contributed to the research
  • 3D non-invasive inspection of the skin lesions by close-range and low-cost photogrammetric techniques
    3D non-invasive inspection of the skin lesions by close-range and low-cost photogrammetric techniques Orun, A.; Goodyer, E. N.; Smith, Geoff In dermatology, one of the most common causes of skin abnormality is an unusual change in skin lesion structure which may exhibit very subtle physical deformation of its 3D shape. However the geometrical sensitivity of current cost-effective inspection and measurement methods may not be sufficient to detect such small progressive changes in skin lesion structure at micro-scale. Our proposed method could provide a low-cost, non-invasive solution by a compact system solution to overcome these shortcomings by using close-range photogrammetric imaging techniques to build a 3D surface model for a continuous observation of subtle changes in skin lesions and other features. The main research group is CCI in collaboration with HLS (School of Pharmacy) Open Access article
  • Texture based characterization of sub-skin features by specified laser speckle effects at λ=650nm region
    Texture based characterization of sub-skin features by specified laser speckle effects at λ=650nm region Orun, A.; Seker, Huseyin; Uslan, Volkan; Goodyer, E. N.; Smith, Geoff Objective: The textural structure of “skin age” related sub-skin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. Methods: This is achieved by a two stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ=650 nm spectral band region. In the second stage a Bayesian inference method is used to select attributes from which a predictive model is built. Results: This technique enables us to contrast different skin age models, such as the laser-speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle based technique yields better results. Conclusion: The method introduced here is non-invasive, low-cost and capable of operating in real-time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models
    A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models Orun, A.; Seker, Huseyin; Rose, John; Moemeni, A.; Fidan, M. With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models. Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.
  • An improvement of skin aging assessment by non-invasive laser speckle effect: A comparative texture analysis
    An improvement of skin aging assessment by non-invasive laser speckle effect: A comparative texture analysis Orun, A.; Seker, H.; Goodyer, E. N.; Smith, Geoff; Uslan, V. Skin aging is a complex biological process that is yet to be successfully modelled as it depends on various internal and external factors. This work therefore investigates novel low-cost skin aging assessment technique and equipment by using robust analysis of textural features unified with a laser-speckle imaging method, which is found to be quite capable of detecting multi-layer cellular textural changes exhibited by the biological skin aging process. This study and low-cost product seem to be the first of its kind, which is expected to bring great benefit to both healthcare and cosmetic sectors.
  • Noninvasive Imaging Method Promising for Skin Assessment
    Noninvasive Imaging Method Promising for Skin Assessment Orun, A. A laser-speckle imaging technique using low-power red lasers can assess skin aging better than traditional imaging methods, a team at De Montfort University has discovered. As a possible alternative to high-cost confocal microscopy, it could lead to better understanding of skin growth, damage and diseases, and could also lead to better and more personalized skin treatment.
  • Optimized parametric skin modelling for diagnosis of skin abnormalities by combining light back-scatter and laser speckle imaging
    Optimized parametric skin modelling for diagnosis of skin abnormalities by combining light back-scatter and laser speckle imaging Orun, A.; Goodyer, E. N.; Seker, H.; Smith, Geoff; Uslan, V.; Chauhan, D.

Click here to view a full listing of Ahmet Orun's publications and outputs

Key research outputs

2013-2014   Development of non-invasive diagnostic tools for early signs of diseases by  use of  comprehensive laser speckle imaging methods;  Full funded by Proof-of-principle Studies (HEIF)  Programm   (Advertised in Photonics USA News Magazine, http://www.photonics.com/Article.aspx?AID=56111 

Related Patent: TR9600736(A1)

2010-2012   Computational modelling and predicting  the system behaviours by Bayesian networks (Internal project);  The project aims to exploit inference techniques like bayesian networks to make a human behaviour analysis;  (Support of collaboration was given by University of Birmingham- School of Psychology.

Research interests/expertise

  • Computer/console Game based user identity analysis 
  • Bayesian network applications (biometrics, cognitive psychology, bio-health, genetics 
  • Laser baser optical skin analysis
  • Photogrammetric vision systems & Industrial inspection 
  • Remote Sensing & Satellite data analysis
  • Quantum physics and its applications in pharmaceuticals.
  • Quantum optics, Quantum computing

Areas of teaching

  • Computer vision (GYTE Institute, 1999)
  • Bioinformatics 
  • Image Processing
  • Matlab basics  
  • Fuzzy logic
  • Image analysis of  Pharmaceuticals

Qualifications

PhD in Computer Vision, MPhil (Satelllite Data Analysis)

Courses taught

  • CTEC0704 - Computing Skills & Research (Lab)
  • IMAT3406  - Fuzzy Logic (lab)
  • IMAT3451- Final year project supervisions (computing, Game projects)
  • IMAT2902 Computer Graphics 
  • PHAR5350 - Bioinformatics 

Honours and awards

TUBITAK-MRC (Military Command & Control Information System) Best Project Annual Award 

Membership of professional associations and societies

  • Photogrammetric Society, London (1988-1994) 
  • Oxford Brookes Alumni Association (1993-1994)
  • IEEE Computer Society (2000-2005)
  • Vision Group (Birmingham University, School of Computer Science) 
  • Inclusion into  "Marquis Who's Who in the World" (2005 - 2015).
  • Inclusion into  "Marquis Who's Who in Science and Engineering (2006 - 2007).
  • Position in  NATO  Electronic Warfare Study Group  (1998-1999)

Professional licences and certificates

  • Laser Health & safety 
  • NATO , EW Study Group Clearance

Projects

2013-2014  Proj. Developer - Development of non-invasive diagnostic tools for early signs of diseases by use of comprehensive laser speckle imaging methods. Full funded  by Proof-of-principle Studies (HEIF) Programme

2013  Researcher -Skin analysis and modelling for early detection of skin cancer melanoma by use of multi-spectral images and light interaction analysis. (De Montfort University, RIF)

2010-2012   Computational modelling and predicting the system behaviours by Bayesian networks (Internal project)  

2001- 2003  Researcher- University of Birmingham, School of Computer Sc. Experimental Research on skin cancer Melanoma by computer modelling, Image analysis.

2000-2001  Researcher - University of Reading, Department of Computer Science, Real-time vehicle identification system development software/hardware management, software development, collaboration with Reading Borough Council & CRS Ltd.

1991- 2000  Project Manager - TUBITAK, Marmara Research Centre Information Technologies Research Institute, Intelligent Systems Unit, Gebze – Kocaeli, Turkey, Subject : Command and Control Systems, research & development of  computer vision systems

Recent research outputs

  • Seker, H., V. Uslan, A.B. Orun, E. Goodyer and G. Smith, “Prediction of skin ages by means of multi-spectral light sources”, 36th Annual International IEEE EMBS Conference., Chicago 26-30 August, 2014 , USA.
  • Ustebay, S., A.B. Orun, H. Seker and A.Sertbas.Investigating the amigdala-thread effects on cognitive learning via graphical domains and Bayesian networks.  AIHLS-2014 Conference, Kusadasi. Turkey,  October 19-22, 2014
  • Orun, A.B., Intelligent Laser Speckle Classification, Wikipedia, http://en.wikipedia.org/wiki/Intelligent_laser_speckle_classification

Consultancy work

  • Project Assessment Referee for  EPSRC  (Engineering and Physical Science Research Council,UK), February 2007
  • Consultancy for  PCI image analysis Software package (SiliconGraphics Representative of Turkey) Informatic Co

Current research students

-Nithindharan Ravichandran MSc in Cryptography Algorithms

-Peter Irigbhogbe MSc in Human-Remote Cognitive Data collection for intruder  behaviour analysis 

-Stephen Paul - MSc in  Dynamic web content for cognitive human-machine interaction 

-Cedric Carteron - MSc. in Computer Vision by mobile devices

-Luke Vella Critien - MSc. in Artificial Intelligence & Data Mining

Externally funded research grants information

2013-2014  (Proof-of-principle Studies (HEIF)  Development  of  non-invasive  diagnostic tools  for early signs  of  diseases  by use of  comprehensive  laser speckle  imaging methods,   Full funded (£25K) 

Published patents

1.  Object type determination by the laser interactive photogrammetrical                                  technique.  (Pat. No: TR9600736(A1))

2.  Camera resolution enhancement in artificial way for the stationary scenes                        (Pat. No: TR 1996-1038B)

3.   Laser-optical smoke and gas detector (Pat.No : TR9900544 )

Professional esteem indicators

  • I am the "Orun & Natarajan Satellite Sensor Model" developer. Nowadays most commercial products use a sensor model are based on this work.  Publication : “A Modified bundle adjustment software for SPOT imagery and photography : A tradeoff”, Photogrammetric Engineering & Remote Sensing, December 1994. (References : KOMPSAT-1, SPOT-DEM, SPOT-pseudo, Stereo,Sensor model, ) (The sensor model .developed has been used for the satellites; SPOT, KOMPSAT-1, IKONOS) (the paper has over 60 citations)
  • Inventor of  3 patents
  • Inclusion into  "Marquis Who's Who in the World" (2005 - 2015).
  • Inclusion into  "Marquis Who's Who in Science and Engineering (2006 - 2007).
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