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  • 1.
    Akser, M.
    et al.
    Ulster University, GBR.
    Bridges, B.
    Ulster University, GBR.
    Campo, G.
    Ulster University, GBR.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Curran, K.
    Ulster University, GBR.
    Fitzpatrick, L.
    Ulster University, GBR.
    Hamilton, L.
    Ulster University, GBR.
    Harding, J.
    Ulster University, GBR.
    Leath, T.
    Ulster University, GBR.
    Lunney, T.
    Ulster University, GBR.
    Lyons, F.
    Ulster University, GBR.
    Ma, M.
    University of Huddersfield, GBR.
    Macrae, J.
    Ulster University, GBR.
    Maguire, T.
    Ulster University, GBR.
    McCaughey, A.
    Ulster University, GBR.
    McClory, E.
    Ulster University, GBR.
    McCollum, V.
    Ulster University, GBR.
    Mc Kevitt, P.
    Ulster University, GBR.
    Melvin, A.
    Ulster University, GBR.
    Moore, P.
    Ulster University, GBR.
    Mulholland, E.
    Ulster University, GBR.
    Muñoz, K.
    BijouTech, CoLab, Letterkenny, Co., IRL.
    O’Hanlon, G.
    Ulster University, GBR.
    Roman, L.
    Ulster University, GBR.
    SceneMaker: Creative technology for digital storytelling2018In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng. / [ed] Brooks A.L.,Brooks E., Springer Verlag , 2018, Vol. 196, p. 29-38Conference paper (Refereed)
    Abstract [en]

    The School of Creative Arts & Technologies at Ulster University (Magee) has brought together the subject of computing with creative technologies, cinematic arts (film), drama, dance, music and design in terms of research and education. We propose here the development of a flagship computer software platform, SceneMaker, acting as a digital laboratory workbench for integrating and experimenting with the computer processing of new theories and methods in these multidisciplinary fields. We discuss the architecture of SceneMaker and relevant technologies for processing within its component modules. SceneMaker will enable the automated production of multimodal animated scenes from film and drama scripts or screenplays. SceneMaker will highlight affective or emotional content in digital storytelling with particular focus on character body posture, facial expressions, speech, non-speech audio, scene composition, timing, lighting, music and cinematography. Applications of SceneMaker include automated simulation of productions and education and training of actors, screenwriters and directors. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

  • 2.
    Andres, Bustamante
    et al.
    Tecnológico de Monterrey, MEX.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Rodriguez-Garcia, Alejandro
    Tecnológico de Monterrey, MEX.
    Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model2019Conference paper (Refereed)
  • 3.
    Bouhennache, Rafik
    et al.
    Science and technology institute, university center of Mila, DZA.
    Bouden, Toufik
    ohammed Seddik Ben Yahia University of Jijel, DZA.
    Taleb-Ahmed, Abdmalik
    university of V alenciennes, FRA.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Institute of Technology.
    A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery2018In: Geocarto International, ISSN 1010-6049, E-ISSN 1752-0762Article in journal (Refereed)
    Abstract [en]

    Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as Built-up Land Features Extraction Index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the Spectral Discrimination Index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.

  • 4. Brik, Bouziane
    et al.
    Lagraa, Nasreddine
    Abderrahmane, Lakas
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    DDGP: Distributed Data Gathering Protocol for vehicular networks2016In: Vehicular Communications, ISSN 2214-2096, Vol. 4, p. 15-29Article in journal (Refereed)
    Abstract [en]

    Vehicular Ad-Hoc Network (VANet) is an emerging research area, it offers a wide range of applications including safety, road traffic efficiency, and infotainment applications. Recently researchers are studying the possibility of making use of deployed VANet applications for data collection. In this case, vehicles are considered as mobile collectors that gather both real time and delay tolerant data and deliver them to interested entities. In this paper, we propose a novel Distributed Data Gathering Protocol (DDGP) for the collection of delay tolerant as well as real time data in both urban and highway environments. The main contribution of DDGP is a new medium access technique that enables vehicles to access the channel in a distributed way based on their location information. In addition, DDGP implements a new aggregation scheme, which deletes redundant, expired, and undesired data. We provide an analytical proof of correctness of DDGP, in addition to the performance evaluation through an extensive set of simulation experiments. Our results indicate that DDGP enhances the efficiency and the reliability of the data collection process by outperforming existing schemes in terms of several criteria such as delay and message overhead, aggregation ratio, and data retransmission rate. (C) 2016 Elsevier Inc. All rights reserved.

  • 5. Brik, Bouziane
    et al.
    Lagraa, Nasreddine
    Lakas, Abderrahmane
    Cherroun, Hadda
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    ECDGP: extended cluster-based data gathering protocol for vehicular networks2015In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677Article in journal (Refereed)
  • 6.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Structure Preserving Binary Image Morphing using Delaunay Triangulation2017In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 85, p. 8-14Article in journal (Refereed)
    Abstract [en]

    Mathematical morphology has been of a great significance to several scientific fields. Dilation, as one of the fundamental operations, has been very much reliant on the common methods based on the set theory and on using specific shaped structuring elements to morph binary blobs. We hypothesised that by performing morphological dilation while exploiting geometry relationship between dot patterns, one can gain some advantages. The Delaunay triangulation was our choice to examine the feasibility of such hypothesis due to its favourable geometric properties. We compared our proposed algorithm to existing methods and it becomes apparent that Delaunay based dilation has the potential to emerge as a powerful tool in preserving objects structure and elucidating the influence of noise. Additionally, defining a structuring element is no longer needed in the proposed method and the dilation is adaptive to the topology of the dot patterns. We assessed the property of object structure preservation by using common measurement metrics. We also demonstrated such property through handwritten digit classification using HOG descriptors extracted from dilated images of different approaches and trained using Support Vector Machines. The confusion matrix shows that our algorithm has the best accuracy estimate in 80% of the cases. In both experiments, our approach shows a consistent improved performance over other methods which advocates for the suitability of the proposed method.

  • 7.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Towards Query by Text Example for pattern spotting in historical documents2016In: Proceedings - CSIT 2016: 2016 7th International Conference on Computer Science and Information Technology, IEEE Computer Society, 2016, article id 7549479Conference paper (Refereed)
    Abstract [en]

    Historical documents are essentially formed of 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.

  • 8.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Kusetogullari, Hüseyin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Object recognition using shape growth pattern2017In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, ISPA, IEEE Computer Society Digital Library, 2017, p. 47-52, article id 8073567Conference paper (Refereed)
    Abstract [en]

    This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.

  • 9.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace EngineeringIn: Article in journal (Refereed)
  • 10.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case2019In: IFIP Advances in Information and Communication Technology, Springer-Verlag New York, 2019, Vol. 559Conference paper (Refereed)
    Abstract [en]

    In engineering, design analyses of complex products rely on 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 non-linear 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 hyperparameter tuning to improve the performance of the RF's model, to extract design parameters' importance and \textit{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.

  • 11.
    Devagiri, Vishnu Manasa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Splicing Forgery Detection and the Impact of Image Resolution2017In: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE - ECAI 2017, IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    With the development of the Internet, and the increase in the online storage space, there has been an explosion in the volume of videos and images circulating online. An important part of the digital forensics' tasks is to scrutinise part of these images to make important decisions. Digital tampering of images can impede reliability of these decisions. Through this paper we attempt to improve the detection rate of splicing forgery. We also examine how well the examined splicing forgery detection algorithm works on low-resolution images. In this paper, the aim is to enhance the accuracy of an existing algorithm. One tailed Wilcoxon signed rank test was utilised to compare the performance of the different algorithms.

  • 12. Kusetogullari, Hüseyin
    et al.
    Yavariabdi, Amir
    KTO Karatay University, TUR.
    Cheddad, Abbas
    Grahn, Håkan
    Johan, Hall
    Arkiv Digital, SWE.
    ARDIS: A Swedish Historical Handwritten Digit Dataset2019In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new image-based handwrittenhistorical digit dataset named ARDIS (Arkiv DigitalSweden). The images in ARDIS dataset are extractedfrom 15,000 Swedish church records which were writtenby different priests with various handwriting styles in thenineteenth and twentieth centuries. The constructed datasetconsists of three single digit datasets and one digit stringsdataset. The digit strings dataset includes 10,000 samplesin Red-Green-Blue (RGB) color space, whereas, the otherdatasets contain 7,600 single digit images in different colorspaces. An extensive analysis of machine learning methodson several digit datasets is examined. Additionally, correlationbetween ARDIS and existing digit datasets ModifiedNational Institute of Standards and Technology (MNIST)and United States Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms,including deep learning methods, provide low recognitionaccuracy as they face difficulties when trained on existingdatasets and tested on ARDIS dataset. Accordingly, ConvolutionalNeural Network (CNN) trained on MNIST andUSPS and tested on ARDIS provide the highest accuracies 58.80% and 35.44%, respectively. Consequently, the resultsreveal that machine learning methods trained on existingdatasets can have difficulties to recognize digits effectivelyon our dataset which proves that ARDIS dataset hasunique characteristics. This dataset is publicly available forthe research community to further advance handwritten digitrecognition algorithms.

  • 13.
    Ola, Spjuth
    et al.
    Karolinska Institutet, SWE.
    Andreas, Karlsson
    Karolinska Institutet, SWE.
    Mark, Clements
    Karolinska Institutet, SWE.
    Keith, Humphreys
    Karolinska Institutet, SWE.
    Emma, Ivansson
    Karolinska Institutet, SWE.
    Jim, Dowling
    Royal Institute of Technology, SWE.
    Martin, Eklund
    Karolinska Institutet, SWE.
    Alexandra, Jauhiainen
    AstraZeneca AB R&D, SWE.
    Kamila, Czene
    Karolinska Institutet, SWE.
    Henrik, Grönberg
    Karolinska Institutet, SWE.
    Pär, Sparén
    Karolinska Institutet, SWE.
    Fredrik, Wiklund
    Karolinska Institutet, SWE.
    Abbas, Cheddad
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    þorgerður, Pálsdóttir
    Nordic Information for Action e-Science Center, SWE.
    Mattias, Rantalainen
    Karolinska Institutet, SWE.
    Linda, Abrahamsson
    Karolinska Institutet, SWE.
    Erwin, Laure
    Royal Institute of Technology, SWE.
    Jan-Eric, Litton
    European Research Infrastructure Consortium, AUT.
    Juni, Palmgren
    Helsinki University, FIN.
    E-Science technologies in a workflow for personalized medicine using cancer screening as a case study2017In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 24, no 5, p. 950-957Article in journal (Refereed)
    Abstract [en]

    Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.

    Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.

    Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.

    Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

  • 14.
    Qian, Wu
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Segmentation-based Deep Learning Fundus Image Analysis2019Conference paper (Refereed)
1 - 14 of 14
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