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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.

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  • 2.
    Andres, Bustamante
    et al.
    Tecnológico de Monterrey, MEX.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Jimenez-Perez, Julio Cesar
    Tecnológico de Monterrey, MEX.
    Rodriguez-Garcia, Alejandro
    Tecnológico de Monterrey, MEX.
    Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model2021In: Photonics, ISSN 2304-6732, Vol. 8, no 4, article id 118Article in journal (Refereed)
    Abstract [en]

    Machine learning (ML) has an impressive capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied where three approaches of ML were explored. Once all images were analyzed, representative areas from every digital image were also extracted, processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning-support vector machine (TL-SVM) (AUC = 0.94, SPE 88%, SEN 100%) and transfer learning-random forest (TL- RF) method (AUC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUC = 0.84, SPE 77%, SEN 91%) and random forest (AUC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas using a small sample.

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  • 3.
    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 (Other academic)
  • 4.
    Benhamza, Hiba
    et al.
    Mohamed Khider University, DZA.
    Djeffal, Abdelhamid
    Mohamed Khider University, DZA.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Image forgery detection review2021In: Proceedings - 2021 International Conference on Information Systems and Advanced Technologies, ICISAT 2021, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper (Refereed)
    Abstract [en]

    With the wide spread of digital document use in administrations, fabrication and use of forged documents have become a serious problem. This paper presents a study and classification of the most important works on image and document forgery detection. The classification is based on documents type, forgery type, detection method, validation dataset, evaluation metrics and obtained results. Most of existing forgery detection works are dealing with images and few of them analyze administrative documents and go deeper to analyze their contents. © 2021 IEEE.

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  • 5.
    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 imagery2019In: Geocarto International, ISSN 1010-6049, E-ISSN 1752-0762, Vol. 34, no 14, p. 1531-1551Article 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%.

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  • 6. 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.

  • 7. 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)
  • 8.
    Chaddad, Ahmad
    et al.
    Guilin University of Electronic Technology, CHN.
    Kucharczyk, Michael
    Dalhousie University, CAN.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Clarke, Sharon E.
    Dalhousie University, CAN.
    Hassan, Lama
    Guilin University of Electronic Technology, CHN.
    Ding, Shuxue
    Guilin University of Electronic Technology, CHN.
    Rathore, Saima
    University of Pennsylvania, USA.
    Zhang, Mingli
    McGill University, CAN.
    Katib, Yousef
    Taibah University, SAU.
    Bahoric, Boris
    McGill University, CAN.
    Abikhzer, Gad Solomon
    McGill University, CAN.
    Probst, Stephan Michael
    McGill University, CAN.
    Niazi, Tamim Mohammad
    McGill University, CAN.
    Magnetic resonance imaging based radiomic models of prostate cancer: A narrative review2021In: Cancers, ISSN 2072-6694, Vol. 13, no 3, p. 1-22, article id 552Article, review/survey (Refereed)
    Abstract [en]

    The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis‐a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi‐institutional collaboration in producing prospectively populated and expertly labeled imaging libraries. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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  • 9.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Machine Learning in Healthcare: Breast Cancer and Diabetes Cases2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12585, p. 125-135Conference paper (Refereed)
    Abstract [en]

    This paper provides insights into a workflow of different applications of machine learning coupled with image analysis in the healthcare sector which we have undertaken. As case studies, we use personalized breast cancer screenings and diabetes research (i.e., Beta-cell mass quantification in mice and diabetic retinopathy analysis). Our tools play a pivotal role in evidence-based process for personalized medicine and/or in monitoring the progression of diabetes as a chronic disease to help for better understanding of its development and the way to combat it. Although this multidisciplinary collaboration provides only succinct description of these research nodes, relevant references are furnished for further details. © 2021, Springer Nature Switzerland AG.

  • 10.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    On Box-Cox Transformation for Image Normality and Pattern Classification2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 154975-154983, article id 9174711Article in journal (Refereed)
    Abstract [en]

    A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda (λ) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assumption underlies a variety of statistical test models. This technique, however, is best known in statistical analysis to handle one-dimensional data. Herein, this paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data, namely, digital images and to study its effect. Moreover, to reduce time complexity, it suffices to estimate the parameter lambda in real-time for large two-dimensional matrices by merely considering their probability density function as a statistical inference of the underlying data distribution. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images and for ameliorating the performance of a colour pattern classification algorithm as an example application. Results with and without the proposed approach, are compared using the AlexNet (transfer deep learning) pretrained model. To the best of our knowledge, this is the first time that the Box-Cox transformation is extended to digital images by exploiting histogram transformation.

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  • 11.
    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.

  • 12.
    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.

  • 13.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Bennour, Akram
    Larbi Tebessi University, DZA.
    Kessentini, Yousri
    Digital Research Center of Sfax, TUN.
    Introduction to the special section on intelligent systems and pattern recognition (SS:ISPR20)2022In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 156, p. 190-191Article in journal (Other academic)
  • 14.
    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.

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  • 15.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Kusetogullari, Hüseyin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hilmkil, Agrin
    Peltarion AB,SWE.
    Sundin, Lena
    Independent Researcher, SWE.
    Yavariabdi, Amir
    KTO Karatay Univ, TUR.
    Aouache, Mustapha
    Dev Technol Avancees CDTA, Div Telecom, DZA.
    Hall, Johan
    Arkiv Digital AD AB, SWE.
    SHIBR-The Swedish Historical Birth Records: a semi-annotated dataset2021In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 33, no 22, p. 15863-15875Article in journal (Refereed)
    Abstract [en]

    This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child's first name, birth date, date of baptism, father's first/last name, mother's first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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  • 16.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement2022In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2022, p. 64-69Conference paper (Refereed)
    Abstract [en]

    Amidst the outbreak of the coronavirus (COVID-19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students' engagement and active participation become an issue; add to that the new emerging effects associated with this setup, such as the so-called "Zoom fatigue", a term coined recently by some authors referring to one's exhaustion feeling that stems from the overuse of virtual meetings. In realising this problem, solutions were suggested in the literature to help trigger students' engagement and enhance teachers' experience in online teaching. This study analyses these effects along with our teachers' experience in the new learning environment and concludes by devising some recommendations. To attain the above objectives, we conducted online interviews with six of our teachers, transcribed the content of the videos and then applied the inductive research approach to assess the results. © 2022 Owner/Author.

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  • 17.
    Cheddad, Zohra Adila
    et al.
    Université Frères Mentouri I, Algeria.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information2024In: Intelligent Systems and Applications: Proceedings of the 2023 Intelligent Systems Conference (IntelliSys) Volume 1 / [ed] Kohei Arai, Springer, 2024, 822, Vol. 822, p. 1-16Conference paper (Refereed)
    Abstract [en]

    Digital audio signal reconstruction of a lost or corrupt segment using deep learning algorithms has been explored intensively in recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on reconstructing audio signals with the fusion of dithering, steganography, and machine learning regressors. Therefore, this paper proposes the combination of steganography, halftoning (dithering), and state-of-the-art shallow and deep learning methods. The results (including comparing the SPAIN, Autoregressive, deep learning-based, graph-based, and other methods) are evaluated with three different metrics. The observations from the results show that the proposed solution is effective and can enhance the reconstruction of audio signals performed by the side information (e.g., Latent representation) steganography provides. Moreover, this paper proposes a novel framework for reconstruction from heavily compressed embedded audio data using halftoning (i.e., dithering) and machine learning, which we termed the HCR (halftone-based compression and reconstruction). This work may trigger interest in optimising this approach and/or transferring it to different domains (i.e., image reconstruction). Compared to existing methods, we show improvement in the inpainting performance in terms of signal-to-noise ratio (SNR), the objective difference grade (ODG) and Hansen’s audio quality metric. In particular, our proposed framework outperformed the learning-based methods (D2WGAN and SG) and the traditional statistical algorithms (e.g., SPAIN, TDC, WCP).

  • 18.
    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 Engineering2020In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 61, no 5, p. 2177-2192Article in journal (Refereed)
    Abstract [en]

    The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

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  • 19.
    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.

  • 20.
    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.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Palmquist, Jonatan
    GKN Aerospace Engine Systems.
    Active Learning to Support In-situ Process Monitoring in Additive Manufacturing2021In: Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 / [ed] Wani M.A., Sethi I.K., Shi W., Qu G., Raicu D.S., Jin R., IEEE, 2021, p. 1168-1173Conference paper (Refereed)
    Abstract [en]

    This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extract deep features from images, and clustering and query by committee sampling to select the representative samples to build defect classification models. We conduct quantitative experiments to evaluate the proposed method's performance and compare it with other selected state-of-the-art AL approaches using a dataset of additive manufacturing (AM) and a publicly available dataset. The experimental results show that the proposed approach outperforms AL with committee based sampling, and AL with clustering and random sampling. The results of the statistical significance test show that there is a significant difference between the studied AL approaches. Hence, the proposed AL approach can be considered an alternative method to reduce labelling costs when building defects classification models, whose generalizability is most likely plausible.

  • 21.
    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.
    Palmquist, Jonatan
    Gkn Aerospace Engine Systems Sweden, Process Engineering Department, SWE.
    Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case2020In: 2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 249-254, article id 9311555Conference paper (Refereed)
    Abstract [en]

    One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for defects classification to speed up the manual verification criteria since a huge data is generated from the additive manufacturing. Although a huge image data is available, the data needs to be labelled manually by experts which results in small sample datasets. Hence, to model small sample sizes and also to acquire the importance of parameters, we opted a traditional machine learning method, Random Forests (RF). For feature extraction, we opted for the Polar Transformation to explore its applicability using the melt-pool image dataset and a publicly available shape image dataset. The results show that RF models with Polar Transformation performed the best on our case study datasets and the second-best for the public dataset when compared to the Histogram of Oriented Gradients, HARALICK, XY-projections of an image, and Local Binary Patterns methods. As such, the Polar Transformation can be considered as a suitable compact shape descriptor. © 2020 IEEE.

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  • 22.
    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.
    Palmquist, Jonatan
    GKN Aerospace Engine Systems.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Clustering-based Adaptive Data Augmentation for Class-imbalance in Machine Learning (CADA): Additive Manufacturing Use-case2022In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed)
    Abstract [en]

    Large amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components.  For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods.  As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class. 

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  • 23.
    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.

  • 24.
    Goswami, Prashant
    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.
    Junede, Fredrik
    Blekinge Institute of Technology. student.
    Asp, Samuel
    Blekinge Institute of Technology. student.
    Interactive landscape–scale cloud animation using DCGAN2023In: Frontiers in Computer Science, E-ISSN 2624-9898, Vol. 5, article id 957920Article in journal (Refereed)
    Abstract [en]

    This article presents an interactive method for 3D cloud animation at the landscape scale by employing machine learning. To this end, we utilize deep convolutional generative adversarial network (DCGAN) on GPU for training on home-captured cloud videos and producing coherent animation frames. We limit the size of input images provided to DCGAN, thereby reducing the training time and yet producing detailed 3D animation frames. This is made possible through our preprocessing of the source videos, wherein several corrections are applied to the extracted frames to provide an adequate input training data set to DCGAN. A significant advantage of the presented cloud animation is that it does not require any underlying physics simulation. We present detailed results of our approach and verify its effectiveness using human perceptual evaluation. Our results indicate that the proposed method is capable of convincingly realistic 3D cloud animation, as perceived by the participants, without introducing too much computational overhead.

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  • 25.
    Goswami, Prashant
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Johansson, Henrik
    Blekinge Institute of Technology. student.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Animated lightning bolt generation using machine learning2023In: 12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    Abstract [en]

    In this paper, we investigate the possibility of leveraging the predictive power of machine learning to generate animated lightning bolts in the image space efficiently. To this end, we selected state-of-the-art machine learning architectures based on Generative Adversarial Network (GAN) and trained them on the commonly available videos. We demonstrate that visually convincing animations are achievable even when employing a limited dataset. The visual realism of the generated sequences of lightning bolts is assessed by conducting a user study on the participants.

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  • 26.
    Hahn, Max
    et al.
    Umea University, SWE.
    Nord, Christoffer
    Umea University, SWE.
    van Krieken, Pim P.
    Karolinska Institute, SWE.
    Berggren, Per-Olof
    Karolinska Institute, SWE.
    Ilegems, Erwin
    Karolinska Institute, SWE.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ahlgren, Ulf
    Umea University, SWE.
    Quantitative 3D OPT and LSFM datasets of pancreata from mice with streptozotocin-induced diabetes2022In: Scientific Data, E-ISSN 2052-4463, Vol. 9, no 1, article id 558Article in journal (Refereed)
    Abstract [en]

    Mouse models for streptozotocin (STZ) induced diabetes probably represent the most widely used systems for preclinical diabetes research, owing to the compound's toxic effect on pancreatic beta-cells. However, a comprehensive view of pancreatic beta-cell mass distribution subject to STZ administration is lacking. Previous assessments have largely relied on the extrapolation of stereological sections, which provide limited 3D-spatial and quantitative information. This data descriptor presents multiple ex vivo tomographic optical image datasets of the full beta-cell mass distribution in mice subject to single high and multiple low doses of STZ administration, and in glycaemia recovered mice. The data further include information about structural features, such as individual islet beta-cell volumes, spatial coordinates, and shape as well as signal intensities for both insulin and GLUT2. Together, they provide the most comprehensive anatomical record of the effects of STZ administration on the islet of Langerhans in mice. As such, this data descriptor may serve as reference material to facilitate the planning, use and (re)interpretation of this widely used disease model.

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  • 27.
    Idrisoglu, Alper
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Jakobsson, Andreas
    Lunds universitet.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    COPDVD: Automated Classification of Chronic Obstructive Pulmonary Disease on a New Developed and Evaluated Voice DatasetManuscript (preprint) (Other academic)
    Abstract [en]

    AbstractBackground: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical effects, e.g., heart failure or voice distortion. However, the systematic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systematic effects could be helpful to detect the condition in its early stages.

    Objective: The proposed study aims to: (i) investigate whether the voice features extracted from the vowel "A" phonation carry information that can be predictive of COPD by employing Machine Learning (ML); and (ii) develop a voice dataset based on the evaluation of the features.

    Methods: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "A" phonation commenced following an information and consent meeting with each participant using the VoiceDiagnistic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations  (SHAP) feature importance measures. 

    Results: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77%, 69%, and 78% on the test set and 93%, 78% and 97% on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82% and AP of 76%. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. 

    Conclusion: This study concludes that the vowel "A" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. Lastly, we believe that the newly developed voice dataset will be a valuable resource to researchers within the domain.

  • 28.
    Kusetogullari, Hüseyin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Yavariabdi, Amir
    KTO Karatay University, TUR.
    Cheddad, Abbas
    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.
    Johan, Hall
    Arkiv Digital, SWE.
    ARDIS: A Swedish Historical Handwritten Digit Dataset2020In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 32, no 21, p. 16505-16518, article id Special issueArticle 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.

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  • 29.
    Lekamlage, Charitha Dissanayake
    et al.
    Blekinge Institute of Technology. student.
    Afzal, Fabia
    Blekinge Institute of Technology. student.
    Westerberg, Erik
    Blekinge Institute of Technology. student.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Mini-DDSM: Mammography-based Automatic Age Estimation2020In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2020, p. 1-6, article id 3441370Conference paper (Refereed)
    Abstract [en]

    Age estimation has attracted attention for its various medical applications. There are many studies on human age estimation from biomedical images. However, there is no research done on mammograms for age estimation, as far as we know. The purpose of this study is to devise an AI-based model for estimating age from mammogram images. Due to lack of public mammography data sets that have the age attribute, we resort to using a web crawler to download thumbnail mammographic images and their age fields from the public data set; the Digital Database for Screening Mammography. The original images in this data set unfortunately can only be retrieved by a software which is broken. Subsequently, we extracted deep learning features from the collected data set, by which we built a model using Random Forests regressor to estimate the age automatically. The performance assessment was measured using the mean absolute error values. The average error value out of 10 tests on random selection of samples was around 8 years. In this paper, we show the merits of this approach to fill up missing age values. We ran logistic and linear regression models on another independent data set to further validate the advantage of our proposed work. This paper also introduces the free-access Mini-DDSM data set. © 2020 ACM.

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  • 30.
    Liang, Xusheng
    et al.
    Blekinge Institute of Technology. student.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hall, Johan
    ArkivDigital AB, SWE.
    Comparative Study of Layout Analysis of Tabulated Historical Documents2021In: Big Data Research, ISSN 2214-5796, E-ISSN 2214-580X, Vol. 24, article id 100195Article in journal (Refereed)
    Abstract [en]

    Nowadays, the field of multimedia retrieval system has earned a lot of attention as it helps retrieve information more efficiently and accelerates daily tasks. Within this context, image processing techniques such as layout analysis and word recognition play an important role in transcribing content in printed or handwritten documents into digital data that can be further processed. This transcription procedure is called document digitization. This work stems from an industrial need, namely, a Swedish company (ArkivDigital AB) has scanned more than 80 million pages of Swedish historical documents from all over the country and there is a high demand to transcribe the contents into digital data. Such process starts by figuring out text location which, seen from another angle, is merely table layout analysis. In this study, the aim is to reveal the most effective solution to extract document layout w.r.t Swedish handwritten historical documents that are featured by their tabular forms. In short, outcome of public tools (i.e., Breuel's OCRopus method), traditional image processing techniques (e.g., Hessian/Gabor filters, Hough transform, Histograms of oriented gradients -HOG- features), machine learning techniques (e.g., support vector machines, transfer learning) are studied and compared. Results show that the existing OCR tool cannot carry layout analysis task on our Swedish historical handwritten documents. Traditional image processing techniques are mildly capable of extracting the general table layout in these documents, but the accuracy is enhanced by introducing machine learning techniques. The best performing approach will be used in our future document mining research to allow for the development of scalable resource-efficient systems for big data analytics. © 2021 Elsevier Inc.

  • 31.
    Maamouli, Khadidja
    et al.
    University of Biskra, Algeria.
    Benhamza, Hiba
    University of Biskra, Algeria.
    Djeffal, Abdelhamid
    University of Biskra, Algeria.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    A CNN based architecture for forgery detection in administrative documents2022In: 2022 International Symposium on iNnovative Informatics of Biskra, ISNIB 2022, Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 135-140Conference paper (Refereed)
    Abstract [en]

    The use of digital documents is knowing a widespread in different daily administrative and economic transactions. Simultaneously, the forgery of many documents becomes a crime that costs billions to states and companies. Several researchers tried to develop techniques that automatically detect forged documents using machine learning and image processing. With the immense success of deep learning applications, we employ, in this work, a convolutional neural network architecture that uses a gathered dataset of forged and authentic administrative documents. The results obtained on our dataset of 493 documents reached 73.95% accuracy and 97.3% recall, surpassing the efficiency of the machine learning base methods. © 2022 IEEE.

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  • 32.
    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.

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  • 33.
    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 Analysis2019In: 2019 IEEE 27TH INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2019), IEEE, 2019, p. 44-53, article id 8936078Conference paper (Refereed)
    Abstract [en]

    Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predictive power of the different compositions of retinal images using deep learning. This paper is to investigate whether there exists specific region that could assist in better prediction of the retinopathy disease, meaning to find the best region in fundus images that can boost the prediction power of models for retinopathy classification. To this end, with image segmentation techniques, the fundus image is divided into three different segments, namely, the optic disc, the blood vessels, and the other regions (regions other than blood vessels and optic disk). These regions are then contrasted against the performance of original fundus images. The convolutional neural network as well as transfer deep learning with the state-of-the-art pre-trained models (i.e., AlexNet, GoogleNet, Resnet50, VGG19) are deployed. We report the average of ten runs for each model. Different machine learning evaluation metrics are used. The other regions' segment reveals more predictive power than the original fundus image especially when using AlexNet/Resnet50.

  • 34.
    Sundstedt, Veronica
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Zepernick, Hans-Juergen
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Goswami, Prashant
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Tutschku, Kurt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Casalicchio, Emiliano
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Fiedler, Markus
    Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Abghari, Shahrooz
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hu, Yan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Garro, Valeria
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Chu, Thi My Chinh
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Arlos, Patrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    HINTS: Human-Centered Intelligent Realities2023In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 9-17Conference paper (Refereed)
    Abstract [en]

    During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

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  • 35.
    Wagenpfeil, Stefan
    et al.
    Univ Hagen, DEU.
    Mc Kevitt, Paul
    Acad Int Sci & Res AISR, GBR.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hemmje, Matthias
    Univ Hagen, DEU.
    Explainable Multimedia Feature Fusion for Medical Applications2022In: JOURNAL OF IMAGING, ISSN 2313-433X, Vol. 8, no 4, article id 104Article in journal (Refereed)
    Abstract [en]

    Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-ray, and multimedia, the management of a patient's data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, efficient indexing indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to readily understand medical multimedia formats.

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  • 36.
    Zhao, Mengqiao
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. student.
    Hochuli, Andre Gustavo
    Pontifical Catholic University of Parana (PPGIa/PUCPR), BRA.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    End-to-End Approach for Recognition of Historical Digit Strings2021In: Lecture Notes in Computer Science / [ed] Lladós J., Lopresti D., Uchida S., Springer Science and Business Media Deutschland GmbH , 2021, p. 595-609Conference paper (Refereed)
    Abstract [en]

    The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition. In the same vein, a recently published data set, known as ARDIS, presents handwritten digits manually cropped from 15.000 scanned documents of Swedish churches’ books that exhibit various handwriting styles. To this end, we propose an end-to-end segmentation- free deep learning approach to handle this challenging ancient handwriting style of dates present in the ARDIS dataset (4-digits long strings). We show that with slight modifications in the VGG-16 deep model, the framework can achieve a recognition rate of 93.2%, resulting in a feasible solution free of heuristic methods, segmentation, and fusion methods. Moreover, the proposed approach outperforms the well-known CRNN method (a model widely applied in handwriting recognition tasks). © 2021, Springer Nature Switzerland AG.

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