| 1. |
- Cheddad, Zohra Adila, et al.
(författare)
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Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information
- 2024. - 822
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Ingår i: Intelligent Systems and Applications. - : Springer. - 9783031477201 ; , s. 1-16
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 2. |
- Andres, Bustamante, et al.
(författare)
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Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology : An Experimental Model
- 2021
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Ingår i: Photonics. - : MDPI. - 2304-6732. ; 8:4
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Tidskriftsartikel (refereegranskat)abstract
- 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. |
- Aouissi, Meftah, et al.
(författare)
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Crack growth optimization using eddy current testing and genetic algorithm for estimating the stress intensity factors
- 2024
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Ingår i: Acta Mechanica. - : Springer. - 0001-5970 .- 1619-6937. ; 235:6, s. 3643-3656
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Tidskriftsartikel (refereegranskat)abstract
- This study developed a procedure for rapidly reconstructing a crack profile for calculating the parameters of fracture mechanics such as stress intensity factor with energy release rate (J) and displacement opening crack tip using data from the eddy current sensor. The inverse problem focused on adopting genetic algorithms to solve the direct problem iteratively. The use of the differential probe allows a rapid and precise resolution of the direct problem. The incident field produced by the two coils is determined using the 3D finite element results and the variation of impedance in each coil due to the crack. For the inverse problem, the crack’s surface is considered regular shape in terms of dimensions, and the sensor’s impedance expresses the objective function in terms of the width and length of the crack. The evaluation of the shape function and mesh matrix is made dependent on the iterative process, which makes the reversal procedure computationally lightweight when using genetic algorithms. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
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| 4. |
- Benhamza, Hiba, et al.
(författare)
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Image forgery detection review
- 2021
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Ingår i: Proceedings - 2021 International Conference on Information Systems and Advanced Technologies, ICISAT 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665478243
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Konferensbidrag (refereegranskat)abstract
- 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. |
- Chaddad, Ahmad, et al.
(författare)
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Magnetic resonance imaging based radiomic models of prostate cancer : A narrative review
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:3, s. 1-22
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Forskningsöversikt (refereegranskat)abstract
- 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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| 6. |
- Cheddad, Abbas, et al.
(författare)
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Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement
- 2022
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Ingår i: ACM International Conference Proceeding Series. - : Association for Computing Machinery (ACM). - 9781450398015 ; , s. 64-69
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Konferensbidrag (refereegranskat)abstract
- 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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| 7. |
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| 8. |
- Cheddad, Abbas
(författare)
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Machine Learning in Healthcare : Breast Cancer and Diabetes Cases
- 2021
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030680060 ; , s. 125-135
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Cheddad, Abbas
(författare)
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On Box-Cox Transformation for Image Normality and Pattern Classification
- 2020
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 8, s. 154975-154983
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 10. |
- Cheddad, Abbas, et al.
(författare)
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SHIBR-The Swedish Historical Birth Records : a semi-annotated dataset
- 2021
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Ingår i: Neural Computing & Applications. - : Springer London. - 0941-0643 .- 1433-3058. ; 33:22, s. 15863-15875
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Tidskriftsartikel (refereegranskat)abstract
- 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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