| 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. |
- Akser, M., et al.
(författare)
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SceneMaker : Creative technology for digital storytelling
- 2018
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Ingår i: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.. - Cham : Springer Verlag. - 9783319558332 ; , s. 29-38
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Konferensbidrag (refereegranskat)abstract
- 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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| 3. |
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| 4. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- Bouhennache, Rafik, et al.
(författare)
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A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery
- 2019
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Ingår i: Geocarto International. - : Taylor & Francis Group. - 1010-6049 .- 1752-0762. ; 34:14, s. 1531-1551
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 8. |
- Brik, Bouziane, et al.
(författare)
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DDGP : Distributed Data Gathering Protocol for vehicular networks
- 2016
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Ingår i: Vehicular Communications. - : Elsevier. - 2214-2096. ; 4, s. 15-29
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
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| 10. |
- 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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