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  1. Article ; Online: Biorthogonal-Wavelet-Based Method for Numerical Solution of Volterra Integral Equations

    Mutaz Mohammad

    Entropy, Vol 21, Iss 11, p

    2019  Volume 1098

    Abstract: Framelets theory has been well studied in many applications in image processing, data recovery and computational analysis due to the key properties of framelets such as sparse representation and accuracy in coefficients recovery in the area of numerical ... ...

    Abstract Framelets theory has been well studied in many applications in image processing, data recovery and computational analysis due to the key properties of framelets such as sparse representation and accuracy in coefficients recovery in the area of numerical and computational theory. This work is devoted to shedding some light on the benefits of using such framelets in the area of numerical computations of integral equations. We introduce a new numerical method for solving Volterra integral equations. It is based on pseudo-spline quasi-affine tight framelet systems generated via the oblique extension principles. The resulting system is converted into matrix equations via these generators. We present examples of the generated pseudo-splines quasi-affine tight framelet systems. Some numerical results to validate the proposed method are presented to illustrate the efficiency and accuracy of the method.
    Keywords volterra integral equations ; multiresolution analysis ; oblique extension principle ; pseudo-splines ; biorthogonal wavelets ; quasi-affine systems ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
    Subject code 518
    Language English
    Publishing date 2019-11-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A New Technique for Solving Neutral Delay Differential Equations Based on Euler Wavelets

    Mutaz Mohammad / Alexander Trounev

    Complexity, Vol

    2022  Volume 2022

    Abstract: An effective numerical scheme based on Euler wavelets is proposed for numerically solving a class of neutral delay differential equations. The technique explores the numerical solution via Euler wavelet truncated series generated by a set of functions ... ...

    Abstract An effective numerical scheme based on Euler wavelets is proposed for numerically solving a class of neutral delay differential equations. The technique explores the numerical solution via Euler wavelet truncated series generated by a set of functions and matrix inversion of some collocation points. Based on the operational matrix, the neutral delay differential equations are reduced to a system of algebraic equations, which is solved through a numerical algorithm. The effectiveness and efficiency of the technique have been illustrated by several examples of neutral delay differential equations. The main advantages and key role of using the Euler wavelets in this work lie in the performance, accuracy, and computational cost of the proposed technique.
    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Implicit Riesz wavelets based-method for solving singular fractional integro-differential equations with applications to hematopoietic stem cell modeling.

    Mohammad, Mutaz / Trounev, Alexander

    Chaos, solitons, and fractals

    2020  Volume 138, Page(s) 109991

    Abstract: Riesz wavelets ... ...

    Abstract Riesz wavelets in
    Keywords covid19
    Language English
    Publishing date 2020-06-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.109991
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  4. Article ; Online: A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals

    ElMoaqet Hisham / Eid Mohammad / Ryalat Mutaz / Penzel Thomas

    Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 642-

    2023  Volume 645

    Abstract: Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require ... ...

    Abstract Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications.
    Keywords eeg signal ; cnn ; bilstm ; deep learning ; Medicine ; R
    Subject code 006
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: On the dynamical modeling of COVID-19 involving Atangana-Baleanu fractional derivative and based on Daubechies framelet simulations.

    Mohammad, Mutaz / Trounev, Alexander

    Chaos, solitons, and fractals

    2020  Volume 140, Page(s) 110171

    Abstract: In this paper, we present a novel fractional order COVID-19 mathematical model by involving fractional order with specific parameters. The new fractional model is based on the well-known Atangana-Baleanu fractional derivative with non-singular kernel. ... ...

    Abstract In this paper, we present a novel fractional order COVID-19 mathematical model by involving fractional order with specific parameters. The new fractional model is based on the well-known Atangana-Baleanu fractional derivative with non-singular kernel. The proposed system is developed using eight fractional-order nonlinear differential equations. The Daubechies framelet system of the model is used to simulate the nonlinear differential equations presented in this paper. The framelet system is generated based on the quasi-affine setting. In order to validate the numerical scheme, we provide numerical simulations of all variables given in the model.
    Keywords covid19
    Language English
    Publishing date 2020-07-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110171
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  6. Article: A Huge Hemangioma of the Urinary Bladder: A Case Report and Literature Review.

    Wael, Muhannad / Abuarafeh, Wael / Ghneimat, Mohammad N / Al Hammouri, Murad / Abuarafeh, Mutaz W / Nabali, Ahmad M

    Cureus

    2024  Volume 16, Issue 1, Page(s) e52852

    Abstract: Cavernous hemangioma of the bladder is a benign and very rare vascular tumor. It can be isolated or part of a syndrome. Neither clinical symptoms nor imaging modalities lead to a definitive diagnosis as there are no specific findings on imaging studies ... ...

    Abstract Cavernous hemangioma of the bladder is a benign and very rare vascular tumor. It can be isolated or part of a syndrome. Neither clinical symptoms nor imaging modalities lead to a definitive diagnosis as there are no specific findings on imaging studies or specific symptoms. Painless gross hematuria is the most common chief complaint and presentation and should never be underestimated. Here, we report a case of a large hemangioma of the urinary bladder in a young man who presented with recurrent recent episodes of painless gross hematuria and, surprisingly, with a previous episode of painless hematuria 15 years ago, which was treated successfully with partial cystectomy. We discuss the clinical features, evaluation, diagnosis, biopsy, management, the challenges encountered in treatment, and assert the value of tissue diagnosis and follow-up pattern and period. Due to the rarity of the tumor and lack of trials and evidence-based guidelines for management, treatment modalities vary and the risk for recurrence is questionable and not known.
    Language English
    Publishing date 2024-01-24
    Publishing country United States
    Document type Case Reports
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.52852
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  7. Article ; Online: On the dynamical modeling of COVID-19 involving Atangana–Baleanu fractional derivative and based on Daubechies framelet simulations

    Mohammad, Mutaz / Trounev, Alexander

    Chaos, Solitons & Fractals

    2020  Volume 140, Page(s) 110171

    Keywords General Mathematics ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110171
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: An End-to-End Deep Leaning Approach for Sleep-Wake Classification Using Single Channel EEG Signals

    El-Moaqet Hisham / Eid Mohammad / Ryalat Mutaz / Penzel Thomas

    Current Directions in Biomedical Engineering, Vol 8, Iss 2, Pp 801-

    2022  Volume 804

    Abstract: Sleep quality has a significant impact on human physical and mental health. The detection of sleep-wake states is thus of significant importance in the study of sleep. The performance of classical machine learning models for automated sleep detection ... ...

    Abstract Sleep quality has a significant impact on human physical and mental health. The detection of sleep-wake states is thus of significant importance in the study of sleep. The performance of classical machine learning models for automated sleep detection depends on the signals considered and feature extraction methods. Moreover, hand-crafted features are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome this limitation, this paper develops an end-to-end deep learning approach for automated feature extraction and detection of sleep-wake states using single channel raw EEG signals. Moreover, we leverage transfer learning to train and fine tune the proposed model to avoid the complexities associated with building a deep learning model from scratch. Using polysomnography (PSG) data of 20 patients, our results demonstrate the effectiveness of the proposed deep learning pipeline, achieving an excellent test performance in detecting sleep events with an overall sensitivity and precision of 92.7% and 92.1% respectively. The results demonstrate that the proposed approach can achieve superior performance compared to state-of-the-art studies on Sleep- Wake classification. Furthermore, it can attain reliable results as an alternative to classical methods that heavily rely on expert defined features.
    Keywords eeg signal ; sleep-wake classification ; deep learning ; transfer learning ; Medicine ; R
    Subject code 006
    Language English
    Publishing date 2022-09-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: The dynamics of COVID-19 in the UAE based on fractional derivative modeling using Riesz wavelets simulation.

    Mohammad, Mutaz / Trounev, Alexander / Cattani, Carlo

    Advances in difference equations

    2021  Volume 2021, Issue 1, Page(s) 115

    Abstract: The well-known novel virus (COVID-19) is a new strain of coronavirus family, declared by the World Health Organization (WHO) as a dangerous epidemic. More than 3.5 million positive cases and 250 thousand deaths (up to May 5, 2020) caused by COVID-19 and ... ...

    Abstract The well-known novel virus (COVID-19) is a new strain of coronavirus family, declared by the World Health Organization (WHO) as a dangerous epidemic. More than 3.5 million positive cases and 250 thousand deaths (up to May 5, 2020) caused by COVID-19 and has affected more than 280 countries over the world. Therefore studying the prediction of this virus spreading in further attracts a major public attention. In the Arab Emirates (UAE), up to the same date, there are 14,730 positive cases and 137 deaths according to national authorities. In this work, we study a dynamical model based on the fractional derivatives of nonlinear equations that describe the outbreak of COVID-19 according to the available infection data announced and approved by the national committee in the press. We simulate the available total cases reported based on Riesz wavelets generated by some refinable functions, namely the smoothed pseudosplines of types I and II with high vanishing moments. Based on these data, we also consider the formulation of the pandemic model using the Caputo fractional derivative. Then we numerically solve the nonlinear system that describes the dynamics of COVID-19 with given resources based on the collocation Riesz wavelet system constructed. We present graphical illustrations of the numerical solutions with parameters of the model handled under different situations. We anticipate that these results will contribute to the ongoing research to reduce the spreading of the virus and infection cases.
    Language English
    Publishing date 2021-02-19
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2132815-8
    ISSN 1687-1847 ; 1687-1839
    ISSN (online) 1687-1847
    ISSN 1687-1839
    DOI 10.1186/s13662-021-03262-7
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  10. Article ; Online: A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.

    ElMoaqet, Hisham / Eid, Mohammad / Ryalat, Mutaz / Penzel, Thomas

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 22

    Abstract: The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the ... ...

    Abstract The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time-Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses.
    MeSH term(s) Humans ; Sleep Stages ; Electroencephalography/methods ; Polysomnography/methods ; Sleep ; Machine Learning
    Language English
    Publishing date 2022-11-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22228826
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