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    Tytuł:
    Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo
    Autorzy:
    Michael, Steve Olsen
    Juan, Sarah Samson
    Mit, Edwin
    Powiązania:
    https://bibliotekanauki.pl/articles/2058507.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    acoustic modeling
    automatic speech recognition
    convolutional neural network
    CNN
    under-resourced language
    NVIDIA NeMo
    Opis:
    For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models. There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). A CNN model is widely used as a method for improving image processing performance. In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language. Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data. Conversely, results and studies concerned with under-resourced languages remain unsatisfactory. Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper. Two experiments were conducted: the number of resources used in the works of our ASR’s training was manipulated, as was the internal parameter of the engine used, namely the epochs. The results of those experiments are then analyzed and compared with the results shown in existing papers.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 1; 43--53
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A few-shot fine-grained image recognition method
    Autorzy:
    Wang, Jianwei
    Chen, Deyun
    Powiązania:
    https://bibliotekanauki.pl/articles/2204540.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    few-shot learning
    attention metric
    CNN
    convolutional neural network
    feature expression
    wskaźnik uwagi
    sieć neuronowa splotowa
    cechy wyrażeń
    Opis:
    Deep learning methods benefit from data sets with comprehensive coverage (e.g., ImageNet, COCO, etc.), which can be regarded as a description of the distribution of real-world data. The models trained on these datasets are considered to be able to extract general features and migrate to a domain not seen in downstream. However, in the open scene, the labeled data of the target data set are often insufficient. The depth models trained under a small amount of sample data have poor generalization ability. The identification of new categories or categories with a very small amount of sample data is still a challenging task. This paper proposes a few-shot fine-grained image recognition method. Feature maps are extracted by a CNN module with an embedded attention network to emphasize the discriminative features. A channel-based feature expression is applied to the base class and novel class followed by an improved cosine similarity-based measurement method to get the similarity score to realize the classification. Experiments are performed on main few-shot benchmark datasets to verify the efficiency and generality of our model, such as Stanford Dogs, CUB-200, and so on. The experimental results show that our method has more advanced performance on fine-grained datasets.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144584
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey
    Autorzy:
    Abdellaoui, Meriem
    Benabdelkader, Souad
    Assas, Ouarda
    Powiązania:
    https://bibliotekanauki.pl/articles/31339413.pdf
    Data publikacji:
    2023
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
    Tematy:
    image restoration
    remote sensing images
    artificial intelligence
    AI
    machine learning
    ML
    deep learning
    DL
    convolutional neural network
    CNN
    Opis:
    Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.
    Źródło:
    Machine Graphics & Vision; 2023, 32, 2; 147-167
    1230-0535
    2720-250X
    Pojawia się w:
    Machine Graphics & Vision
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Towards a new deep learning algorithm based on GRU and CNN: NGRU
    Autorzy:
    Atassi, Abdelhamid
    el Azami, Ikram
    Powiązania:
    https://bibliotekanauki.pl/articles/2141895.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    Convolutional Neural Network
    CNN
    Gated Recurrent Unit
    GRU
    SemEval
    Twitter
    word2vec
    Keras
    TensorFlow
    Adadelta
    Adam
    soft-max
    deep learning
    Opis:
    This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will compare with the second system which uses Gated Recurrent Unit (GRU). And through this comparison we propose a new system based on the positive points of the two previous systems. Therefore, this new system will take the right choice of hyper-parameters recommended by the authors of both systems. At the final stage we propose a method to apply this new system to the dataset of different languages (used especially in socials networks).
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 45-47
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid approach of a deep learning technique for real-time ECG beat detection
    Autorzy:
    Patro, Kiran Kumar
    Prakash, Allam Jaya
    Samantray, Saunak
    Pławiak, Joanna
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/2172118.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    cardiac abnormalities
    CAD
    convolutional neural network
    CNN
    deep learning
    ECG
    electrocardiogram
    supra ventricular ectopic beats
    SVE
    nieprawidłowości kardiologiczne
    sieć neuronowa konwolucyjna
    uczenie głębokie
    EKG
    elektrokardiogram
    Opis:
    This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 455--465
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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