Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "konwolucyjna sieć neuronowa" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-7 z 7
    Tytuł:
    Multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on visual cues
    Autorzy:
    Jadhav, Nagesh
    Sugandhi, Rekha
    Powiązania:
    https://bibliotekanauki.pl/articles/2086876.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    convolution neural network
    emotion recognition
    transfer learning
    late fusion
    uczenie głębokie
    konwolucyjna sieć neuronowa
    rozpoznawanie emocji
    Opis:
    In the domain of affective computing different emotional expressions play an important role. To convey the emotional state of human emotions, facial expressions or visual cues are used as an important and primary cue. The facial expressions convey humans affective state more convincingly than any other cues. With the advancement in the deep learning techniques, the convolutional neural network (CNN) can be used to automatically extract the features from the visual cues; however variable sized and biased datasets are a vital challenge to be dealt with as far as implementation of deep models is concerned. Also, the dataset used for training the model plays a significant role in the retrieved results. In this paper, we have proposed a multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on the visual cues. We have used a CNN and pre-trained ResNet-50 model for the transfer learning. VGGFace model’s weights are used to initialize weights of ResNet50 for fine-tuning the model. The proposed system shows significant improvement in test accuracy in affective state recognition compared to the singleton CNN model developed from scratch or transfer learned model. The proposed methodology is validated on The Karolinska Directed Emotional Faces (KDEF) dataset with 77.85% accuracy. The obtained results are promising compared to the existing state of the art methods.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138819, 1--11
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Vehicle classification using the convolution neural network approach
    Autorzy:
    Trivedi, Janak
    Devi, Mandalapu Sarada
    Dhara, Dave
    Powiązania:
    https://bibliotekanauki.pl/articles/2091225.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    convolution neural network
    vehicle classification
    deep learning
    intelligent transportation system
    konwolucyjna sieć neuronowa
    klasyfikacja pojazdów
    uczenie głębokie
    inteligentny system transportowy
    Opis:
    We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.
    Źródło:
    Zeszyty Naukowe. Transport / Politechnika Śląska; 2021, 112; Bibliogr. 13 poz.
    0209-3324
    2450-1549
    Pojawia się w:
    Zeszyty Naukowe. Transport / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns
    Autorzy:
    Bernardo, Lucas Salvador
    Damaševičius, Robertas
    de Albuquerque, Victor Hugo C.
    Maskeliūnas, Rytis
    Powiązania:
    https://bibliotekanauki.pl/articles/2055162.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Parkinson’s disease
    spirography
    convolutional neural network
    deep learning
    choroba Parkinsona
    spirografia
    sieć neuronowa konwolucyjna
    uczenie głębokie
    Opis:
    Parkinson’s disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is performed to help to obtain the disease’s diagnosis. The spiral trajectory differs between people with PD and healthy ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM) as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes’ spiral images derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest (RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 549--561
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using neural networks and deep learning algorithms in electrical impedance tomography
    Zastosowanie sieci neuronowych i algorytmów głębokiego uczenia w elektrycznej tomografii impedancyjnej
    Autorzy:
    Kłosowski, G.
    Rymarczyk, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/408307.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    imaging tomography
    multilayer perceptron
    deep learning
    convolutional neural networks
    tomografia obrazowa
    perceptron wielowarstwowy
    uczenie głębokie
    sieć neuronowa konwolucyjna
    Opis:
    This paper refers to the cases of the use of Artificial Neural Networks and Convolutional Neural Networks in impedance tomography. Machine Learning methods can be used to teach computers different technical problems. The efficient use of conventional artificial neural networks in tomography is possible able to effectively visualize objects. The first step of implementation Deep Learning methods in Electrical Impedance Tomography was performed in this work.
    W artykule zaprezentowano dwa przypadki dotyczące zastosowania sztucznych sieci neuronowych i konwolucyjnych sieci neuronowych w tomografii impedancyjnej. Uczenie maszynowe może znaleźć zastosowanie przy rozwiązywaniu różnorodnych problemów technicznych. W tomograficznej rekonstrukcji obrazów można stosować konwencjonalne sieci neuronowe. W niniejszej pracy przedstawiono przykład zastosowania metod głębokiego uczenia w obszarze elektrycznej tomografii impedancyjnej.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2017, 7, 3; 99-102
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A genetic algorithm based optimized convolutional neural network for face recognition
    Autorzy:
    Karlupia, Namrata
    Mahajan, Palak
    Abrol, Pawanesh
    Lehana, Parveen K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2201023.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    convolutional neural network
    genetic algorithm
    deep learning
    evolutionary technique
    sieć neuronowa konwolucyjna
    algorytm genetyczny
    uczenie głębokie
    technika ewolucyjna
    Opis:
    Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    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
    Tytuł:
    Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea
    Autorzy:
    Kandukuri, Usha Rani
    Prakash, Allam Jaya
    Patro, Kiran Kumar
    Neelapu, Bala Chakravarthy
    Tadeusiewicz, Ryszard
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/24200694.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sleep apnea
    convolutional neural network
    constant Q-transform
    deep learning
    single lead ECG signal
    non apnea
    obstructive sleep apnea
    bezdech senny
    sieć neuronowa konwolucyjna
    uczenie głębokie
    sygnał EKG
    obturacyjny bezdech senny
    Opis:
    Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 493--506
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies