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    Wyświetlanie 1-9 z 9
    Tytuł:
    Theory I: Deep networks and the curse of dimensionality
    Autorzy:
    Poggio, T.
    Liao, Q.
    Powiązania:
    https://bibliotekanauki.pl/articles/200623.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep network
    shallow network
    convolutional neural network
    function approximation
    deep learning
    sieci neuronowe
    aproksymacja funkcji
    uczenie głębokie
    Opis:
    We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    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:
    Zastosowanie sieci konwolucyjnej głębokiego uczenia w detekcji pojazdów
    Use of deep learning convolutional network in vehicle detectionmears
    Autorzy:
    Oszutowska-Mazurek, D. A.
    Mazurek, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/136004.pdf
    Data publikacji:
    2017
    Wydawca:
    Wyższa Szkoła Techniczno-Ekonomiczna w Szczecinie
    Tematy:
    sieć konwolucyjna
    uczenie głębokie
    detekcja pojazdów
    przetwarzanie obrazów
    image processing
    deep learning
    convolutional neural network
    vehicle detection
    Opis:
    Wstęp i cel: Detekcja pojazdów na znaczenie w bezpieczeństwie ruchu drogowego oraz programowaniu pojazdów autonomicznych. Celem pracy jest detekcja pojazdów odróżniająca obrazy pojazdów od innych obrazów nie zawierających pojazdów. Materiał i metody: W pracy wykorzystano bazę pojazdów zawierającą obrazy ekstrahowane z sekwencji wideo, które przetwarzano za pomocą sieci konwolucyjnej głębokiego uczenia. Wyniki: Uzyskana sieć konwolucyjna charakteryzuje się bardzo dobrymi parametrami, krzywa PSNR względem kroku uczenia rośnie co oznacza, że zachodzi proces odszumiania kerneli w całym procesie uczenia. Wniosek: Proponowana metoda może być wykorzystana w programowaniu pojazdów autonomicznych oraz implementacji w Inteligentnych Systemach Transportowych ITS do detekcji pojazdów; bazuje na uczeniu a nie na projektowaniu algorytmu syntetycznego, dzięki temu jest potrzebny relatywnie krótki czas opracowania klasyfikatora.
    Introduction and aim: Vehicle detection plays essential role in road safety and automatic vehicle programming. The aim of study is vehicle detection distinguishing car and non-car images Material and methods: Vehicle database images extracted from video sequences were processed by deep learning convolutional network. Results: Obtained convolutional network is characterised by very good parameters, PSNR curve indicates denoising of kernels in learning process. Conclusion: Proposed method is potentially useful in autonomic vehicles programming and Intelligent Transportation Systems (ITS) for vehicles detection. The solution is based on learning, not on synthetic algorithm design, thanks to this, a relatively short time of classifier development is needed.
    Źródło:
    Problemy Nauk Stosowanych; 2017, 7; 47-56
    2300-6110
    Pojawia się w:
    Problemy Nauk Stosowanych
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimating the distance to an object from grayscale stereo images using deep learning
    Autorzy:
    Kulawik, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/2202043.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
    Tematy:
    estimating distance
    stereo vision
    convolutional neural network
    deep learning
    szacowanie odległości
    widzenie stereoskopowe
    konwolucyjne sieci neuronowe
    uczenie głębokie
    Opis:
    This article presents an innovative proposal for estimating the distance between an autonomous vehicle and an object in front of it. Such information can be used, for example, to support the process of controlling an autonomous vehicle. The primary source of information in research is monochrome stereo images. The images were made in compliance with the laws of the canonical order. The developed convolutional neural network model was used for the estimation. A proprietary dataset was developed for the experiments. The analysis was based on the phenomenon of disparity in stereo images. As a result of the research, a correctly trained model of the CNN network was obtained in six variants. High accuracy of distance estimation was achieved. This publication describes an original proposal for a hybrid blend of digital image analysis, stereo-vision, and deep learning for engineering applications.
    Źródło:
    Journal of Applied Mathematics and Computational Mechanics; 2022, 21, 4; 60--72
    2299-9965
    Pojawia się w:
    Journal of Applied Mathematics and Computational Mechanics
    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:
    Combining Spectral Analysis with Artificial Intelligence in Heart Sound Study
    Autorzy:
    Kucharski, Dariusz
    Kajor, Marcin
    Grochala, Dominik
    Iwaniec, Marek
    Iwaniec, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/102508.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    deep learning
    heart sound classification
    convolutional neural network
    machine learning
    signal processing
    uczenie głębokie
    klasyfikacja dźwięku serca
    splotowa sieć neuronowa
    uczenie maszynowe
    przetwarzanie sygnałów
    Opis:
    The auscultation technique has been widely used in medicine as a screening examination for ages. Nowadays, advanced electronics and effective computational methods aim to support the healthcare sector by providing dedicated solutions which help physicians and support diagnostic process. In this paper, we propose a machine learning approach for the analysis of heart sounds. We used the spectral analysis of acoustic signal to calculate feature vectors and tested a set of machine learning approaches to provide the most effective detection of cardiac disorders. Finally, we achieved 91% of sensitivity and 99% of positive predictivity for a designed algorithm based on convolutional neural network.
    Źródło:
    Advances in Science and Technology. Research Journal; 2019, 13, 2; 112-118
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    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:
    Hybrid deep learning method for detection of liver cancer
    Autorzy:
    Deshmukh, Sunita P.
    Choudhari, Dharmaveer
    Amalraj, Shankar
    Matte, Pravin N.
    Powiązania:
    https://bibliotekanauki.pl/articles/38701864.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    liver cancer detection
    deep learning
    fully convolutional neural network
    hybrid approach
    discrete wavelet transform
    wykrywanie raka wątroby
    uczenie głębokie
    neuronowa sieć konwulcyjna
    podejście hybrydowe
    dyskretna transformata falkowa
    Opis:
    Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 151-165
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and 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 1-9 z 9

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