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    Wyświetlanie 1-7 z 7
    Tytuł:
    Usage of artificial neural networks in the diagnosis of knee joint disorders
    Zastosowanie sztucznych sieci neuronowych w diagnozie schorzeń stawu kolanowego
    Autorzy:
    Witkowski, Konrad
    Wieczorek, Mikołaj
    Powiązania:
    https://bibliotekanauki.pl/articles/27315456.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    classification
    MRI images
    Resnet
    Alexnet
    klasyfikacja
    zdjęcia MRI
    Opis:
    Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neuralnet architectures which aim to successfully classify abnormalityusing MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of modelswe used pretrainedAlexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showedthat for certain abnormalities our models can achieve up to 90% accuracy.
    Niniejszy artykuł porusza temat automatycznej diagnozy uszkodzenia stawu kolanowego z zastosowaniem sieci neuronowych. Zaproponowanokilka hybrydowych sieci neuronowych, które podjęły próbę poprawnej klasyfikacji nieprawidłowości wykorzystując zdjęcia rezonansu magnetycznego pochodzące z publicznie dostępnego zbioru. Do konstrukcjikombinacji sieci skorzystanoz pretrenowanych modeli (Alexnet, Resnet18, Resnet34) pobranychz Torchvision. Eksperyment pokazał, że dla klasyfikacji niektórych schorzeń modele osiągnęły nawet 90% skuteczności.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 4; 11--14
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Plant disease detection using ensembled CNN framework
    Autorzy:
    Mondal, Subhash
    Banerjee, Suharta
    Mukherjee, Subinoy
    Sengupta, Diganta
    Powiązania:
    https://bibliotekanauki.pl/articles/27312905.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    convolutional neural network
    disease detection
    ResNet-50
    VGG-19
    InceptionV3
    Opis:
    Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.
    Źródło:
    Computer Science; 2022, 23 (3); 321--333
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Advancing Chipboard Milling Process Monitoring through Spectrogram-Based Time Series Analysis with Convolutional Neural Network using Pretrained Networks
    Autorzy:
    Kurek, Jarosław
    Szymanowski, Karol
    Chmielewski, Leszek
    Orłowski, Arkadiusz
    Powiązania:
    https://bibliotekanauki.pl/articles/27323142.pdf
    Data publikacji:
    2023
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
    Tematy:
    convolutional neural networks
    CNN
    vgg16
    vgg19
    resnet34
    tool state monitoring
    chipboard milling
    Opis:
    This paper presents a novel approach to enhance chipboard milling process monitoring in the furniture manufacturing sector using Convolutional Neural Networks (CNNs) with pretrained architectures like VGG16, VGG19, and RESNET34. The study leverages spectrogram representations of time-series data obtained during the milling process, providing a unique perspective on tool condition monitoring. The efficiency of the CNN models in accurately classifying tool conditions into distinct states (‘Green’, ‘Yellow’, and ‘Red’) based on wear levels is thoroughly evaluated. Experimental results demonstrate that VGG16 and VGG19 achieve high accuracy, however with longer training times, while RESNET34 offers faster training at the cost of reduced precision. This research not only highlights the potential of pretrained CNNs in industrial applications but also opens new avenues for predictive maintenance and quality control in manufacturing, underscoring the broader applicability of AI in industrial automation and monitoring systems.
    Źródło:
    Machine Graphics & Vision; 2023, 32, 2; 89--108
    1230-0535
    2720-250X
    Pojawia się w:
    Machine Graphics & Vision
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Segmentation of bone structures with the use of deep learning techniques
    Autorzy:
    Krawczyk, Zuzanna
    Starzyński, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2173574.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    semantic segmentation
    U-net
    FCN
    ResNet
    computed tomography
    technika deep learning
    głęboka nauka
    segmentacja semantyczna
    tomografia komputerowa
    Opis:
    The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136751
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Segmentation of bone structures with the use of deep learning techniques
    Autorzy:
    Krawczyk, Zuzanna
    Starzyński, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2128158.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    semantic segmentation
    U-net
    FCN
    ResNet
    computed tomography
    technika deep learning
    głęboka nauka
    segmentacja semantyczna
    tomografia komputerowa
    Opis:
    The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136751, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The automatic focus segmentation of multi-focus image fusion
    Autorzy:
    Hawari, K.
    Ismail
    Powiązania:
    https://bibliotekanauki.pl/articles/2173548.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    ResNet50
    multifocus image fusion
    głęboka nauka
    wieloogniskowa fuzja obrazu
    Opis:
    Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method is not sufficient to handle the ghost effect and unpredicted noises. Computational intelligence has developed quickly over recent decades, followed by the rapid development of multi-focus image fusion. The proposed method is multi-focus image fusion based on an automatic encoder-decoder algorithm. It uses deeplabV3+ architecture. During the training process, it uses a multi-focus dataset and ground truth. Then, the model of the network is constructed through the training process. This model was adopted in the testing process of sets to predict the focus map. The testing process is semantic focus processing. Lastly, the fusion process involves a focus map and multi-focus images to configure the fused image. The results show that the fused images do not contain any ghost effects or any unpredicted tiny objects. The assessment metric of the proposed method uses two aspects. The first is the accuracy of predicting a focus map, the second is an objective assessment of the fused image such as mutual information, SSIM, and PSNR indexes. They show a high score of precision and recall. In addition, the indexes of SSIM, PSNR, and mutual information are high. The proposed method also has more stable performance compared with other methods. Finally, the Resnet50 model algorithm in multi-focus image fusion can handle the ghost effect problem well.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e140352, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid deep learning model-based prediction of images related to cyberbullying
    Autorzy:
    Elmezain, Mahmoud
    Malki, Amer
    Gad, Ibrahim
    Atlam, El-Sayed
    Powiązania:
    https://bibliotekanauki.pl/articles/2142490.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    cyberbullying
    ResNet50
    MobileNetV2
    support vector machine
    cyberprzemoc
    maszyna wektorów wsparcia
    Opis:
    Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 323--334
    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

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