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    Wyświetlanie 1-4 z 4
    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
    Tytuł:
    Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation
    Autorzy:
    Kantavat, P.
    Kijsirikul, B.
    Songsiri, P.
    Fukui, K. I.
    Numao, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330532.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    support vector machine
    multi-class classification
    generalization error
    decision tree
    maszyna wektorów wsparcia
    klasyfikacja wieloklasowa
    błąd generalizacji
    drzewo decyzyjne
    Opis:
    We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes. We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 705-717
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Facial expression recognition under difficult conditions: A comprehensive study on edge directional texture patterns
    Autorzy:
    Ahmed, F.
    Kabir, M. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/331105.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    directional ternary pattern
    compressed DTP
    facial feature descriptor
    texture encoding
    support vector machine
    deskryptor cech
    kodowanie struktury
    maszyna wektorów wsparcia
    Opis:
    In recent years, research in automated facial expression recognition has attained significant attention for its potential applicability in human–computer interaction, surveillance systems, animation, and consumer electronics. However, recognition in uncontrolled environments under the presence of illumination and pose variations, low-resolution video, occlusion, and random noise is still a challenging research problem. In this paper, we investigate recognition of facial expression in difficult conditions by means of an effective facial feature descriptor, namely the directional ternary pattern (DTP). Given a face image, the DTP operator describes the facial feature by quantizing the eight-directional edge response values, capturing essential texture properties, such as presence of edges, corners, points, lines, etc. We also present an enhancement of the basic DTP encoding method, namely the compressed DTP (cDTP) that can describe the local texture more effectively with fewer features. The recognition performances of the proposed DTP and cDTP descriptors are evaluated using the Cohn–Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, we simulate difficult conditions using original database images with lighting variations, low-resolution images obtained by down-sampling the original, and images corrupted with Gaussian noise. In all cases, the proposed method outperforms some of the well-known face feature descriptors.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 399-409
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Implementation of digital twin and support vector machine in structural health monitoring of bridges
    Autorzy:
    Al-Hijazeen, Asseel Za'al Ode
    Fawad, Muhammad
    Gerges, Michael
    Koris, Kálmán
    Salamak, Marek
    Powiązania:
    https://bibliotekanauki.pl/articles/27312162.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    monitorowanie stanu konstrukcji
    most
    uszkodzenie
    bliźniak cyfrowy
    uczenie maszynowe
    maszyna wektorów wsparcia
    structural health monitoring
    bridge
    damage
    digital twin
    machine learning
    support vector machine
    Opis:
    Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.
    Źródło:
    Archives of Civil Engineering; 2023, 69, 3; 31--47
    1230-2945
    Pojawia się w:
    Archives of Civil Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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