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Wyszukujesz frazę "Mohammed, Hesham S." wg kryterium: Autor

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    Wyświetlanie 1-2 z 2
    Tytuł:
    Kinetics study and reaction mechanism for titanium dissolution from rutile ores and concentrates using sulfuric acid solutions
    Autorzy:
    Ismael, Mohamed H.
    Mohammed, Hesham S.
    El Hussaini, Omneya M.
    El-Shahat, Mohamed F.
    Powiązania:
    https://bibliotekanauki.pl/articles/2146851.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    leaching kinetics
    leaching mechanism
    titanium
    rutile concentrate
    leaching design
    shrinking
    core model
    Opis:
    Recent developments of acid leaching of titanium concentrates and ores have produced renewed industrial and commercial interest. However, the leaching kinetics and mechanism of these concentrates and ores had received little attention. This work, therefore, addresses the leaching kinetics and mechanism of Ti from a rutile concentrate in sulfuric acid solution. The leaching reaction was controlled by diverse parameters like temperature, particle size, acid concentration, liquid/solid (L/S) ratio, and stirring speed. The leaching kinetics was investigated using the Shrinking Core Model in order to determine the optimum criteria which control the reaction. The kinetics analysis showed that the rate of dissolution of Ti increased by increasing reaction temperature, L/S ratio, and stirring speed, while it decreased upon increasing particle size. The kinetics analysis revealed that the dissolution reaction is controlled by the chemical reaction at the rutile particle surface. Applying the Arrhenius relation, the apparent energy of activation Ea for the leaching reaction was calculated to be 23.4kJ/mol. A semi-empirical overall rate equation was introduced to describe the combined effects of the process variables upon the rate of the dissolution reaction: 〖1-(1-x)〗^(1/3)=k_0 〖 C〗_([H2SO4])^0.803 〖 (dp)〗^(-0.518) 〖(L/S)〗^0.793 〖(w)〗^0.668 e^((-23400/RT)) t
    Źródło:
    Physicochemical Problems of Mineral Processing; 2022, 58, 1; 138--148
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques
    Autorzy:
    Eldeeb, Ahmed H.
    Amr, Mohammed Nagah
    Ibrahim, Amin S.
    Kamel, Hesham
    Fouad, Sara
    Powiązania:
    https://bibliotekanauki.pl/articles/2200729.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Deep learning
    COVID-19
    Artificial Intelligence
    computed tomography
    Convolutional Neural Networks
    Opis:
    The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 731--739
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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