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  1. Article ; Online: Using Statistical Model to Study the Daily Closing Price Index in the Kingdom of Saudi Arabia (KSA)

    Hassan M. Aljohani / Azhari A. Elhag

    Complexity, Vol

    2021  Volume 2021

    Abstract: Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information (or instances) whose category membership ...

    Abstract Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information (or instances) whose category membership is known. The article aims to use the Gaussian Mixture Model to model the daily closing price index over the period of 1/1/2013 to 16/8/2020 in the Kingdom of Saudi Arabia. The daily closing price index over the period declined, which might be the effect of corona virus, and the mean of the study period is about 7866.965. The closing price is the last regular deal that took place during the continuous trading period. If there are no transactions on the stock during the day, the closing price is the previous day’s closing price. The closing auction period comes after the continuous trading period (from 3 : 00 PM to 3 : 10 PM), during which investors can enter by buying and selling the stocks at this period. The experimental results show that the best mixture model is E (equal variance) with three components according to the BIC criterion. The expectation-maximization (EM) algorithm converged in 2 repetitions. The data source is from Tadawul KSA.
    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Subject code 330
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article: Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning.

    Mohamed, Nuzaiha / Almutairi, Reem Lafi / Abdelrahim, Sayda / Alharbi, Randa / Alhomayani, Fahad Mohammed / Elamin Elnaim, Bushra M / Elhag, Azhari A / Dhakal, Rajendra

    Cancers

    2023  Volume 16, Issue 1

    Abstract: Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, ... ...

    Abstract Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.
    Language English
    Publishing date 2023-12-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers16010181
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Artificial neural networks and statistical models for optimization studying COVID-19.

    Elhag, Azhari A / Aloafi, Tahani A / Jawa, Taghreed M / Sayed-Ahmed, Neveen / Bayones, F S / Bouslimi, J

    Results in physics

    2021  Volume 25, Page(s) 104274

    Abstract: The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the ...

    Abstract The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the world has passed from the first peak with relative success, this should be evaluated by statistical analysis in preparation for potential further waves. Artificial neural networks and logistic regression models were used in this study, and some statistical indicators were extracted to shed light on this pandemic. WHO website data for 32 European countries from 11th of January 2020 to 29th of May 2020 was utilized. The rationale for choosing the stated methodological tools is that the classification accuracy rate of artificial neural networks is 85.6% while the classification accuracy rate of logistic regression models 80.8%.
    Language English
    Publishing date 2021-05-12
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2631798-9
    ISSN 2211-3797 ; 2211-3797
    ISSN (online) 2211-3797
    ISSN 2211-3797
    DOI 10.1016/j.rinp.2021.104274
    Database MEDical Literature Analysis and Retrieval System OnLINE

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