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  1. Article ; Online: Development of a Web-Based Prediction System for Students’ Academic Performance

    Dabiah Alboaneen / Modhe Almelihi / Rawan Alsubaie / Raneem Alghamdi / Lama Alshehri / Renad Alharthi

    Data, Vol 7, Iss 21, p

    2022  Volume 21

    Abstract: Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to ...

    Abstract Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the prediction’s Mean Absolute Percentage Error (MAPE) reached 6.34%, and the academic factors had a higher impact on students’ academic performance than the demographic factors, the midterm exam score in the top. The developed web-based prediction system is available on an online server and can be used by tutors.
    Keywords academic performance ; machine learning ; students’ performance ; Bibliography. Library science. Information resources ; Z
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia.

    Alboaneen, Dabiah / Pranggono, Bernardi / Alshammari, Dhahi / Alqahtani, Nourah / Alyaffer, Raja

    International journal of environmental research and public health

    2020  Volume 17, Issue 12

    Abstract: The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. ...

    Abstract The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.
    MeSH term(s) Betacoronavirus/pathogenicity ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/virology ; Forecasting ; Health Surveys ; Humans ; Models, Biological ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/virology ; SARS-CoV-2 ; Saudi Arabia/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-06-25
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph17124568
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

    Dabiah Alboaneen / Bernardi Pranggono / Dhahi Alshammari / Nourah Alqahtani / Raja Alyaffer

    International Journal of Environmental Research and Public Health, Vol 17, Iss 4568, p

    2020  Volume 4568

    Abstract: The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. ...

    Abstract The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.
    Keywords 2019 novel coronavirus ; COVID-19 ; Saudi Arabia ; logistic growth model ; SIR model ; Medicine ; R
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: A Predictive and Preventive Model for Onset of Alzheimer's Disease.

    Singhania, Udit / Tripathy, Balakrushna / Hasan, Mohammad Kamrul / Anumbe, Noble C / Alboaneen, Dabiah / Ahmed, Fatima Rayan Awad / Ahmed, Thowiba E / Nour, Manasik M Mohamed

    Frontiers in public health

    2021  Volume 9, Page(s) 751536

    Abstract: Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several ... ...

    Abstract Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
    MeSH term(s) Algorithms ; Alzheimer Disease/diagnosis ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Support Vector Machine
    Language English
    Publishing date 2021-10-11
    Publishing country Switzerland
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2711781-9
    ISSN 2296-2565 ; 2296-2565
    ISSN (online) 2296-2565
    ISSN 2296-2565
    DOI 10.3389/fpubh.2021.751536
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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  5. Article: Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

    Alboaneen, Dabiah / Pranggono, Bernardi / Alshammari, Dhahi / Alqahtani, Nourah / Alyaffer, Raja

    Int. j. environ. res. public health (Online)

    Abstract: The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. ...

    Abstract The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #614074
    Database COVID19

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  6. Article ; Online: Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia

    Alboaneen, Dabiah / Pranggono, Bernardi / Alshammari, Dhahi / Alqahtani, Nourah / Alyaffer, Raja

    2020  

    Abstract: The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. ...

    Abstract The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.
    Keywords covid19
    Language English
    Publishing date 2020-06-25
    Publisher MDPI AG
    Publishing country uk
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article ; Online: A Predictive and Preventive Model for Onset of Alzheimer's Disease

    Udit Singhania / Balakrushna Tripathy / Mohammad Kamrul Hasan / Noble C. Anumbe / Dabiah Alboaneen / Fatima Rayan Awad Ahmed / Thowiba E. Ahmed / Manasik M. Mohamed Nour

    Frontiers in Public Health, Vol

    2021  Volume 9

    Abstract: Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several ... ...

    Abstract Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
    Keywords machine learning ; Alzheimer's disease ; prediction ; prevention ; convolutional neural networks ; support vector machine ; Public aspects of medicine ; RA1-1270
    Subject code 006
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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