Browsing Faculty Contributions by Subject "Machine Learning"

Now showing items 21-26 of 26

    • Osseointegration Pharmacology: A Systematic Mapping Using Artificial Intelligence 

      Mahri M.; Shen N.; Berrizbeitia F.; Rodan R.; Daer A.; ... more authors ( Acta Materialia Inc , 2021 , Article)
      Clinical performance of osseointegrated implants could be compromised by the medications taken by patients. The effect of a specific medication on osseointegration can be easily investigated using traditional systematic ...

    • Predicting hypertension using machine learning: Findings from Qatar Biobank Study 

      AlKaabi, Latifa A; Ahmed, Lina S; Al Attiyah, Maryam F; Abdel-Rahman, Manar E ( PLoS ONE , 2020 , Article)
      Hypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The ...

    • Predictive Auto-scaling of Multi-tier Applications Using Performance Varying Cloud Resources 

      Iqbal, Waheed; Erradi, Abdelkarim; Abdullah, Muhammad; Mahmood, Arif ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Article)
      The performance of the same type of cloud resources, such as virtual machines (VMs), varies over time mainly due to hardware heterogeneity, resource contention among co-located VMs, and virtualization overhead. The performance ...

    • PV power prediction in Qatar based on machine learning approach 

      Benhmed K.; Touati F.; Al-Hitmi M.; Chowdhury N.A.; Gonzales A.S.P.; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      PV output power is highly sensitive to many environmental parameters, hence, power available from plants based on this technology will be affected, especially in harsh environments such that of Gulf countries. In order to ...

    • Self-organized Operational Neural Networks with Generative Neurons 

      Kiranyaz, Mustafa Serkan; Malik J.; Abdallah H.B.; Ince T.; Iosifidis A.; ... more authors ( Elsevier Ltd , 2021 , Article)
      Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron ...

    • Smart stock exchange market: A secure predictive decentralized model 

      Bansal, Gaurang; Hasija, Vikas; Chamola, Vinay; Kumar, Neeraj; Guizani, Mohsen ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)
      Stock exchanges around the world are exploring the best possible solution that can improve trading efficiency, lower the risks and tighten secu- rity levels. The working and functioning of a stock exchange involves very ...