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您的检索式：作者名="Rashed Alsharif"
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| 1 | Ensemble machine learning framework for daylight modelling of various building layouts显示文摘The application of machine learning(ML)modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment.Although many advancements have been made,no standardized ML modelling framework exists in daylight assessment.In this study,625 different building layouts were generated to model useful daylight illuminance(UDI).Two state-of-the-art ML algorithms,eXtreme Gradient Boosting(XGBoost)and random forest(RF),were employed to analyze UDI in four categories:UDI-f(fell short),UDI-s(supplementary),UDI-a(autonomous),and UDI-e(exceeded).A feature(internal finish)was introduced to the framework to better reflect real-world representation.The results show that XGBoost models predict UDI with a maximum accuracy of R^(2)=0.992.Compared to RF,the XGBoost ML models can significantly reduce prediction errors.Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction. | Rashed Alsharif Mehrdad Arashpour Emad Golafshani Milad Bazli Saeed Reza Mohandes | 2023 | Building Simulation2023，16，11： | 0 |
| 2 | Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network显示文摘Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep learning.It is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities. | Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed | 2022 | Computers, Materials & Continua2022，，9： | 0 |
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