Artificial Intelligence

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    A HYBRID DEEP LEARNING MODEL FOR FORECASTING PM2.5 AIR POLLUTANT CONCENTRATIONS
    (An-Najah National University, 2024-12-18) Massad, Asma
    Air quality forecasting is a crucial research field that aids scientists and policymakers in making informed decisions to combat air pollution. Among various pollutants, PM2.5 -particulate matter with a diameter smaller than 2.5 micrometers- poses significant health risks, as it can reach the lower respiratory tract and enter the bloodstream. Accurately forecasting PM2.5 levels is thus essential. Although machine learning-based spatiotemporal forecasting models have advanced, the pursuit for more accurate forecasts continues. The use of hybrid deep learning models for PM2.5 forecasting represents a promising and active area of research, as these models aim to capture complex spatiotemporal dependencies more effectively. We developed a Dynamic Graph Attention Network (DyGAT) to model spatial dependencies effectively. DyGAT leverages engineered edge features, including distance, wind speed, and wind direction, while using attention mechanisms to capture the dynamic nature of these dependencies. DyGAT was then combined with Informer, a Transformer for efficient time-series forecasting, to capture spatial and temporal patterns comprehensively, improving prediction accuracy. Our model was evaluated on a benchmark dataset from Beijing, with 420,768 records over four years. DyGAT-Informer outperformed a version without the DyGAT component and other baseline models. It achieved 50.43 for MAE, 79.9 for RMSE and 28.88% for SMAPE, compared to 51.44 for MAE, 80.83 for RMSE and 30.25% for SMAPE in the next best model. Additionally, we conducted a case study using a dataset from Nablus, Palestine, consisting of 2692 records per station over a two months period. We incorporated geospatial features about nearby pollution sources into the dataset. Due to the insufficient number of records in the Nablus dataset for training the Informer, it was replaced with a sequence-to-sequence Long Short-Term Memory (LSTM) model. DyGAT-LSTM, trained with additional geospatial features about nearby pollution sources, achieved a 2.08% reduction in MAE, 1.17% in RMSE, and 1.96% in SMAPE. This confirms the benefit of incorporating such data. Finally, despite the short distances between stations, DyGAT successfully captured spatial dependencies, where DyGAT-LSTM achieved a reduction of 3.13% in MAE, 1.48% in RMSE, and 3.67% in SMAPE when compared to the LSTM-only model.
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    COMPUTER VISION AND MACHINE LEARNING APPROACHES FOR INTERPRETATION OF BREAST HISTOPATHOLOGY
    (An-Najah National University, 2024-09-12) Odeh, Omar
    Breast cancer, along with other cancer types, represents a major health issue worldwide, especially in regions with limited medical resources, such as Palestine where it’s harder to diagnose and treat cancer patients. The traditional diagnostic methods which rely mainly on the manual examination of histology slides in the lab by experts are both time consuming and require high skills, which often leads to delays in diagnosis and treatment. This research aims to leverage Whole Slide Imaging (WSI) and advanced image processing techniques, including machine learning and deep learning, to develop a tool to support breast cancer diagnosis in Palestine. A collection of histopathology images from breast cancer cases, provided by An-Najah Hospital in Nablus, Palestine, was used in this study. An automated classification model was developed, capable of identifying normal, benign, in situ, and invasive breast cancer tissues at magnifications of x20 and x40. It was empowered using advanced preprocessing techniques to normalize the colors and sizes of the training data. The classification model resulted in a remarkable performance of InceptionV3 and Gradient Boosting models with accuracies of up to 98.7% and 98% respectively in different settings. The same algorithm was applied to a BACH challenge dataset, resulting in a high accuracy of 96.7%, surpassing the results of other studies. The study also covers whole slide image (WSI) analysis, emphasizing the complex approach required for the proper detection of malignancy and tumor size estimation using a customized segmentation approach which resulted in an 84% Jaccard index score. Additionally, it discusses novel approaches in localization of tumor beds and grading of treatment responses, aiming to provide a methodological basis for improving patient care outcomes in challenging environments like Palestine. Keywords: Breast Cancer Diagnosis, Automated Image Analysis, Whole Slide Imaging (WSI), Deep Learning in Pathology, Healthcare in Palestine.