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    Assessing the Spreading Behavior of the Covid-19 Epidemic: A Case Study of Turkey
    (Institute of Electrical and Electronics Engineers Inc., 2022) Demir, E.; Canitez, M.N.; Elazab, M.; Hameed, A.A.; Jamil, A.; Al-Dulaimi, A.A.
    Coronavirus (Covid-19) disease is a rapidly spreading type of virus that was discovered in Wuhan, China, and emerged towards the end of 2019. During this period, various studies were conducted, and intensive studies are continued in different fields regarding coronavirus, especially in the field of medicine. The virus continues to spread and is yet to be controlled fully. Machine learning is a well-explored field in the domain of computer science that can learn patterns based on existing data and make predictions on new data. This study focused on using various machine learning approaches for predicting the spreading behavior of the COVID-19 virus. The models that were considered include SARIMAX, Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Artificial Neural Network (ANN). The models were trained and then predictions were made by applying these models to the daily updated data provided by the Turkish Ministry of Health. Experiments on the test data showed that both XGBoost and Decision Tree models outperformed other models. © 2022 IEEE.
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    Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques
    (Springer Science and Business Media Deutschland GmbH, 2024) Al-Dulaimi, A.A.; Hameed, A.A.; Guneser, M.T.; Jamil, A.
    Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.