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4th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2022 ; : 91-100, 2022.
Article in English | Scopus | ID: covidwho-2271371


Accurate energy consumption prediction is critical for proper resource allocation, meeting energy demand, and energy supply security. This work aims at developing a methodology for accurately modeling and predicting electricity consumption during abnormal long-lasting events, such as COVID-19 pandemic, which considerably affect consumption patterns in different types of premises. The proposed methodology involves three steps: (A) selects among multiple models the most accurate one in energy consumption prediction under normal conditions, (B) uses the selected model to analyze the impact of a specific abnormal event on energy consumption for various classes of premises, and (C) investigates which features contribute most to energy consumption prediction for abnormal conditions and which features can be added to improve such predictions.We use COVID-19 as a case study with datasets obtained from Fort Collins Utilities, which contain energy consumption data for residential and different sizes of commercial and industrial premises in the city of Fort Collins, Colorado, USA. We also use temperature records from NOAA and COVID-19 public orders from Larimer County.We validate the methodology by demonstrating that the methodology can help design a model suited for the pandemic situation using representative features, and as a result, accurately predict the energy consumption. Our results show that the MLP model selected by our methodology performs better than the other models even when they all use the COVID-related features. We also demonstrate that the methodology can help measure the impacts of the pandemic on the energy consumption. © 2022 IEEE.

2023 Australasian Computer Science Week, ACSW 2023 ; : 151-159, 2023.
Article in English | Scopus | ID: covidwho-2265791


Chest X-ray images provide critical information for the diagnosis of COVID-19. Machine learning techniques for COVID-19 detection require substantial amounts of chest images to discover correct patterns. However, concerns over confidentiality and privacy have limited access to patients' data. The distribution of samples across normal/abnormal classes is typically biased or skewed due to unavailability of sufficient data because of COVID-19 recency. Existing synthetic COVID-19 data generation approaches fail to generate high-resolution and diverse images. Moreover, there is a lack of research identifying whether synthetic images represent patients at high risk of severe disease, which is critical for making treatment decisions. We propose a High-Resolution COVID-19 X-Ray Generator (HRCX) framework based on a combination of a generative adversarial network and a predictive learning model that uses limited available chest images to generate balanced diverse high-resolution COVID-19 images with their severity scores. We use StyleGAN2 with adaptive discriminator augmentation, which controls generated images' style and generates diverse patterns. In addition, we provide a COVID-19 severity index to aid in predicting illness severity. We generated 3300 high-quality and diverse COVID-19 X-Ray images with a resolution of 512x512, which we further increased to 1024x1024 with the help of Super-Resolution. Additionally, severity scores of 300 images are calculated and demonstrated to be effective in both normal and infected cases. © 2023 ACM.