Browsing by Author "Fatima, Areej"
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Item Energy-efficiency model for residential buildings using supervised machine learning algorithm(Tech Science Press, 2021) Aslam, Muhammad Shoukat; Ghazal, Taher M.; Fatima, Areej; Said, Raed A.; Abbas, Sagheer; Khan, Muhammad Adnan; Siddiqui, Shahan Yamin; Ahmad, MunirThe real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appro-priate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent optimization (E2B-GDO). This model can predict a building’s heating-load conservation based on a building energy performance dataset. The input layer includes area (distribution of the glazing area, wall area, and surface area), relative density, and overall elevation. The proposed E2B-GDO model achieved an accuracy of 99.98% for training and 98.00% for validation. © 2021, Tech Science Press. All rights reserved.Item Impact and Research Challenges of Penetrating Testing and Vulnerability Assessment on Network Threat(Institute of Electrical and Electronics Engineers Inc., 2023) Fatima, Areej; Khan, Tahir Abbas; Abdellatif, Tamer Mohamed; Zulfiqar, Sidra; Asif, Muhammad; Safi, Waseem; Hamadi, Hussam Al; Al-Kassem, Amer HaniItem An iomt-enabled smart healthcare model to monitor elderly people using machine learning technique(Hindawi Limited, 2021) Khan, Muhammad Farrukh; Ghazal, Taher M.; Said, Raed A.; Fatima, Areej; Abbas, Sagheer; Khan, M.A.; Issa, Ghassan F.; Ahmad, Munir; Khan, Muhammad AdnanThe Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result. © 2021 Muhammad Farrukh Khan et al.