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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Data Sciences
Abusaleh, Sumaya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Detection of COVID-19 using EfficientnetV2-XL and Radam Optimizer from Chest X-ray Images Alshalabi, Ibrahim Alkore; Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Zakarya; Alemerien, Khalid; Al-Eidi, Shorouq; Alshamaseen, Hamzah
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.512

Abstract

Automating the detection of the COVID-19 pandemic has become necessary for assisting radiologists and medical practitioners in the diagnosis process. It enables them not only to save time through early diagnosis but also to ensure that they are making more accurate diagnoses. Therefore, this research presents a novel approach for automatically identifying COVID-19 in chest X-ray images by utilizing the EfficientNetV2-XL model in combination with the Rectified Adam optimizer for training. For conducting the experiments, we used the dataset available on Kaggle, known as the “COVID-19 Radiography Dataset.” The totality of this dataset was 21,165, and it included four patterns: COVID-19, viral pneumonia, lung opacity, and normal cases. The dataset was divided into 80% training and 20% testing. The preprocessing stage included resizing images to 512 × 512 pixels and then applying data augmentation techniques to enhance model robustness. Consequently, a fine-tuned multiclass categorization system was implemented. The proposed system's effectiveness is evidenced by the experimental outcomes, which show a 99.31% accuracy rate and a perfect Area Under the Curve score of 1 for identifying COVID-19. Additionally, the Score-CAM visualization method was utilized to enhance the interpretability of model predictions, identifying key regions within the chest X-ray images that influence the classification outcome. This Localization technique aids healthcare professionals in understanding the reasoning behind the model and confirming the accuracy of the diagnosis. The proposed system outperformed the state-of-the-art models for COVID-19 detection. 
Life balloon: a paradigm shift in earthquake safety-intelligent IoT detection and protection system for optimal resilience Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Z.; Alemerien, Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp987-997

Abstract

Internet of things (IoT) applications for environmental monitoring have greatly improved due to advances in hardware and software technologies. Given the significant economic and societal impacts of earthquakes, there is an increasing need to develop effective earthquake early warning systems (EEWS). However, designing such intelligent systems remains challenging because of inefficient classification methods and limitations in high-fidelity sensing capabilities. To reduce the devastating effects of earthquakes, this paper proposes an earthquake detection and protection system. The system’s primary function is to detect seismic signals and activate a specially designed airbag (life balloon) unit that protects occupants in apartment buildings. In addition, the unit helps maintain necessary oxygen levels, thereby improving occupant safety during seismic events. The proposed system also includes a communication method that transmits critical information about the affected area to relevant parties. Early data transmission enables rapid response and guides the efficient deployment of required resources, making aftershock management more effective. By combining advanced sensor technologies with efficient communication methods, the proposed system aims to enhance safety and emergency management while providing comprehensive protection and support during seismic events. Experimental results show that the proposed method achieves approximately 95% sensitivity and 94.2% accuracy.
Co-Authors Al-Eidi, Shorouq Alemerien, Khalid Alksasbeh, Malek Z. Alksasbeh, Malek Zakarya Alrawashdeh, Tawfiq Alshalabi, Ibrahim Alkore Alshamaseen, Hamzah