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您的检索式：作者名="Saleh Alrashed"
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| 1 | Deep Fakes in Healthcare:How Deep Learning Can Help to Detect Forgeries显示文摘With the increasing use of deep learning technology,there is a growing concern over creating deep fake images and videos that can potentially be used for fraud.In healthcare,manipulating medical images could lead to misdiagnosis and potentially life-threatening consequences.Therefore,the primary purpose of this study is to explore the use of deep learning algorithms to detect deep fake images by solving the problem of recognizing the handling of samples of cancer and other diseases.Therefore,this research proposes a framework that leverages state-of-the-art deep convolutional neural networks(CNN)and a large dataset of authentic and deep fake medical images to train a model capable of distinguishing between authentic and fake medical images.Specifically,the paper trained six CNN models,namely,ResNet101,ResNet50,DensNet121,DenseNet201,MobileNetV2,andMobileNet.These models had trained using 2000 samples over three classes:Untampered,False-Benign,and False-Malicious,and compared against several state-of-the-art deep fake detection models.The proposed model enhanced ResNet101 by adding more layers,achieving a training accuracy of 99%.The findings of this study show near-perfect accuracy in detecting instances of tumor injections and removals. | Alaa Alsaheel Reem Alhassoun Reema Alrashed Noura Almatrafi Noura Almallouhi Saleh Albahli | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 2 | Numerical simulations of reaction-diffusion equations modeling prey-predator interaction with delay显示文摘 | Ishtiaq Ali Ghulam Rasool Saleh Alrashed | 2018 | International Journal of Biomathematics2018，11，4： | 0 |
| 3 | COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries显示文摘Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback.The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms.This phenomenon caused a state of panic among people.Different studies were conducted to stop the spread of fake news to help people cope with the situation.In this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)techniques.It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative reactions.The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment overall.The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly. | Saleh Albahli Ahmad Algsham Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed Hafiz Tayyab Rauf Muhammad Arif Mazin Abed Mohammed | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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