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"Enhanced framework for COVID-19 prediction with computed tomography sc" by Anand Motwani, Piyush Kumar Shukla et al.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of ‘False Negatives’ can put lives at risk. The primary objective is to improve the model so that it does not reveal ‘Covid’ as ‘Non-Covid’. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the ....

False Negative , Chest Ct Images , Covid 19 , Deep Learning , Ense Convolutional Neural Network , Loss Function , Ars Cov 2 ,

MSNBC The ReidOut June 4, 2024 00:59:00

About the racial healing worked at the color foundation had been done, and i will be totally say, slightly skeptical, when i hear the word. i have materialist conception of how the stuff works. it s about who owns property, how the loss function, but the work they have done is fascinating. they do this interesting work with groups, and it is a way in to get from what i, would say, is the psychological, to the political. i think there is an interesting story to tell about how it works. it s particularly important in early 2023, which you see several ways of consciousness about racism, racial hierarchy in america, and in its effects. the backlash isn t that consciousness, and they re in that contested space. they re in the summer of 2020, and there is the anti critical race theory. there is an opening now, for conversations, and it was a ....

Stuff Works , Loss Function , Color Foundation , U S , Isn T , Anti Critical Race Theory ,

"Conditional Generative Adversarial Networks for Domain Transfer: A Sur" by Guoqiang Zhou, Yi Fan et al.

Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how to improve it to achieve better performance and how to evaluate such performance across the variants. Afterward, the main applications of CGAN in domain transfer are presented. Finally, as another major contribution, we also list the current problems and challenges of CGAN. ....

Generative Adversarial Network , Adversarial Network , Conditional Generative Adversarial Network , Ycle Consistency , Domain Transfer , Loss Function , Rogressive Enhancement Mechanism ,

Population imbalances lead to changes in geographical boundaries, says RSS chief Mohan Bhagwat

Rashtriya Swayamsevak Sangh chief Dr Mohan Bhagwat said, ....

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