Abstract-Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps -first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.

Artificial Intelligence (AI) is the notion of machines mimicking complex cognitive functions usually associated with humans, such as reasoning, predicting, planning, and problem-solving. With constantly growing repositories of data, improving algorithmic sophistication and faster computing resources, AI is becoming increasingly integrated into everyday use. In healthcare, AI represents an opportunity to increase safety, improve quality, and reduce the burden on increasingly overstretched systems. As applications expand, the need for responsible oversight and governance becomes even more important. Artificial intelligence in the delivery of healthcare carries new opportunities and challenges, including the need for greater transparency, the impact AI tools may have on a larger number of patients and families, and potential biases that may be introduced by the way an AI platform was developed and built. This study provides practical guidance in the development and implementation of AI applications in healthcare, with a focus on risk identification, management, and mitigation.

In recent years, the average cost of healthcare-related data breaches increased from approximately US$7 million in 2020 to over US$9 million in 2021. Moreover, breaches in healthcare have been consistently more costly than in other sectors for 11 consecutive years. With the frequency and costs of cyberattacks expected to rise, healthcare organizations must carefully plan for and identify strategies to mitigate cyber-related risks. This paper provides practical guidance for boards, senior leaders and risk managers in the development and implementation of organization-specific cybersecurity measures, with a focus on the identification, mitigation and management of risks.