Alzheimer's Disease Prediction Using 3D-CNNs: Intelligent Processing of Neuroimaging Data.

Alzheimer's disease (AD) is a severe neurological illness that demolishes memory and brain functioning. This disease affects an individual's capacity to work, think, and behave. The proportion of individuals suffering from AD is rapidly increasing.

An efficient sparse code shrinkage technique for ECG denoising using empirical mode decomposition.

Accurate denoising of Electrocardiogram (ECG) signals is essential for reliable cardiac diagnostics, but traditional methods often struggle with high-frequency noise and artifacts, leading to potential misinterpretations. It is often impeded by interference such as power line interference (PLI) and Gaussian noise. To address this challenge, we suggest a novel ECG denoising technique that combines empirical mode decomposition (EMD) with wavelet domain sparse code shrinking.

A secure blockchain framework for healthcare records management systems.

Electronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance.

Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception.

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks.

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere.