Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification.

The classification of Alzheimer's disease (AD) using deep learning models is hindered by the limited availability of data. Medical image datasets are scarce due to stringent regulations on patient privacy, preventing their widespread use in research. Moreover, although open-access databases such as the Open Access Series of Imaging Studies (OASIS) are available publicly for providing medical image data for research, they often suffer from imbalanced classes.

Enhancing Photocurrent Performance Based on Photoanode Thickness and Surface Plasmon Resonance Using Ag-TiO Nanocomposites in Dye-Sensitized Solar Cells.

This study investigated the different thicknesses of TiO photoanode films and the effect of surface plasmon resonance (SPR) of Ag-TiO nanocomposites on the current-voltage (I-V) performance of dye-sensitized solar cells (DSSC). The TiO layer was deposited using the doctor blade technique and the thickness of the TiO films was controlled by using a different number of Scotch tape layers. The silver nanoparticles (AgNP) were synthesised using a chemical reduction method and the concentration of sodium citrate as a reducing agent was varied from 4 to 12 mM to study the effect of citrate ion on the size of the nanoparticles.

Analysis of the Biceps Brachii Muscle by Varying the Arm Movement Level and Load Resistance Band.

Biceps brachii muscle illness is one of the common physical disabilities that requires rehabilitation exercises in order to build up the strength of the muscle after surgery. It is also important to monitor the condition of the muscle during the rehabilitation exercise through electromyography (EMG) signals. The purpose of this study was to analyse and investigate the selection of the best mother wavelet (MWT) function and depth of the decomposition level in the wavelet denoising EMG signals through the discrete wavelet transform (DWT) method at each decomposition level.