Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively
tackling the intricate task of classifying MRI images, specifically in Alzheimer's disease …

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain
tumor classification. Radiologists could reliably detect tumors using machine learning …

ABSTRACT A brain tumour (BT) is a growth of tissue that is organised by a gradual
accumulation of anomalous cells, and it is significant to segment and classify the BT from …

Zigbee is a wireless network technology that operates on a community-based infrastructure.
The primary objective of this system is to allow for the effective and inexpensive transmission …

Simple Summary Wilms tumor (WT) is the most frequent pediatric tumor in children and
shows highly variable histology, leading to variation in classification. Artificial intelligence …

Epistaxis is a typical presentation in the otolaryngology and emergency department. When
compressive therapy fails, directive nasal cautery is necessary, which strongly …

This work aims to explore the fusion of deep learning and blockchain technology for
research applications in photography and art studies. A digital image watermarking model is …

The extensive accessibility of data in RS has propelled the use of FL. This approach
facilitates collaborative model training across distributed data sources while upholding data …

In this paper, an MXene (Ti 3 C 2 Tx) mediated surface plasmon resonance (SPR) sensor
based on bimetal (Copper-Nickle (Cu-Ni)) is suggested. MXene, a new class of2D …

Recent years have seen a surge in the number of studies utilizing Artificial Intelligence (AI)
on Magnetic Resonance Imaging (MRI) to analyze and categorize brain tumors. Despite the …