In today's world, a large amount of data is being collected every minute. This data came from
different sources: health care, social media, business applications, manufacturing industries …

An algorithm framework based on CycleGAN and an upgraded dual‐path network (DPN) is
suggested to address the difficulties of uneven staining in pathological pictures and difficulty …

The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful
tool for connecting various medical equipment with in‐built sensors and healthcare …

The study is intended to be in accordance with recent World Health Organization (WHO)
findings on Congenital heart disorders (CHD), the most frequent heart condition and feasible …

Through the use of a Random Forest Classifier and the utilization of a varied dataset, this
research presents a machine learning model that is targeted at the early diagnosis of lung …

This paper implements the deep learning technique VGG-16 convolutional neural network
architecture that provides a method to correctly recognize ASL alphabet letters and symbols …

This research investigates the accuracy and loss metrics of many text-to-image synthesis
models, such as VQGAN+ CLIP, BigGAN, StyleGAN, SR3, Imagen, and Glide. We aim to …

This American Sign Language Model represents an advanced deep learning model
framework that employs convolutional, bilateral LSTM, and dense layers to analyze …

The monitoring of health and the technologies that are related to it are an exciting area of
research. The paper proposes a mechanical manufacturing vibration monitoring system that …

The complicated CNN (convolutional neural networks) of the InceptionV3 model is proposed
in this article as a novel approach for the classification of eye conditions. The work uses …