Abstract Machine learning and deep learning models are commonly developed using
programming languages such as Python, C++, or R and deployed as web apps delivered …

Background In Japan, challenges were reported in accurately estimating the prevalence of
schizophrenia among the general population. Retrieving previous studies, we investigated …

Modern healthcare systems operate in highly dynamic environments requiring adaptable
access control mechanisms. Access to sensitive data and medical equipment should be …

In the article we propose designing and implementing an information system for code-free
generating of Deep Neural Networks (DNN) based on TensorFlow and Flutter. Because of …

In recent years, Deep Learning (DL) applications in JavaScript environment have become
increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks …

This study aims to train and implement a deep learning model for the fusion of website
creation and artificial intelligence, in the era known as the AI revolution following the launch …

Introduction: Contributions to medicine may come from different areas; and most areas are
full of researchers wanting to support. Physists may help with theory, such as for nuclear …

The research in this project explores the intersection of human computer interaction (HCI)
and deep neural networks. Advances in real-time output has reduced latency making …

This chapter discusses how to build production-ready machine learning systems. There are
several challenges involved in accomplishing this, each with its specific solutions regarding …

Traditional means of on-farm weed control mostly rely on manual labor. This process is time-
consuming, costly, and contributes to major yield losses. Further, the conventional …