Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre-Train Deep Learning Models

Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2022, Vol 1, Issue 2

Abstract

Artificial intelligence (AI) and natural language processing (NLP) are relentless technologies for healthcare that can support a strong and secure digital system with embedded applications of internet of things (IoTs). The study tried to build an artificial intelligence-natural language processing cluster system. In the system, rich content is extracted using parts of speech and then classified into an understandable dataset. The unavailable uniqueness systems with standardize process and procedures for artificial intelligence and natural language processing across different systems to support E-healthcare sector is a big challenge for nations and the world at large. Aim to train a cluster system that extract rich content and fit into a deep learning model frame to enable interpretation of the dataset for healthcare needs through a fast and secure digital system. The study uses (behavior-oriented driven and influential functions) to determine the significance of AI and NLP on E-health. Based on a selective scorings method, a rate of 1 out of 5 grading was developed called the Key Benefits score. The behavior-oriented drive and influential function allows an in-depth evaluation of E-health based on the selection of text content applied to the sample proposed study. Results show a score of 3.947 scale significance of NLP and AI on E-health. The study concluded that well-defined artificial intelligence and natural language processing applications are perfect areas that advance positive results in healthcare electronic services.

Authors and Affiliations

Pascal Muam Mah

Keywords

Related Articles

Innovative Hybrid Deep Learning Models for Financial Sentiment Analysis

This study explores hybrid deep learning architectures for the classification of financial sentiment, focusing on the integration of the Convolutional Neural Network (CNN) with the Support Vector Machine (SVM) and the Ra...

A Survey on Multimedia Ontologies for a Semantic Annotation of Cinematographic Resources for the Web of Data

The Semantic Web provides approaches and tools that allow for the processing and analysis of online content, including multimedia resources. Multimedia resources like videos, audios, and photos are increasingly common in...

Predicting Bank Users’ Time Deposits Based on LSTM-Stacked Modeling

Accurately predicting whether bank users will opt for time deposit products is critical for optimizing marketing strategies and enhancing user engagement, ultimately improving a bank’s profitability. Traditional predicti...

Robust Leaf Disease Detection Using Complex Fuzzy Sets and HSV-Based Color Segmentation Techniques

Leaf diseases pose a significant threat to global agricultural productivity, impacting both crop yields and quality. Traditional detection methods often rely on expert knowledge, are labor-intensive, and can be time-cons...

A Comprehensive Review of Ant Colony Optimization in Swarm Intelligence for Complex Problem Solving

Swarm intelligence (SI) has emerged as a transformative approach in solving complex optimization problems by drawing inspiration from collective behaviors observed in nature, particularly among social animals and insects...

Download PDF file
  • EP ID EP731874
  • DOI https://doi.org/10.56578/ataiml010201
  • Views 66
  • Downloads 0

How To Cite

Pascal Muam Mah (2022). Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre-Train Deep Learning Models. Acadlore Transactions on AI and Machine Learning, 1(2), -. https://europub.co.uk/articles/-A-731874