RESUMEN
A chatbot is a software that produces a computer model of conversation through auditory or textual method between the user and the computer. Such bots use AI and NLP to understand user's questions and automate responses to them, simulating human conversation. In this modern technology almost, every person utilize smartphones in their day to day lives. Simply because, it has made life of a person simple by the touch of the screen, from extracting information from the internet, to provide personal assistance to entertainment everything happens just by the click of a button. Today Chatbots are used in various domains like education, customer service, medical, website help etc for seeking information, FAQ answering and guidance. Due to the advancements in AI and techniques used in data mining, healthcare chatbots are often used for medical screening. The aim of this paper is to do detailed study of various research papers related to chatbots and analyze the different tools, algorithms, software and platforms that are utilized. © 2022 IEEE.
RESUMEN
Pneumonia isa lunginfection that usually causes fever, coughing, and difficulty in breathing. Pneumonia is one of the most significant causes of death and morbidity in children under five years of age worldwide. Pneumonia is a very dangerous condition that is very difficult to diagnose at an early stage. This paper focuses on the development of a deep learning model using Convolution Neural Network for detecting Pneumonia disease from X-ray images of the Chest and improve it for efficiency and accuracy by making various hyperparameter optimizations and modifications to achieve better detection and performance accuracy. The model also uses some of the existing models by training them on the required data sets. The research focuses to develop a system that can that detect pneumonia from the chest X-ray images with an improved accuracy which can help to provide an early assistance service at places where the experts are not available easily. Also, this system can be used in the future for the detection of COVID-19 disease. © 2021 IEEE.