There is a visible eagerness in the business community to integrate chatbots with their websites and mobile apps. They provide a humanised interface to information and can serve as digital assistants that can perform tasks on behalf of an individual. There are many commercial platforms which provide interfaces to build these chatbots. They are used by both professional software developers as well as people from non-IT backgrounds. Based on our experiences with three popular chatbot-building platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex, we present a list of desirable features that these platforms should exhibit in order to cater to their mixed user base. We also rate the availability and ease of use of these features on the current versions of these platforms.

Desirable Features of a Chatbot-building Platform

In this research, we constructed a topic selection system in order to provide “topics that users are interested in and are not familiar with” from the dialogue system side. To achieve this goal, we used a concept distance to provide topics that the user is not familiar with, and improved the accuracy by removing those with a short conceptual distance. Many words in the conversation are not included in the thesaurus dictionary. We constructed a conceptual distance measurement model using machine learning so that the conceptual distance can be measured even for words that are not included in the thesaurus dictionary. As a result of the subject experiment, it was confirmed that the proposal system can be used to provide topics that the user is interested in and is not familiar with. 

On selection of topics that users are interested in but are not familiar with

The Covid-19 pandemic has brought many changes in the healthcare industry lately. As things are going normal with time, many health projects designed and used during emergencies are left unexploited. To make the perpetual use of those technologies, the current need should be taken into consideration along with necessary ideas and frameworks to evolve the existing system into. To demonstrate the same, in this paper, we have presented a package of healthcare services powered by Artificial Intelligence via a chatbot system, where a user can entertain the services either by an interactive Graphical User Interface or a conversational chatbot system. This proposed system showcases how a similar Covid-19 system can be developed into a sophisticated healthcare service. This paper emphasises adding Artificial Intelligence to any conventional software via chatbot services which would broaden the services it provides even further. In order to find out the probable best technology to integrate AI with, about 50 papers have been analysed and out of which 27 relevant papers have been included in the literature review. In future, we intend to add medical support and other intelligence-based services to our system in order to meet user requirements and essential features in the field of healthcare.

An Interactive AI-Powered Web Healthcare System

The Covid-19 pandemic has brought many changes in the healthcare industry lately. As things are going normal with time, many health projects designed and used during emergencies are left unexploited. To make the perpetual use of those technologies, the current need should be taken into consideration along with necessary ideas and frameworks to evolve the existing system into. To demonstrate the same, in this paper, we have presented a package of healthcare services powered by Artificial Intelligence via a chatbot system, where a user can entertain the services either by an interactive Graphical User Interface or a conversational chatbot system. This proposed system showcases how a similar Covid-19 system can be developed into a sophisticated healthcare service. This paper emphasises adding Artificial Intelligence to any conventional software via chatbot services which would broaden the services it provides even further. In order to find out the probable best technology to integrate AI with, about 50 papers have been analysed and out of which 27 relevant papers have been included in the literature review. In future, we intend to add medical support and other intelligence-based services to our system in order to meet user requirements and essential features in the field of healthcare. 

AbstractStroke is a second leading cause of death globally according to WHO Global Health Estimates and is listed in top 5 causes of disability globally from past few decades. Especially in developing country like India having population over 1 Billion, stroke rate is much higher than any other developing countries, and in this current pandemic situation the incident of stroke has doubled in past few years. So awareness about general population on stroke risk factors and symptoms is necessary for prevention against stroke and immediate effective treatment. The aim of this project is to assist general population of risk factor, warning symptoms of stroke, prevention and immediate effective treatment of stroke as soon as possible. To achieve this Chatbot is design using Deep Learning for the purpose of awareness, diagnostic evaluation, stroke prediction from user input data and assisting immediate measures if stroke risk prediction is positive. The main goal of this research is to look at how few popular Machine Learning algorithms function and to forecast stroke with high accuracy.KeywordsStrokeChatBotDemographic dataMachine LearningRandom Forest ClassifierDecision Tree ClassifierSupport Vector ClassifierDeep LearningNeural Network 

Stroke Awareness ChatBot Assistant with Stroke Risk Prediction Using Demographic Data

Providing a multi-modal user interface adds value to any application. Allowing users to speak or chat with the system is one such area where software practitioners are putting a lot of effort. This involves building components which can understand the nuances of human conversation. Such components, often called “chatbots, can be built either from scratch, or using a commercial platform. The process of architecting such applications may differ significantly from the “conventional” applications that the software practitioners usually build. In this work, we present a Reference Architecture for building such applications. We apply the Reference Architecture to a sample use-case and provide two Concrete Architectures for the same. The two architectures are designed keeping in mind, two commercial platforms, IBM Watson Assistant and Google DialogFlow, assuming that they were used to build the conversational components.

T.V. Prabhakar

Papers

Desirable Features of a Chatbot-building Platform

A Reference Architecture for Applications with Conversational Components