Abarna R

Bio: Abarna R is an academic researcher. The author has contributed to research in topics: Chatbot. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Chatbot and bullyfree Chat

Abstract: In this technology world, a recent technology called chatbot which have been in demand and usage for every business purpose and have hit the market.Chatbots is an interaction between person and bot which gives us a efficient service and it also gives the way to develop customer engagement and efficiency by reduction of cost by using these service.Chatbots can be accessible at anytime,which can handle capacity that is chatbot can chat with thousands of people at a time,It has a flexible attribute as well as customer satisfaction. A chatbot is constructed using natural language processing with the help of machine learning algorithm for training the bot and to make up the bot to perform in a right way and so training and testing is done using ML.This paper gives an overview of chatbot and challenges we faced behind the chatbot with extra features of images.

OggyBug: A Test Automation Tool in Chatbots

Abstract: Context: Motivated by the reduction in operating costs, the use of chatbots to automate customer service has been growing. Chatbots have evolved a lot in terms of technologies used as well as in the different application areas. Problem: As it is a recent technology, there is no tool offers to support chatbot test automation with the possibility of testing context information that happens in the dialogue between the chatbot and the human; the existing tools also lack facilities to integrate different data sources that are used during the tests. Objective: Propose and evaluate a new framework for chatbot testing that considers context information and allows the integration test between different data sources. Method: from the analysis of the lack of existing works reported in the literature, a framework for self-testing of chatbots called OggyBug was proposed, which was used by two chatbots development teams that provided feedback on their use. Results: Construction of the framework called OggyBug that allows implementing, manage and report the results of the execution of automatic tests for chatbots, either through an API or through a web interface, with ease of integrating different sources of information within the automation scripts. After collecting the feedback from the teams that used the framework, we can observe the ease in defining scenarios and repeating the execution of the tests. Conclusion: Testing in context information proved to be important to verify or define the information of the conversation session. The configuration of integration tests proved to be complex, due to the need to configure web services in the chatbot's actions.

Customer Support Chatbot Using Machine Learning.

Abstract: In customer support, chatbot by using machine learning customer can converse by a chatbot and acquire the query intent information. With the enhancement of globalization and industrialization, it becomes a problem for enterprises to interact with the customer and listen to their difficulties to a big extent. Chatbots make ease the pain that the industries nowadays facing. The aim of this chatbot is to support and reply to the client by giving him/her the relevant intent depending on the query request from the customers.

Natural Language Processing based Human Assistive Health Conversational Agent for Multi-Users

Abstract: Background: Most of the people are not medically qualified for studying or understanding the extremity of their diseases or symptoms. This is the place where natural language processing plays a vital role in healthcare. These chatbots collect patients' health data and depending on the data, these chatbot give more relevant data to patients regarding their body conditions and recommending further steps also. Purposes: In the medical field, AI powered healthcare chatbots are beneficial for assisting patients and guiding them in getting the most relevant assistance. Chatbots are more useful for online search that users or patients go through when patients want to know for their health symptoms. Methods: In this study, the health assistant system was developed using Dialogflow application programming interface (API) which is a Google's Natural language processing powered algorithm and the same is deployed on google assistant, telegram, slack, Facebook messenger, and website and mobile app. With this web application, a user can make health requests/queries via text message and might also get relevant health suggestions/recommendations through it. Results: This chatbot acts like an informative and conversational chatbot. This chatbot provides medical knowledge such as disease symptoms and treatments. Storing patients personal and medical information in a database for further analysis of the patients and patients get real time suggestions from doctors. Conclusion: In the healthcare sector AI-powered applications have seen a remarkable spike in recent days. This covid crisis changed the whole healthcare system upside down. So this NLP powered chatbot system reduced office waiting, saving money, time and energy. Patients might be getting medical knowledge and assisting ourselves within their own time and place.

Building a Chatbot System to Analyze Opinions of English Comments

Abstract: Chatbot research has advanced significantly over the years. Enterprises have been investigating how to improve these tools’ performance, adoption, and implementation to communicate with customers or internal teams through social media. Besides, businesses also want to pay attention to quality reviews from customers via social networks about products available in the market. From there, please select a new method to improve the service quality of their products and then send it to publishing agencies to publish based on the needs and evaluation of society. Although there have been numerous recent studies, not all of them address the issue of opinion evaluation on the chatbot system. The primary goal of this paper’s research is to evaluate human comments in English via the chatbot system. The system’s documents are preprocessed and opinion-matched to provide opinion judgments based on English comments. Based on practical needs and social conditions, this methodology aims to evolve chatbot content based on user inter-actions, allowing for a cyclic and human-supervised process with the following steps to evaluate comments in English. First, we preprocess the input data by collecting social media comments, and then our system parses those comments according to the rating views for each topic covered. Finally, our system will give a rating and comment result for each comment entered into the system. Experiments show that our method can improve accuracy better than the referenced methods by 78.53%.

GuidoBot: A Novel Approach for Assisting Faculty in Tracking Goals and finding tasks

Abstract: In the world of Artificial Intelligence, many inventions facilitate human works, and one such innovation is a Chabot. A Window-based Guido Bot is developed as a guide that helps faculty with goal tracking and in managing daily tasks effectively. This Chabot is designed with three different components like the Profile Maintenance module, Goal Tracking module, and Task Finding module. Profile maintenance module deals with the academic profile, status of research publications of faculty. And an important module is a Goal Tracking module that helps faculty to achieve their academic goals, research goals with timely updates and notifications. And the Task Finding module helps faculty in assigning and tracking the status of the task with daily updates of pending tasks along with their deadlines. Here, a web application is developed as an interface for admins (Management) to monitor and issue goals, and administrative tasks for faculty. And the Guido Bot uses this interface as a data source for efficient performance.