Developing a Chatbot Using Machine Learning
04 Aug 2020-Vol. 3, Iss: 8, pp 40-43
TL;DR: This project developed a chatbot using machine learning which helps to give information about the authors' college and it will give response for the query given by the user and it also capable of executing tasks.
Abstract: Now-a-days development of chatbot using different methods become trendier, till now many conversational chatbots were designed for the replacement of traditional chatbots. A chatbot is a software that is capable of communicating and performing actions like the way human do. Chatbot will give response for the query given by the user and it also capable of executing tasks. Chatbots developed in olden days are so difficult to perform task but chatbot developed in recent years are good in performance and its development also become easier because of wide availability of development platforms and wide availability of source code. There are many methods to develop chatbot, it can be developed using either natural language processing (NLP) or deep learning. In this project we developed a chatbot using machine learning which helps to give information about our college.
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02 Aug 2021
TL;DR: In this article, the authors explore the feasibility of implementing a chatbot for an interview process and provide the design of a chat bot to conduct an interview, as well as processing the interview result by using Artificial Intelligence (AI) or machine learning.
Abstract: This study aims to explore the feasibility of implementing a chatbot for an interview process. The development of chatbots evolved rapidly to efficiently collect information in numerous fields, including customer service, health care, and etc. However, there was limited discussion of how chatbots are used to conduct an interview process autonomously. A human-driven interview also has some major limitations, e.g., it may only be conducted on a small-scale and is susceptible to bias. Hence, this study provides the design of a chatbot to conduct an interview, as well as processing the interview result by using Artificial Intelligence (AI) or machine learning. We have identified the difference between the typical chatbot communication method and the interview bot. This finding can be an opportunity to make a new interview bot or improve the implementation of a chatbot. In the end, we also discuss the challenges and benefits of the development of an interview bot.
5 citations
28 Apr 2021
TL;DR: In this paper, a rule based chatbot on a platform named Discord, also showcased that how a chatbot can be integrated into other online platforms to counter the challenges faced in teaching, where Q/A features are used to get in-depth knowledge about various preinstalled data in chatbot.
Abstract: Nowadays, educators can show case the technology of chatbots in various fields such as teaching and learning. Earlier the resources are almost negligible, in the field of education with the learning design integrated in it. But now with the advancement of technology, chatbot can fill the gap in the teaching landscape too. Using chatbot in the education domain is reassuring, since these bots can point out some of the logistics and diversified issues that a normal class might face. Besides this, with the further advancement of Artificial Intelligence, tech-giants like Apple, Google, and Amazon are providing platforms where building conversation is more focused rather than technicalities of computer programming i.e., to be more specific in natural language processing. In this paper, we have built a rule based chatbot on a platform named Discord, also showcased that how a chatbot can be integrated into other online platforms to counter the challenges faced in teaching. Q/A features are used to get in-depth knowledge about various preinstalled data in chatbot.
2 citations
TL;DR: In this article , the authors examined chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain.
Abstract: The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain. The study examined three points of view. The first point of view traces the technological evolution of chatbots. The second point of view reports the fields of application of the chatbots, giving space to the expectations of use and the expected benefits from a cross-domain point of view, also affecting the health domain. The third and main point of view is that of the analysis of the state of use of chatbots in the health domain based on the scientific literature represented by systematic reviews. The overview identified the topics of greatest interest with the opportunities. The analysis revealed the need for initiatives that simultaneously evaluate multiple domains all together in a synergistic way. Concerted efforts to achieve this are recommended. It is also believed to monitor both the process of osmosis between other sectors and the health domain, as well as the chatbots that can create psychological and behavioural problems with an impact on the health domain.
2 citations
23 Aug 2022
TL;DR: A generative model for an automatic question answering system (chatbot) in the Indonesian language with domain Indonesia JKN-KIS is developed and the results show that the chatbot able to solved the problem with a large and varied Indonesia dataset without certain rules.
Abstract: Jaminan Kesehatan Nasional- Kartu Indonesia Sehat (JKN-KIS) is a government program for the Ministry of Health Indonesia. Information of all the participant is the key to the sustainability of the JKN-KIS program. Lack of socialization and reading guidebooks desire are the main reasons why JKN-KIS services do not run properly and has many problem in the community. A question answering system (chatbot) has many benefits because it can automatically responds to questions and help the government with public communication. Unfortunately, most chatbot services today do not offer a completely automated solution. There are lots of chatbots out still static, so the resulting response must comply with certain rules. That chatbot is not scalable and less effective when applied to large JKN-KIS data. However, this research develops a generative model for an automatic question answering system (chatbot) in the Indonesian language with domain Indonesia JKN-KIS. The generative chatbot model is built with the Sequence to Sequence (Seq2Seq) model, encoder and decoder, with the LSTM Multiplicative Attention method. The dataset used is question and answer pairs from JKN-KIS guidebook. The results show that the chatbot able to solved the problem with a large and varied Indonesia dataset without certain rules. With a 20 thousand question and answer conversation pairs dataset, this research get the best chatbot results with the parameter at 6000 iterations for 15 batch sizes and 1000 hidden size architectures. The results show that the resulting loss value is 0.23 with a BLEU Score of unigram 0.86 and a BLEU Score of bigrams 0.85.
1 citations
TL;DR: In this paper , the authors explored the cybersecurity attacks and vulnerabilities in chatbots, specifically in the context of creating the malware code, phishing emails, undetectable zero-day attacks, and generation of macros and LOLBINs.
Abstract: Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity threats and vulnerabilities. This paper discussed the relevant literature, reports, and explanatory incident attacks generated against chatbots. Our initial point is to explore the timeline of chatbots from ELIZA (an early natural language processing computer program) to GPT-4 and provide the working mechanism of ChatGPT. Subsequently, we explored the cybersecurity attacks and vulnerabilities in chatbots. Besides, we investigated the ChatGPT, specifically in the context of creating the malware code, phishing emails, undetectable zero-day attacks, and generation of macros and LOLBINs. Furthermore, the history of cyberattacks and vulnerabilities exploited by cybercriminals are discussed, particularly considering the risk and vulnerabilities in ChatGPT. Addressing these threats and vulnerabilities requires specific strategies and measures to reduce the harmful consequences. Therefore, the future directions to address the challenges were presented.