A Chatbot is a software application that replaces a live human agent to conduct a conversation via text or text to speech. It is designed to behave like a human would behave in that conversation. In this system, we demonstrate a chatbot that uses Artificial Intelligence to produce dynamic responses to online client enquiries. This web-based platform provides a vast intelligent base that can help humans to solve problems. The chatbot recognises the user's context, which prompts an intended response. Because this is a dynamic response, the user's desired response will be generated. This also uses a machine-learning algorithm to learn the chatbot by experiencing various requests and responses. Chatbots come to use in numerous fields of our daily life. Because AI enhances the human touch in every communication, chatbots are becoming increasingly robust.. It triggers accurate responses after understanding a user's query. Its objective is to reduce human dependency in every organisation and reduce the need for different systems for different processes.

Intelligent Chatbot

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

A Chatbot is software that makes an interaction between user and machine where the automatic responses will be generated. Chatbot can make the user to think that he/she is chatting with another human by using these bots. The usage of a smart phone with many applications increased. The motive of the chatbot is to generate a quick response within less time to the user’s query and reduce the workload of the management system. Every year, there is a breakthrough in the field of artificial intelligence. Bots are now utilized for communication to provide required or predetermined acknowledgment. The Chatbot should be forthright and communicative. They assist us by providing distraction, saving time, and reducing stress. These are designed to simulate the behavior of a human to engage with the real-time conversations. A remarkable customer experience can be built by using chatbots. These can be utilized by a large number of people at the same time. Our concept of considering chatbot can help students in pandemic and socially distant situations. The proposed system is developed by using Multinomial Naive Bayes algorithm and will be able to act as user side and generate responses to the user queries accurately.

Information Chatbot for College Management System Using Multinomial Naive Bayes

Chat and Voice Bot Implementation for Cardio and ENT Queries Using NLP

Abstract In the last decade, conversational agents have been developed and adopted in several application domains, including education, healthcare, finance, and tourism. Nevertheless, chatbots still need to address several limitations and challenges, especially regarding personalization, limited knowledge-sharing capabilities, multi-domain campaign support, real-time monitoring, or integration of chatbot communities. To cope with these limitations, many approaches based on multi-agent systems models and technologies have been proposed in the literature, opening new research directions in this context. To better understand the current panorama of the different chatbot technology solutions employing agent-based methods, this Systematic Literature Review investigates the different application domains, end-users, requirements, objectives, technology readiness levels, designs, strengths, limitations, and future challenges of the solutions found in this scope. The results of this review are intended to provide researchers, software engineers, and innovators with a complete overview of the current state of the art and a discussion of the open challenges. 

/pdf/exploring-agent-based-chatbots-a-systematic-literature-1404natm.pdf

Exploring agent-based chatbots: a systematic literature review

A chatbot is an interactive AI tool that tries to imitate human behavior that interprets information provided and responds accordingly in either textual or audio format Nowadays chatbots are used to efficiently carry out digital communication Our concept introduces a new field of research in chatbot communication Nowadays most chatbots efficiently complete the required task, but there is one thing to notice Many of the conversations between a human and a chatbot are repetitive Technology has always been reducing human effort and hence a multi-agent system is proposed that has a chatbot as a middleware between the user and the outside world This chatbot has something unique, ie it understands the user and their requirements It is like having an assistant who understands the user & their requirements Now since it understands the user, it has the capability of making decisions on behalf of the user, thereby reducing the efforts of a user to carry out a task Our concept of considering one task that is done with the help of a chatbot is demonstrated The proposed system adapts and acts accordingly on behalf of the user

A Self Learning Chat-Bot From User Interactions and Preferences

Students learn best when they can see the material being taught. So, instead of a traditional approach where teachers just write a sentence and it is up to students to visualize the sentence which is written, a new approach has been proposed. The system that has been proposed is, whenever a user scans the text, it will be detected and processed using Natural Language Processing (NLP). An animated scene will be created which will describe the text as appropriate as possible. This system will have a good impact on primary education. Students will be able to visualize the sentence and facts that are being taught by the teacher. The system consists of two steps. The first step is scanning the written text and converting it into actual text using OCR and then processing that text using NLP. Second step is to generate a scene which best describes the text.

Jaydeep Vaghasiya

Papers

A Self Learning Chat-Bot From User Interactions and Preferences

Generating a Scene from Text for Smart Education