An AI-based chatbot using deep neural networks and LSTM was developed to process user input and assist scholars with college-related queries.
The paper proposes an AI-based chatbot using deep neural networks to assist college students with campus-related queries.
The present era of technology had a tremendous influence on society. With the advancements in the artificial intelligence and machine learning domains, machines are trying to impersonate humans. Chabot’s are gaining popularity in this aspect and have changed the patterns of conversations between humans and computers. With the creation of these virtual assistants, the conversational services have been improvised. Basically, a Chabot is a software program programmed to interact with humans using artificial intelligence in messaging platforms. Nowadays, Chabot’s are not just restricted to one field, they can be used in numerous fields in different forms. The Chabot introduced in this paper mainly concerns itself with college activities. College campuses are vast in the area. If a particular person has a query about which he wants to inquire, the individual would have to shuttle from here and there in order to gather the information for the query. So, the main intention of this research is to develop a college friendly chatterbot sooner or later, servicing the scholars. The tools used are artificial intelligence, such as natural language input and deep learning methods like deep neural networks. This uses LSTM, an extension of RNN, in order to process the user input. It trains the Chabot using these techniques and gives a response. All the data of the Chabot, which is the input given by the human, will be stored in a database and the data will be saved every time a query is asked for future use.
Chempavathy et al. (Wed,) conducted a other in College activities and queries. AI-based chatbot using deep neural networks (LSTM) was evaluated. An AI-based chatbot using deep neural networks and LSTM was developed to process user input and assist scholars with college-related queries.