G Krishna Vamsi

Bio: G Krishna Vamsi is an academic researcher from Maulana Azad National Institute of Technology. The author has contributed to research in topics: Deep learning & Dialog system. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Chatbot: A Deep Neural Network Based Human to Machine Conversation Model

Abstract: A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. The recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. This paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data.

Agroxpert - Farmer Assistant

Abstract: Agriculture occupies an important position in the Indian economy. Indian farmers today are facing the problem of low income due to the lack of information about government schemes, fertilizers, farming equipment etc. Some smallholders and marginalized farmers have low awareness as most of them live in remote areas and don't have access to information about soil properties, seeds, recently used tools, fertilizers, etc. The document proposes an intelligent, portable system that uses natural language processing methods to help farmers use different farming methods, and further help them decide which crops give the highest yield and productivity. To meet all the requirements of farmers, a chatbot is proposed using natural language processing technology. The system will act as an interactive virtual assistant for farmers, answering all queries related to agriculture. This paper will go through the implementation of the chatbot using the chatterbot libraries and Django framework.

Chatbot Using Deep Learning

Abstract: In this paper we have proposed the working of Assistant conversational agent(Chatbot) using deep learning concepts with the utilization of tensorflow library. The LSTM is used overhere, so that the input taken with more than 30-40 words in a sentence canbe replied or answerd with more adequate conversation. The movie dataset used to train the model is taken from Cornell. The model isdesigned to perform a movie dialogue prediction conversation between the user and chatbot. The main aim is to increase the accuracy and estimation of the model. In the proposed model, we have developed a Seq2Seq AI Chatbot with attention mechanism using LSTM and libraries like tenserflow.

An Ontology-Based Chatbot to Enhance Experiential Learning in a Cultural Heritage Scenario

Abstract: Italy is rich in cultural attractions, many known worldwide, others more hidden and unrecognized. Cultural attractions include tangible cultural assets (works of art, archaeological excavations, and churches) and intangible ones (music, poetry, and art). Today, given the pervasive diffusion of “smart” devices, the intelligent use of modern technologies could play a crucial role in changing the habit of consulting and visiting cultural heritage mainly with traditional methodologies, making little or no use of the advantages coming from the more and more availability of digitalized resources. A realm of particular interest is “experiential learning” when applied to cultural heritage, where tourists more and more ask to be helped in discovering the richness of sites they explore. In this article, we will present an innovative chatbot-based system, called HeriBot, that supports experiential tourism. Our system has been developed and experimented with a research effort for applying ICT technologies to enhance the knowledge, valorization, and sustainable fruition of the Cultural Heritage related to the Archaeological Urban Park of Naples (PAUN—Parco Archeologico Urbano di Napoli). Our article starts exploiting the ontological approach based on a purpose ontology describing the Park Heritage. Using such an ontology, we designed a chatbot that can identify the specific characteristics and motivations of the tourist, defining language, tone, and visitable scenarios and, through the ontology, allows the visit to be transformed into a personalized educational opportunity. The system has been validated in terms of dialogue effectiveness and training efficiency by a panel of experts, and we present and discuss obtained results.

Robot Chat System (Chatbot) to Help Users “Homelab” based in Deep Learning

Abstract: Homelab is a discussion platform on course materials and assignments for students and is packed in an Android application product and website. The Homelab website is built using Laravel. For Android-based Homelab application development, a special Application Programming Interface (API) with JWT security is made in this research. In Homelab, besides the question and answer feature, a virtual conversation agent (chatbot) based on deep learning with a retrieval model that uses multilayer perceptron and a special text dataset for conversations about Homelab products is also created. The virtual conversation agent at Homelab is made by utilizing the Sastrawi library and natural language processing to facilitate the processing of user messages in Indonesian. The output of this research is the response from the chatbot and the probability value from the classification results of the available response classes. The system made has an accuracy rate of 96.43 percent with an average processing time of 0.3 seconds to get a response.