An Overview of Chatbots using ML Algorithms in Agricultural Domain

Pravinkrishnan, K.; Balasundaram, Prabavathy; Kalinathan, Lekshmi

doi:10.5120/ijca2022922082

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“…As indicated in the figure, Pravin krishnan etal. [30] present an overview of chatbots that apply machine learning techniques in the agriculture industry. The article demonstrates the vast variety of uses for chatbots that expand beyond the sectors of technology and education.…”

Section: Literarure Reviewmentioning

confidence: 99%

Campus Companion : Creating a Supportive Chat – Assistant for Students

Bodapati

2024

IJSREM

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This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.

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Section: Literarure Reviewmentioning

confidence: 99%

Campus Companion : Creating a Supportive Chat – Assistant for Students

Bodapati

2024

IJSREM

View full text Add to dashboard Cite

show abstract

An Overview of Chatbots using ML Algorithms in Agricultural Domain

Cited by 1 publication

References 0 publications

Campus Companion : Creating a Supportive Chat – Assistant for Students

Campus Companion : Creating a Supportive Chat – Assistant for Students

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