Artificially Intelligent Health Chatbot Using Deep Learning

Bagwan, Faraz; Phalnikar, Rashmi; Desai, Sharmishta

doi:10.1109/incet51464.2021.9456195

Cited by 2 publications

(5 citation statements)

References 18 publications

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“…A Knowledge base (KB) is used to store data about patterns and responses. Question-Answer pairs are used to create a knowledge base [2]. The collected data will undergo pre-processing and cleaning for noise.…”

Section: Knowledge Base (Kb)mentioning

confidence: 99%

“…The input of rule-based chatbots is processed through a set of predefined rules [10]. Rule-based chatbots will reply only when the query matches exactly with the information stored in the knowledge base [2]. To some degree, they are built with heuristic imitation of the human ability to respond based on their memory [4].…”

Section: Rule-basedmentioning

confidence: 99%

“…It is a feedforward network, where the data flows from the input layer to the output layer without looping back [11]. It is said to be more complicated than the neural network in a way that it can analyze the data more efficiently and produce a better accuracy prediction [2].…”

Section: Deep Learningmentioning

confidence: 99%

“…A deep neural network contains three or more layers. The more layers, the deeper the network [2]. The layers are connected with all the nodes with the following layers.…”

Section: Deep Learningmentioning

confidence: 99%

“…When people are faced with even a minor laptop technical issue, they would visit the internet to study more about it because generally, people may have experienced that same error. For some people, they would prefer to talk to a real person as if they went to a physical store and talk to the worker in the electronic store [2]. Hence, when they wanted to discuss with an online qualified person, they would try to reach out to a trusted electronic service provider who provides the facility and try to chat with the person.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Designing a Chatbot-Enabled Laptop Diagnostic Assistant

Muhamad Sapiee,

Mokhtar,

Abd Rahman

et al. 2022

JSCDM

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This paper proposes a chatbot developed with deep learning techniques to help people troubleshoot operating system errors in laptops. In today's world, people can't wait for anything and expect an immediate response when they have a question because they want their problems solved quickly and completely. The system addresses the software aspect of technical laptop issues concerning a laptop's operating system. Deep learning is used to create the chatbot because it has been shown to be more accurate in selecting its response when conversing with users. The chatbot will be integrated into Telegram, an instant messaging service, and users will be able to communicate about laptop issues via Telegram.

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Section: Knowledge Base (Kb)mentioning

confidence: 99%

Section: Rule-basedmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

“…A deep neural network contains three or more layers. The more layers, the deeper the network [2]. The layers are connected with all the nodes with the following layers.…”

Section: Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Designing a Chatbot-Enabled Laptop Diagnostic Assistant

Muhamad Sapiee,

Mokhtar,

Abd Rahman

et al. 2022

JSCDM

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Advancing Chatbot Conversations: A Review of Knowledge Update Approaches

Da Costa,

Melchiades,

Girelli

et al. 2024

JBCS

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Conversational systems like chatbots have emerged as powerful tools for automating interactive tasks traditionally confined to human involvement. Fundamental to chatbot functionality is their knowledge base, the foundation of their reasoning processes. A pivotal challenge resides in chatbots' innate incapacity to seamlessly integrate changes within their knowledge base, thereby hindering their ability to provide real-time responses. The increasing literature attention dedicated to effective knowledge base updates, which we term content update, underscores the significance of this topic. This work provides an overview of content update methodologies in the context of conversational agents. We delve into the state-of-the-art approaches for natural language understanding, such as language models and alike, which are essential for turning data into knowledge. Additionally, we discuss turning point strategies and primary resources, such as deep learning, which are crucial for supporting language models. As our principal contribution, we review and discuss the core techniques underpinning information extraction as well as knowledge base representation and update in the context of conversational agents.

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Artificially Intelligent Health Chatbot Using Deep Learning

Cited by 2 publications

References 18 publications

Designing a Chatbot-Enabled Laptop Diagnostic Assistant

Designing a Chatbot-Enabled Laptop Diagnostic Assistant

Advancing Chatbot Conversations: A Review of Knowledge Update Approaches

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