Health Care Chatbot using Natural Language Processing with SGD and ADAM Optimizer Parameter Optimization

Bandhu, Kailash Chandra; Mishra, Binod Kumar; Patel, Mohit; Choyal, Narottam; Koushal, Priya; Varathe, Prakhar

doi:10.1109/aic55036.2022.9848955

Cited by 3 publications

(2 citation statements)

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“…Another chatbot for health care was built using stochastic gradient descent (SGD) and the Adam optimizer [21]. Adam shows greater accuracy than SGD and ADAGRAD.…”

Section: Recent Workmentioning

confidence: 99%

Islamic QA with Chatbot System Using Convolutional Neural Network

Anggraini,

Tursina,

Sarno

2024

Iraqi Journal of Science

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Many questions and answers about Islamic law are scattered on the internet and have been explained repeatedly by various sites. One solution is presented by the website www.piss-ktb.com, which creates a web-based source of information in the form of Frequently Asked Questions (FAQ). However, web-based FAQs have a weakness: users still have to browse through the available questions one by one according to the questions they want to know the answers to. Browsing through thousands of FAQs is inefficient and exhausting. Thus, a chatbot system can become a better alternative to the FAQ website. Still, chatbots are difficult to use because most of their conversations are hard to understand. A single character error will cause the system to misunderstand its meaning. In reality, users expect a chatbot that can understand everyday language. Thus, it is necessary to develop a chatbot system that can understand various common sentence combinations in everyday language and understand the meaning of words. In addition, it should be able to predict answers automatically to various kinds of questions and requests, even though the initial training data is relatively low. Therefore, this study aims to develop a system that can provide answers automatically based on user commands in natural language using Global Vectors for Word Representations (GloVe), Convolutional Neural Networks (CNN), and Transfer Learning techniques. The result shows that the use of transfer learning and the Nadam optimizer can improve the system’s performance.

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“…Another chatbot for health care was built using stochastic gradient descent (SGD) and the Adam optimizer [21]. Adam shows greater accuracy than SGD and ADAGRAD.…”

Section: Recent Workmentioning

confidence: 99%

Islamic QA with Chatbot System Using Convolutional Neural Network

Anggraini,

Tursina,

Sarno

2024

Iraqi Journal of Science

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“…Chatbots utilize NLP and AI technologies to simulate human conversation [2]. They can be developed using supervised, unsupervised, or reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Smart Chatbot System for Banking using Natural Language Processing Tools

Amama,

Okengwu

2023

Preprint

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This research work focuses on creating a Smart Chatbot System for banking that leverages Natural Language Processing Toolkit and Machine Learning. The study involves gathering and preprocessing a dataset of user queries and responses. Key words are defined for pattern matching, and a decision tree algorithm is used to train the model. The Chatbot's performance is evaluated, and the results demonstrate the successful development of a functional Chatbot with a high F1 score of 0.97, indicating 97% accuracy in understanding user queries and providing relevant responses. User testing and a comparison with an existing system reveal significant improvements achieved through the incorporation of NLP and machine learning algorithms. The Chatbot is designed with an intuitive and user-friendly interface accessible through a web application, and administrators have a separate back-end access to effectively manage the knowledge base. The research also identifies areas for future improvement, such as enhancing accuracy, incorporating system memory and anaphora resolution, and integrating a logging system. Addressing these limitations will contribute to the advancement and effectiveness of conversation agents in the banking sector.

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Building a Conversational Chatbot Using Machine Learning

Solanki

Rajawat

Gadekar

et al. 2023

Advances in Medical Education, Research, and Ethics

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The healthcare industry is facing numerous challenges in providing efficient and effective care to patients, including increased demand, limited resources, and a growing shortage of healthcare providers. To address these challenges, many healthcare organizations are turning to technology, specifically artificial intelligence (AI) and machine learning (ML), to improve patient care and outcomes. In response, the development of smart chatbots has emerged as a promising solution in the healthcare field. This chapter focuses on the design and implementation of a smart chatbot using AI and ML for healthcare applications. The main goal of the chatbot is to provide a more convenient and accessible method of delivering healthcare information and services to patients. This chapter will also explore the various components and algorithms used in the design of the chatbot, as well as its potential impact on the healthcare industry. Overall, this chapter demonstrates the value of AI and ML in healthcare and encourages further exploration and development of chatbots for healthcare applications.

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Health Care Chatbot using Natural Language Processing with SGD and ADAM Optimizer Parameter Optimization

Cited by 3 publications

References 0 publications

Islamic QA with Chatbot System Using Convolutional Neural Network

Islamic QA with Chatbot System Using Convolutional Neural Network

Smart Chatbot System for Banking using Natural Language Processing Tools

Building a Conversational Chatbot Using Machine Learning

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