Conversational AI: Chatbots

Meshram, Siddhant; Naik, Namit; Vr, Megha; More, Tanmay; Kharche, Shubhangi

doi:10.1109/conit51480.2021.9498508

Cited by 34 publications

(6 citation statements)

References 18 publications

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“…The Chatbot can initially start with the rule-based mode for straightforward questions (frequent questions) [31]. However, the rule-based primarily based totally competencies each in know-how and responding to textual content activities are restricted typically to the scope of the script, even though they may be programmed to reply to particular keywords [32]. Therefore, using conversational AI and rule-based Chatbots can attain a lot of extra advantages because of their better ranges of sophistication.…”

Section: Common Chatbot Operation Processmentioning

confidence: 99%

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Majeed,

Hardan,

Hardan

et al. 2024

RIA

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Early diagnosis of heart disorders is an essential issue since they might be life-threatening conditions that call for urgent medical care. Chatbots as a user-friendly technology can be used to deliver general assistance and information anywhere anytime. However, the decision-making accuracy is one of the key challenges of Chatbots. In this paper, a medical Chatbot is developed for the purpose of early cardiac disease diagnosis based on given symptoms. An approved dataset known as the Cleveland Heart Disease dataset is utilized in this work. Hence, for accurate decision-making, three machine learning algorithms Support Vector Machine (SVM), and Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) are compared and tested to nominate the best algorithms for heart health disorders detection. Three simulation scenarios are applied to test the performance of each algorithm using first all features of the dataset, then the k-best feature selection method, and the application of Grid Search (GS) optimization algorithms. The performance evaluation shows that the prediction accuracy of the proposed XGBoost algorithm outweighs other algorithms.

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Section: Common Chatbot Operation Processmentioning

confidence: 99%

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Majeed,

Hardan,

Hardan

et al. 2024

RIA

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“…Different types of chatbot have been discussed base, such as knowledge domain, human aid, build methods, and generation methods. There are two types of chatbots: rule-based and machine learning-based chatbots [7]. In the research presented in [8] the authors give a detailed overview of existing chatbots that are used in educational fields, and the result shows that the chatbots being used now have the potential impact on students' learning in different ways.…”

Section: Related Workmentioning

confidence: 99%

Empathy in AI: Developing a Sentiment-Sensitive Chatbot through Advanced Natural Language Processing

2024

IJATCSE

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This paper explores the development of an empathetic chatbot that utilises advanced natural language processing (NLP) techniques for accurate emotion detection and response generation. This paper also explores the impact of the use of a hybrid model that recognises and categorises emotional states from textual input. Using generation models like DialoGPT, fine-tuned with empathetic dialogue datasets. The proposed chatbot‘s architect performance was evaluated using various metrics, and the results show advantage over existing models.

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“…The utilization of chatbots in developing countries has been observed in the realm of business operations and the enhancement of organizational capabilities [151]. The potential of chatbot applications extends to various contexts, including corporate settings [174], where they facilitate the acquisition of skills related to the utilization of novel systems by employees [242]. This, in turn, contributes to advancements in domains such as accounting, human resources, sales, procurement, and logistics [200].…”

Section: Chatbot For Business and Organizationmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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Conversational AI: Chatbots

Cited by 34 publications

References 18 publications

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Empathy in AI: Developing a Sentiment-Sensitive Chatbot through Advanced Natural Language Processing

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

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