Xiaoying Chen^1,*, Jie Kang¹, Cong Hu²

Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833

Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements.… More >

Batyrkhan Omarov^1,*, Sergazi Narynov², Zhandos Zhumanov²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5105-5122, 2023, DOI:10.32604/cmc.2023.034655

Abstract Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners… More >

Thi Thanh Sang Nguyen^*, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 601-616, 2023, DOI:10.32604/iasc.2023.032080

Abstract Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot. A… More >

V. P. Harshini Raji^*, P. Uma Maheswari

International Journal of Mental Health Promotion, Vol.24, No.2, pp. 189-205, 2022, DOI:10.32604/ijmhp.2022.011865

Abstract India imposed the largest lockdown in the world in response to fight the spread of the Novel Coronavirus disease (COVID-19) from 19 March till 31 May 2020. The onset of the pandemic left the general public feeling psychosocially distressed, helpless, and anxious. The researcher developed a Messenger supported Chatbot, based on the broaden and build model, to cater to the healthy general public to promote positivity and mental well-being. 31 participants between 22 and 45 years old consensually took a pre-test, Chatbot intervention, and post-test. The Chatbot provided guided activities out of which positive affirmations, meditation, and exercises were mostly… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3937-3952, 2022, DOI:10.32604/cmc.2022.023127

Abstract Artificial intelligent based dialog systems are getting attention from both business and academic communities. The key parts for such intelligent chatbot systems are domain classification, intent detection, and named entity recognition. Various supervised, unsupervised, and hybrid approaches are used to detect each field. Such intelligent systems, also called natural language understanding systems analyze user requests in sequential order: domain classification, intent, and entity recognition based on the semantic rules of the classified domain. This sequential approach propagates the downstream error; i.e., if the domain classification model fails to classify the domain, intent and entity recognition fail. Furthermore, training such intelligent… More >

Anurag Jain¹, Kusum Yadav², Hadeel Fahad Alharbi², Shamik Tiwari^1,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 423-438, 2022, DOI:10.32604/cmc.2022.020238

Abstract Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19… More >

Mukhtar Ghaleb^1,*, Yahya Almurtadha², Fahad Algarni³, Monir Abdullah³, Emad Felemban⁴, Ali M. Alsharafi³, Mohamed Othman⁵, Khaled Ghilan⁶

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2619-2638, 2022, DOI:10.32604/cmc.2022.020358

Abstract People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 1-11, 2022, DOI:10.32604/iasc.2022.018757

Abstract Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent… More >

SoYeop Yoo, OkRan Jeong^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2807-2817, 2021, DOI:10.32604/cmc.2021.015634

Abstract People occasionally interact with each other through conversation. In particular, we communicate through dialogue and exchange emotions and information from it. Emotions are essential characteristics of natural language. Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans. For a computer to interact like a human being, it must understand the emotions inherent in the conversation and generate the appropriate responses. However, existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language, excluding emotions. We propose a chatbot based on emotion, which is an essential… More >