Article
Education & Educational Research
Kanyarat Sriwisathiyakun, Chawaporn Dhamanitayakul
Summary: This study investigated the use of digital platforms by senior citizens in Thailand and developed a chatbot to enhance their digital literacy. The chatbot was found to be easy to access and use, with comprehensive content and functionality to improve digital literacy skills. Further research will be conducted to experiment with more senior citizens and improve their digital competence in preparation for Thailand's aging society transition.
EDUCATION AND INFORMATION TECHNOLOGIES
(2022)
Review
Green & Sustainable Science & Technology
Chien-Chang Lin, Anna Y. Q. Huang, Stephen J. H. Yang
Summary: A conversational chatbot or dialogue system is a computer program that simulates conversation with human users. These chatbots can be integrated into messaging apps, mobile apps, or websites and are designed to engage in natural language conversations. Chatbots have various applications, including educational support and handling inquiries or tasks. They use AI techniques like NLP and neural networks to understand and respond to user input. The objectives, methodologies, achievements, challenges, and future trends of chatbot development will be explored in this study.
Article
Computer Science, Cybernetics
Eunjoo Jin, Matthew Eastin
Summary: The gender of healthcare providers has an impact on patients' health-related behaviors and the reliability of health information. This study found that a female doctor design cue for healthcare chatbots led to higher communication satisfaction and future intentions to use the chatbot, especially among female users. However, there was no significant difference in perceived expertise between male and female doctor design cues.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Computer Science, Information Systems
SoYeop Yoo, OkRan Jeong
Summary: The research introduces a chatbot named EP-Bot that is based on emotion and utilizes PolarisX to automatically generate knowledge graphs for better understanding user input. EP-Bot can detect emotions and dialogue acts, then generate responses that align with the emotions.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Psychology, Multidisciplinary
Kristina Machova, Martina Szaboova, Jan Paralic, Jan Micko
Summary: Emotions are an integral part of human life. Detecting human emotions using artificial intelligence can improve human-machine interaction, but fully automating emotion detection remains an unresolved issue.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Psychology, Multidisciplinary
Stefan Kopp, Nicole Kramer
Summary: The study of human-human communication and the development of computational models for human-agent communication have diverged significantly over the past decade. Despite claims of super-human performance in certain tasks, no system is currently capable of engaging in a coherent conversation with a human. The paper argues for a re-evaluation of the hallmarks of cooperative communication and the core capabilities needed for conversational agents based on research on human-human communication and psychological processes.
FRONTIERS IN PSYCHOLOGY
(2021)
Review
Public, Environmental & Occupational Health
Claudia Pernencar, Inga Saboia, Joana Carmo Dias
Summary: Digital health interventions, such as chatbot technology, have become integral parts of healthcare systems in modern societies. The use of chatbots in chronic disease management, like inflammatory bowel disease (IBD), shows potential in improving patient self-care practices. However, there is a need for strong guidelines on personalizing communication with chatbots to enhance user satisfaction and dialogue quality.
FRONTIERS IN PUBLIC HEALTH
(2022)
Review
Computer Science, Artificial Intelligence
Bei Luo, Raymond Y. K. Lau, Chunping Li, Yain-Whar Si
Summary: This review critically examines the state-of-the-art technologies and innovative applications of chatbots, identifying gaps in research and proposing new directions. It will advance both academic research and practical business applications of chatbots, providing guidance for practitioners and useful insights for researchers to explore fruitful research topics and future directions.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Review
Education & Educational Research
Mohammad Amin Kuhail, Nazik Alturki, Salwa Alramlawi, Kholood Alhejori
Summary: This study presents a systematic review of 36 papers analyzing the application of chatbots in education. The results show that chatbots are mainly used to teach computer science, language, and general education. Most of the chatbots follow a predetermined conversational path and some utilize personalized learning. Experimental evaluations demonstrate improved learning and subjective satisfaction.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Article
Psychology, Multidisciplinary
Quynh N. Nguyen, Anna Sidorova, Russell Torres
Summary: The study found that chatbot systems lead to lower levels of perceived autonomy and higher cognitive load compared to menu-based interface systems, resulting in lower user satisfaction.
COMPUTERS IN HUMAN BEHAVIOR
(2022)
Article
Computer Science, Artificial Intelligence
Anli Yan, Zhenxiang Chen, Haibo Zhang, Lizhi Peng, Qiben Yan, Muhammad Umair Hassan, Chuan Zhao, Bo Yang
Summary: Recent studies have shown that deep neural networks can accurately detect malicious traffic, but their operation remains opaque like a black box. To address this, a method to extract rules from deep neural networks for detecting malicious network traffic was proposed, showing high effectiveness in terms of accuracy, precision, recall, and F-Measure through extensive experiments.
Article
Business
Eric W. T. Ngai, Maggie C. M. Lee, Mei Luo, Patrick S. L. Chan, Tenglu Liang
Summary: This study introduces an intelligent knowledge-based conversational agent system to aid customer services in e-commerce sales. The system incorporates various emerging technologies and has been implemented in a real-world setting. Results show the system's effectiveness, while challenges and lessons learned during implementation are discussed.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Zhengwei Huang, Huayuan Liu, Jun Zhu, Jintao Min
Summary: This paper proposes a conversational sentiment analysis method based on contextual semantic and affective interaction information. By analyzing the conversations between customers and businesses, the customer sentiment can be accurately identified, which helps in accurately identifying customer needs and improving customer satisfaction.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Davide Calvaresi, Jean-Paul Calbimonte, Enrico Siboni, Stefan Eggenschwiler, Gaetano Manzo, Roger Hilfiker, Michael Schumacher
Summary: The increasing use of asynchronous messaging to support human-machine interactions through chatbots has become a common practice. However, modern chatbots often lack personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. A framework named EREBOTS has been developed to address these challenges, featuring multi-front-end connectors, multi-scenario behavior configuration, online learning, personalized conversations and recommendations, and a responsive multi-device monitoring interface. The framework has been successfully tested in physical balance preservation during social confinement, with positive feedback from participants in terms of physical improvement and interaction satisfaction.
Article
Mathematics
Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Shiti Cui, Jianfa Han, Liming Zhang, Jun Yao
Summary: Reservoir characterization is crucial for production management, but data-driven models often face the challenges of poor interpretability and limited generalizability. To address these issues, this paper proposes a knowledge interaction neural network (KINN) that combines artificial neural networks and the physical principle of the waterflooding process. The KINN model improves interpretability and prediction accuracy while maintaining robustness.
Proceedings Paper
Multidisciplinary Sciences
Somaiyeh Vedadi, Zaleha Binti Abdullah, Hoshang Kolivand, Adrian David Cheok, Baharuddin Bin Aris
ADVANCED SCIENCE LETTERS
(2018)
Article
Engineering, Multidisciplinary
Sasa Arsovski, Branko Markoski, Nikola Petrov, Sanja Stanisavljev, Mila Zakin
TEHNICKI VJESNIK-TECHNICAL GAZETTE
(2018)
Article
Computer Science, Interdisciplinary Applications
Bosede Iyiade Edwards, Kevin S. Bielawski, Rui Prada, Adrian David Cheok
Article
Computer Science, Artificial Intelligence
Sasa Arsovski, Adrian David Cheok, Kirthana Govindarajoo, Nurizzaty Salehuddin, Somaiyeh Vedadi
APPLIED INTELLIGENCE
(2020)
Article
Computer Science, Hardware & Architecture
Adrian David Cheok
IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
(2020)
Proceedings Paper
Education, Scientific Disciplines
Somaiyeh Vedadi, Zaleha Binti Abdullah, Adrian David Cheok
PROCEEDINGS OF 2019 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON)
(2019)
Proceedings Paper
Education, Scientific Disciplines
Bosede I. Edwards, Idris O. Muniru, Nosiba Khougali, Adrian D. Cheok, Rui Prada
2018 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE)
(2018)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Kevin Stanley Bielawski, Nur Ellyza Abd Rahman, Azhri Azhar, Kasun Karunanayaka, Mohammed Rabea Taleb Banalzwaa, Ibrahim Gamal Mahmoud Moteir, Adrian David Cheok
ADVANCES IN COMPUTER ENTERTAINMENT TECHNOLOGY, ACE 2017
(2018)
Article
Computer Science, Theory & Methods
Sasa Arsovski, Sze Hui Wong, Adrian David Cheok
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2018)
Article
Art
Predrag K. Nikolic, Adrian David Cheok
Article
Humanities, Multidisciplinary
Carlos Velasco, Andy T. Woods, Xiaoang Wan, Alejandro Salgado-Montejo, Cesar Bernal-Torres, Adrian D. Cheok, Charles Spence
PSYCHOLOGY OF AESTHETICS CREATIVITY AND THE ARTS
(2018)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
