Article
Computer Science, Information Systems
Lianmeng Jiao, Haoyu Yang, Zhun-ga Liu, Quan Pan
Summary: This paper introduces an interpretable fuzzy clustering algorithm that combines the flexibility of fuzzy partition with the interpretability of decision tree, using an unsupervised multi-way fuzzy decision tree to achieve interpretability in clustering.
INFORMATION SCIENCES
(2022)
Article
Engineering, Multidisciplinary
R. J. Kuo, Muhammad Naufal Alfareza, Thi Phuong Quyen Nguyen
Summary: This study proposed a clustering algorithm that combines density peak clustering and genetic algorithm (GA) to address some challenges in clustering analysis. The results showed that the proposed algorithm outperformed previous algorithms in terms of accuracy and robustness, and achieved promising results in customer segmentation for a retail company.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2023)
Article
Computer Science, Artificial Intelligence
Jung-Sik Hong, Jeongeon Lee, Min K. Sim
Summary: This study proposes a novel Concise Algorithm to effectively remove irrelevant conditions from classification rules, aiming to enhance the interpretability of machine learning models.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Metallurgy & Metallurgical Engineering
Xu Zhe, Ni Wei-chen, Ji Yue-hui
Summary: Randomness is crucial in ensemble learning, and a common practice is to rotate feature space randomly. However, this requires a large number of trees and computing resources. The MGARF algorithm proposed in this paper utilizes multimodal genetic algorithm to select diverse and accurate base learners, outperforming random forest and random rotation methods on classification datasets.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Computer Science, Artificial Intelligence
Nur Farahaina Idris, Mohd Arfian Ismail
Summary: Breast cancer is the second leading cause of death among female cancer patients worldwide, and prevention remains a challenge. Early diagnosis is crucial for preventing cancer spread and increasing survival rates. The Fuzzy-ID3 (FID3) algorithm is proposed as a classification method in breast cancer detection, combining fuzzy system and decision tree techniques.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yang Li, Wenju Zhou
Summary: This paper proposes a minimum spanning tree clustering algorithm based on fuzzy distance to address the shortcomings of the minimum spanning tree algorithm in handling unbalanced data. By introducing neighbourhood rough set theory to measure the distances between data points, and using the Prim algorithm to solve the minimum spanning tree problem, the algorithm achieves clustering by partitioning the minimum spanning tree. Experimental results show that the proposed algorithm performs well, especially in face recognition.
COGNITIVE COMPUTATION
(2022)
Article
Automation & Control Systems
Jan Rabcan, Vitaly Levashenko, Elena Zaitseva, Miroslav Kvassay
Summary: This article discusses a fuzzy classifier-based approach for EEG signal classification. The results of the study indicate that fuzzy classifiers are effective tools for EEG signal classification and achieve the highest classification accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Lucas Daniel Del Rosso Calache, Victor Claudio Bento Camargo, Lauro Osiro, Luiz Cesar Ribeiro Carpinetti
Summary: Consensus is crucial in group decision making. This study proposes a new consensus model based on dual hesitant fuzzy and evolutionary algorithm for defining unknown criteria and decision makers' weights. The proposed Genetic Algorithm is used to find decision makers' weights to achieve better consensus levels. The effectiveness of the model is verified in an application case in a steel company.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoyu Han, Xiubin Zhu, Witold Pedrycz, Zhiwu Li
Summary: This study designs a three-way classification mechanism by combining fuzzy decision trees and expressing uncertainty. A fuzzy decision tree is constructed through generalization and the three-way decision model is widely used. An efficient way to flag uncertain data is proposed, which is not possible with commonly used fuzzy decision trees. The developed mechanism consists of two stages: building a fuzzy decision tree and determining the uncertainty level to reject instances. The rejection quality is quantified in terms of accuracy and coefficient, and the mechanism performs better than other three-way decision models.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Kunal Biswas, Palaiahnakote Shivakumara, Umapada Pal, Tapabrata Chakraborti, Tong Lu, Mohamad Nizam Bin Ayub
Summary: The usage of social media has been increasing exponentially in recent years for various applications such as content sharing and entertainment. This paper proposes a new method for classifying social images based on personality traits, using fuzzy and genetic algorithms. The proposed approach extracts profile pictures and descriptions to construct vocabularies and utilizes fuzzy logic and genetic algorithms for classification. The effectiveness of the approach is demonstrated on different datasets, showing improved classification rate compared to existing methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Gonzalo Napoles, Fabian Hoitsma, Andreas Knoben, Agnieszka Jastrzebska, Maikel Leon Espinosa
Summary: This paper presents a Prolog-based reasoning module for generating counterfactual explanations based on predictions from a black-box classifier. The approach involves four stages: preprocessing the dataset, transforming numerical features into symbolic ones using fuzzy clustering, encoding instances as Prolog rules, and computing the overall confidence of each rule using fuzzy-rough set theory. Additionally, a chatbot is implemented to handle natural language queries and generate counterfactual explanations.
INFORMATION SCIENCES
(2023)
Article
Environmental Sciences
Jionghua Wang, Haowen Luo, Wenyu Li, Bo Huang
Summary: This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data, and verified on a dataset from Shenzhen, China. It was found that basic building attributes and POIs contributed most to the classification process, while crowdsensing data becomes increasingly important in more complicated classification tasks.
Article
Multidisciplinary Sciences
Zne-Jung Lee, Chou-Yuan Lee, Li-Yun Chang, Natsuki Sano
Summary: This paper discusses clustering and classification techniques based on distributed automatic feature engineering, proposing an improved fuzzy decision tree approach. By selecting valuable features and utilizing the improved decision tree classification method, the classification accuracy is enhanced.
Article
Computer Science, Information Systems
R. J. Kuo, Y. R. Zheng, Thi Phuong Quyen Nguyen
Summary: Smart devices and technology applications are widely used in various fields, leading to a rapid increase in recorded and collected information, making data analysis, specifically clustering analysis, crucial for obtaining valuable information from datasets. This study proposes a possibilistic fuzzy k-modes (PFKM) algorithm, which combines the concept of possibility with the fuzzy k-modes (FKM) algorithm to improve clustering results for categorical data by addressing outliers. Additionally, the study employs three metaheuristics – a genetic algorithm (GA), a particle swarm optimization (PSO), and the sine-cosine algorithm (SCA) – to enhance clustering performance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Chandrashekhar Azad, Bharat Bhushan, Rohit Sharma, Achyut Shankar, Krishna Kant Singh, Aditya Khamparia
Summary: The study introduced a novel prediction model, PMSGD, and through different layers of processing, it improved the accuracy and effectiveness of diabetes classification.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jie Sun, Hui Li, Pei-Chann Chang, Kai-Yu He
ENTERPRISE INFORMATION SYSTEMS
(2016)
Article
Computer Science, Artificial Intelligence
Jheng-Long Wu, Pei-Chann Chang, Cheng-Chin Tsao, Chin-Yuan Fan
APPLIED SOFT COMPUTING
(2016)
Article
Automation & Control Systems
Xiaosong Zhao, Chia-Yu Hsu, Pei-Chann Chang, Li Li
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2016)
Article
Computer Science, Artificial Intelligence
Cheng-Chin Tsao, Pei-Chann Chang, Chin-Yuan Fan, Shu-Hao Chang, Fred Phillips
Article
Engineering, Multidisciplinary
Amy J. C. Trappey, Charles V. Trappey, Chin-Yuan Fan, Abby P. T. Hsu, Xuan-Kai Li, Ian J. Y. Lee
JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
(2017)
Article
Computer Science, Artificial Intelligence
Amy J. C. Trappey, Charles V. Trappey, Chin-Yuan Fan, Ian J. Y. Lee
ADVANCED ENGINEERING INFORMATICS
(2018)
Article
Computer Science, Artificial Intelligence
Pei-Chann Chang, Jheng-Long Wu
Proceedings Paper
Telecommunications
Shu-Hao Chang, Chin-Yuan Fan
MOBILE AND WIRELESS TECHNOLOGY 2018, ICMWT 2018
(2019)
Article
Business
Shu-Hao Chang, Hsin-Yuan Chang, Chin-Yuan Fan
INTERNATIONAL JOURNAL OF INNOVATION SCIENCE
(2018)
Proceedings Paper
Engineering, Industrial
A. J. C. Trappey, C. V. Trappey, C. Y. Fan, I. J. Y. Lee
TRANSDISCIPLINARY ENGINEERING: A PARADIGM SHIFT
(2017)
Proceedings Paper
Engineering, Industrial
Chin-Yuan Fan, Shu-Hao Chang, Hsin-Yuan Chang, Sung-Shun Weng, Shan Lo
TRANSDISCIPLINARY ENGINEERING: A PARADIGM SHIFT
(2017)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Chin-Yuan Fan, Shu-Hao Chang
2016 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, MANAGEMENT SCIENCE AND APPLICATIONS (ICIMSA)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Pei-Chann Chang, Jheng-Long Wu, Cheng-Chin Tsao, Chin-Yuan Fan
DATA MINING AND BIG DATA, DMBD 2016
(2016)
Article
Business
Shu-Hao Chang, Chin -Yuan Fan
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2016)
Article
Business
Shu-Hao Chang, Chin-Yuan Fan
INTERNATIONAL JOURNAL OF INNOVATION SCIENCE
(2017)
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)
