Article
Computer Science, Artificial Intelligence
Yu Hu, Yan Zhu Hu, Zhong Su, Xiao Li Li, Zhen Meng, Wen Jia Tian, Yan Ying Yang, Jia Feng Chai
Summary: Formal concept analysis (FCA) is an effective tool for data analysis in software engineering and machine learning. The construction and update of concept lattice is a key step in FCA. The SsimAddExten algorithm presents a method to update the concept lattice by mapping knowledge classes on a graphic and using graphic edge structure similarity as the calculation index.
Article
Computer Science, Artificial Intelligence
Tao Zhang, Mei Rong, Haoran Shan, Mingxin Liu
Summary: This study introduces a method for analyzing the stability of an incremental concept tree (ICT) in concept cognitive learning. The method measures the similarity of ICT throughout the learning process. Numerical experiments demonstrate the effectiveness of the method and its potential for further applications in incremental concept cognitive learning.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Mengqi Chen, Rong Qu, Weiguo Fang
Summary: A case-based reasoning (CBR) system was developed for fault diagnosis of aero-engines by retrieving similar fault cases and using a highly accurate similarity measure. The system was built based on 143 cases of correctly diagnosed and resolved aero-engine faults, forming the first tentative case base in the field. The proposed similarity measure integrates three local similarity measures, including a tree-based semantic measure, to define the relationship between fault parts and fault modes. The system achieved high retrieval accuracy in real-world scenarios and shows promising potential in aiding aero-engine maintenance and support services.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Erhe Yang, Fei Hao, Yixuan Yang, Carmen De Maio, Aziz Nasridinov, Geyong Min, Laurence T. T. Yang
Summary: Knowledge graph is widely used in different fields to describe entities using RDF data. However, the increasing RDF descriptions of entities lead to information overload. In this article, the authors propose an incremental entity summarization method called IES-FCA, which leverages Formal Concept Analysis (FCA). Experimental results show that IES-FCA outperforms existing algorithms in terms of time consumption and effectiveness.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Remote Sensing
Zheng Zhao, Jianhua Chen, Kaihang Xu, Huawei Xie, Xianxia Gan, He Xu
Summary: Various machine learning methods have been applied to regional landslide risk assessment, with a novel spatial case-based reasoning method proposed in this study. By integrating spatial and attribute features, the method showed better performance compared to other models in experiments conducted in Lushan, China.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Computer Science, Artificial Intelligence
Hui Cui, Guanli Yue, Li Zou, Xin Liu, Ansheng Deng
Summary: This paper presents a linguistic reasoning algorithm based on property-oriented linguistic concept lattice combined with neural network to tackle the challenges of dealing with mass linguistic information in uncertain environment. The study involves linguistic information expression, rule extraction, and decision information prediction.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Mathematics, Interdisciplinary Applications
Shan Xiao, Cheng Di, Pei Li
Summary: With the rapid development of the information age, various social groups and institutions are generating a large amount of data every day. Traditional semantic similarity algorithms have low accuracy and poor convergence, leading to the need for improvements in impact analysis methods. The proposed new method based on language conceptual structure in this paper shows significant accuracy improvement compared to traditional algorithms in experimental simulations.
Article
Automation & Control Systems
Xiaolin Shi, Xitian Tian, Jianguo Gu, Gangfeng Wang, Dongping Zhao, Liping Ma
Summary: This paper introduces a combination of case-based reasoning (CBR) method and ontology theory for decision making in assembly sequence planning (ASP). The CBR approach allows for a unified representation of previous and target cases by utilizing the ontology that unifies different sources of assembly sequence-related knowledge. The similarity between the target ASP case and previous ASP cases is calculated based on the similarity measure of classes and properties in ontology theory, considering various factors such as connection type, motion-transmission type, and location-support type. The combination of ontology and CBR enables flexible and high-quality assembly sequence decisions, and a rule-based reasoning (RBR) method based on ontology is also used as a supplement to CBR. The effectiveness of the proposed method is validated through a reducer case.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Mathematics, Applied
L. E. Caraballo, J. M. Diaz-Banez, F. Rodriguez, V Sanchez-Canales, I Ventura
Summary: Measuring melodic similarity is crucial in music information retrieval. This paper proposes using geometric matching techniques to measure the similarity between two melodies. Music is represented as sets of points or sets of horizontal line segments in the Euclidean plane, and efficient algorithms for optimization problems inspired by two operations on melodies – linear scaling and audio compression – are proposed.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Psychology, Multidisciplinary
Igor Douven, Steven Verheyen, Shira Elqayam, Peter Gardenfors, Matias Osta-Velez
Summary: This passage discusses similarity-based inferences and the possibility of formalizing them using similarity spaces. It also presents three studies that support the proposal of measuring the strength of such inferences based on the distance in the similarity space.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Franz Baader, Oliver Fernandez Gil
Summary: This article introduces the problem of using traditional logic-based knowledge representation languages in AI applications, which can lead to complex definitions and difficult reasoning. To overcome this, the article proposes a new concept constructor and utilizes graded membership functions to define the semantics. Additionally, a class of concept measures is introduced and the algorithmic properties of the corresponding logic are analyzed.
ARTIFICIAL INTELLIGENCE
(2024)
Article
Green & Sustainable Science & Technology
Jinwu Zhan, Jiacheng Wang, Song Chen, Caisong Luo, Yalai Zhou
Summary: This paper proposes a case-based reasoning method for evaluating the adaptability of TBM tunneling, which can accurately and efficiently evaluate the adaptability of TBM. The calculation formula of TBM tunneling adaptability similarity is established based on the nearest neighbor method, and the evaluation index system for TBM tunneling adaptability is constructed. The case-based reasoning-based TBM tunneling adaptability evaluation decision system CBR-TBMEAEDS is proposed and developed.
Article
Mathematics
Huirong Zhang, Zhenyu Zhang, Lixin Zhou, Shuangsheng Wu
Summary: The article discusses how judgment debtors in China try to resist law enforcement by concealing and transferring their property, and proposes a case-based reasoning method for analyzing hidden property. The results show that this method can reduce the work pressure of law enforcement officers and improve the efficiency of handling enforcement cases.
Article
Computer Science, Artificial Intelligence
Zhuo Zhang, Xueli Xu, Fengbin Yue, Yujing Ba
Summary: In this paper, the concept lattice is applied to the path planning problem for mobile robots for the first time, and a static path planning algorithm is proposed. The given grid map is transformed into a formal context of the grid, and the locational relations between rectangular regions are mapped into partial-order relations in the graph of rectangular regions. The path planning problem on the original grid map is then transformed into a path searching problem in the graph of rectangular regions. A novel path planning algorithm is constructed, which can generate paths consisting of rectangular regions with significantly fewer inflection points compared to the A* algorithm for a four-direction mobile robot.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Remote Sensing
Jianhua Chen, Bingqian Wang, Feng Wang, Mingcai Hou, Zuowei Hu
Summary: This study proposes a new method to identify strata from oblique photogrammetric data using an SCBR model, with high overall accuracy and verification accuracy in experimental results, demonstrating the effectiveness of the proposed approach for differentiating outcropping strata.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Agriculture, Multidisciplinary
Jirapond Muangprathub, Nathaphon Boonnam, Siriwan Kajornkasirat, Narongsak Lekbangpong, Apirat Wanichsombat, Pichetwut Nillaor
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2019)
Article
Computer Science, Artificial Intelligence
Jirapond Muangprathub, Siriwan Kajornkasirat, Apirat Wanichsombat, Veera Boonjing, Jarunee Saelee, Arthit Intarasit
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
(2019)
Article
Telecommunications
Jirapond Muangprathub, Arthit Intarasit, Laor Boongasame, Nattakarn Phaphoom
WIRELESS PERSONAL COMMUNICATIONS
(2020)
Article
Environmental Sciences
Jirapond Muangprathub, Anirut Sriwichian, Apirat Wanichsombat, Siriwan Kajornkasirat, Pichetwut Nillaor, Veera Boonjing
Summary: The paper proposes a new elderly tracking system utilizing multiple technologies combined with machine learning, which has been tested and found to effectively monitor elderly activities and provide real-time alerts and emergency assistance. Through collaboration with local agencies, the system also provides comprehensive support and information related to elderly care.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Green & Sustainable Science & Technology
Nathaphon Boonnam, Tanatpong Udomchaipitak, Supattra Puttinaovarat, Thanapong Chaichana, Veera Boonjing, Jirapond Muangprathub
Summary: This study modeled and predicted coral reef bleaching under climate change using machine learning techniques to provide data to support coral reef protection. Supervised and unsupervised machine learning were applied to predict coral damage levels and analyze relationships among bleaching factors.
Article
Green & Sustainable Science & Technology
Pichetwut Nillaor, Anirut Sriwichian, Apirat Wanichsombat, Siriwan Kajornkasirat, Veera Boonjing, Jirapond Muangprathub
Summary: Understanding the context of the elderly is crucial for improving their quality of life. In Thailand, the lack of centralized data collection among elderly care organizations negatively impacts government monitoring. This study proposes a central database system for elderly care, facilitating data collection, management, analysis, and visualization. A case study involving 240 elderly individuals shows that this model accurately predicts quality of life using only 14 out of 39 available factors. This system has implications for efficient data collection, planning, and policy setting in elderly care.
Proceedings Paper
Computer Science, Software Engineering
Narongsak Lekbangpong, Theera Srisawat, Apirat Wanichsombat, Jirapond Muangprathub
2019 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE 2019)
(2019)
Proceedings Paper
Computer Science, Software Engineering
Anirut Sriwichian, Veera Boonjing, Pichetwut Nillaor, Jirapond Muangprathub
2019 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE 2019)
(2019)
Article
Public, Environmental & Occupational Health
Siriwan Kajornkasirat, Jirapond Muangprathub, Nathaphon Boonnam
IRANIAN JOURNAL OF PUBLIC HEALTH
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Weenawadee Muangon, Jirapond Muangprathub, Jarunee Saelee, Tasanawan Soonklang, Sunee Pongpinigpinyo, Karanya Sitdhisanguan
ICIME 2018: PROCEEDINGS OF THE 2018 10TH INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND ENGINEERING
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Siriwan Kajornkasirat, Jirapond Muangprathub, Naphatsawat Rachpibool, Nitikorn Phomnui
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2018, PT I
(2018)
Article
Computer Science, Artificial Intelligence
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Summary: Deep reinforcement learning has significant potential in industrial decision-making, but its lack of interpretability poses challenges for safety-critical systems. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, addressing these challenges and achieving excellent results in predictive maintenance for turbofan engines.
DATA & KNOWLEDGE ENGINEERING
(2024)
Article
Computer Science, Artificial Intelligence
Tongzhao Xu, Turdi Tohti, Askar Hamdulla
Summary: This paper proposes a multi-hop KGQA model that combines global and item-by-item reasoning fusion. It introduces a convolutional attention reasoning mechanism and serial prediction of relations to form reasoning paths, effectively addressing the issues of ignoring intermediate path reasoning and information interaction. The proposed model achieves significant accuracy improvement on three datasets.
DATA & KNOWLEDGE ENGINEERING
(2024)
Article
Computer Science, Artificial Intelligence
Ilias Dimitriadis, George Dialektakis, Athena Vakali
Summary: The high growth of Online Social Networks (OSNs) has led to the emergence of social bots, which pose high-level security threats. This paper proposes an adaptive bot detection framework called CALEB based on CGAN and AC-GAN, which can simulate bot evolution and enhance detection performance. Experimental results show that the proposed approach outperforms previous methods in detecting new unseen bots.
DATA & KNOWLEDGE ENGINEERING
(2024)
