Article
Automation & Control Systems
Brindaban Gohain, Rituparna Chutia, Palash Dutta
Summary: This article introduces a new distance measurement method for solving decision-making, pattern recognition, and clustering problems in an interval-valued intuitionistic fuzzy environment. The method takes into account both the optimistic viewpoint of the information and the cross-time information factors. Numerical and comparative studies have been conducted to demonstrate the superiority and applicability of the proposed method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Brindaban Gohain, Rituparna Chutia, Palash Dutta
Summary: Decision-making in uncertain conditions is challenging, and intuitionistic fuzzy sets play a crucial role in managing uncertainty. Distance measures of IFSs are used in various decision-making problems, and a new symmetric distance formula has been proposed for effectively determining the distance between information held by IFSs. The proposed measure follows axiomatic definitions of a distance measure and can be applied in diverse decision-making problems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Adeeba Umar, Ram Naresh Saraswat
Summary: Tools such as entropy, divergence measures, and similarity measures are widely applied to real-world problems in decision-making, robotics, pattern recognition, clustering, expert systems, and medical diagnosis. Picture fuzzy set (PFS) is a generalization of fuzzy set (FS) and intuitionistic fuzzy set (IFS) that shows better adaptation to various real-world problems. The development and application of a divergence measure for PFS in decision-making and machine learning have shown promising results in improving effectiveness and efficiency compared to existing methods.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Surender Singh, Abdul Haseeb Ganie
Summary: This paper investigates knowledge measure and accuracy measure in fuzzy environments, exploring their applications in pattern analysis and decision-making. It compares the proposed measures with existing methods to evaluate their effectiveness and advantages.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Koushal Singh, Surender Singh
Summary: This paper presents a new approach for defining a dual proximity measure for intuitionistic fuzzy sets, which provides values of similarity and non-similarity simultaneously and offers a comprehensive evaluation of intuitionistic fuzzy sets. The application of this measure in pattern recognition and clustering analysis is investigated, and its performance is compared with other methods in terms of structured linguistic variables, pattern classification, and clustering analysis.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Naser Valizadeh, Dariush Hayati
Summary: The study aimed to develop and validate an agricultural sustainability measurement index through a multi-stage process and statistical analysis. The index categorized indicators into factors, validated the measurement structure, and introduced main indicators. This could help guide agricultural policies, compare sustainability across regions, and facilitate interventions.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Harish Garg, Dimple Rani
Summary: The paper presents a novel distance/similarity measure among IFSs based on transformation techniques and characteristics. It proposes new distance and similarity measures based on right-angled triangles over a unit square area, and an algorithm for decision-making problems, demonstrating improved performance compared to existing measures. The reliability of the developed measure is verified through clustering and pattern recognition problems, showing successful classification results where existing measures fail.
COGNITIVE COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Surender Singh, Sonam Sharma
Summary: The text discusses the concept of fuzziness and the development of intuitionistic fuzzy theory. It proposes flexible measure of intuitionistic fuzzy entropy and generalized IF-dissimilarity measure, showcasing their superiority. The text also introduces an improved decision-making method and investigates the performance of proposed IF-dissimilarity in a pattern recognition problem, obtaining encouraging results.
Article
Computer Science, Artificial Intelligence
Juthika Mahanta, Subhasis Panda
Summary: The text discusses the research hotspot of distance measure in Pythagorean fuzzy environment, introduces a novel distance measure for PFSs, and demonstrates its superiority and reasonability through numerical examples. Applications of this measure include multi-attribute decision-making, pattern recognition, and medical diagnosis.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Lun Guo, Jianming Zhan, Zeshui Xu, Jose Carlos R. Alcantud
Summary: In this paper, a novel fuzzy large group decision making method is proposed, which utilizes three-way clustering and an adaptive exit-delegation mechanism. The method separates the edge points and outliers from the clustering results using the three-way relationships, and determines the individual weight and trust weight using a consensus measure-based model. Comparative analyses verify the feasibility and effectiveness of the proposed method.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Abdul Haseeb Ganie, Surender Singh
Summary: The study introduces a novel picture fuzzy distance measure based on direct operations on membership functions, and discusses its advantages in pattern classification problems. Conversion formulae are derived to apply the proposed method in real data sets. Additionally, a new multi-attribute decision-making method using the proposed PF distance measure is introduced and its performance is compared with classical methods in a PF environment.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Juthika Mahanta, Subhasis Panda
Summary: The distance function introduced for measuring the similarity of intuitionistic fuzzy sets has been validated for its axiomatic definition, and it exhibits boundedness and nonlinear characteristics. Its efficacy extends beyond highly uncertain scenarios and is equally effective in other cases, as demonstrated through numerical examples. The applicability of the proposed distance measure has been extended to areas such as pattern recognition and medical diagnosis.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Muhammad Riaz, Anam Habib, Muhammad Jabir Khan, Poom Kumam
Summary: This article introduces basic concepts of cubic bipolar fuzzy set and discusses the definitions, properties, as well as applications of correlation coefficients in pattern recognition and clustering analysis.
Article
Computer Science, Artificial Intelligence
Longjun Yin, Qinghua Zhang, Fan Zhao, Qiong Mou, Sidong Xian
Summary: This paper proposes a new distance measure for Pythagorean Fuzzy Sets (PFSs) based on earth mover's distance (EMD), and proves its satisfaction of distance measurement axiom. By comparing examples and applications in pattern recognition, medical diagnosis, and multi-criteria decision making, the effectiveness and practicability of the proposed method and algorithms are demonstrated.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Mathematics, Applied
W. Y. Zeng, H. S. Cui, Y. Q. Liu, Q. Yin, Z. S. Xu
Summary: This article proposes a new distance measure for intuitionistic fuzzy sets, taking into account various factors and avoiding information loss. The proposed measure is compared with existing distance measures and applied in pattern recognition, demonstrating its effectiveness and wider application scope.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Sergio Silva, Pyramo Costa, Marcio Santana, Daniel Leite
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Cristiano Garcia, Daniel Leite, Igor Skrjanc
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Automation & Control Systems
Charles Aguiar, Daniel Leite, Daniel Pereira, Goran Andonovski, Igor Skrjanc
Summary: This study presents a novel approach using fuzzy models and controllers to address key dynamics issues in crane systems, ensuring system stability and performance through the construction of an LMI feasibility problem. The method proves to be more effective than optimal quadratic controllers and can smoothly and safely move cargo to the destination.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Agriculture, Multidisciplinary
Tatiane Carvalho Alvarenga, Renato Ribeiro De Lima, Sergio Domingos Simao, Luiz Carlos Brandao Junior, Julio Silvio De Sousa Bueno Filho, Renata Ribeiro Alvarenga, Paulo Borges Rodrigues, Daniel Furtado Leite
Summary: This study proposed the use of hybrid Bayesian networks to predict the AMEn values of broiler feed, and obtained prediction equations by training on the chemical compositions. The resulting equations showed high accuracy in predicting AMEn.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Computer Science, Information Systems
Daniel Leite, Igor Skrjanc, Saso Blazic, Andrej Zdesar, Fernando Gomide
Summary: This paper introduces Interval Incremental Learning (IIL), a method for capturing spatial and temporal patterns in uncertain data streams. The method uses information granules and a granular rule base to represent the patterns. An Uncertainty-Weighted Recursive-Least-Squares (UW-RLS) method is proposed for updating local functions associated with the rules. Online recursive procedures are described for building interval-based models and guaranteeing balanced information granularity. The IIL method is aligned with the Internet of Things, Big Data processing, and eXplainable Artificial Intelligence.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Gabriella Casalino, Giovanna Castellano, Olgierd Hryniewicz, Daniel Leite, Karol Opara, Weronika Radziszewska, Katarzyna Kaczmarek-Majer
Summary: Acoustic features of speech can serve as objective markers for mental health monitoring, and semi-supervised learning methods can enhance the prediction of bipolar disorder episodes.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Leticia Decker, Daniel Leite, Daniele Bonacorsi
Summary: This study introduces a method called evolving Log Parsing (eLP) to extract information granules and a rule-based classification model from unstructured log files. The eLP method identifies templates in an unsupervised and incremental way and achieves an effective online pattern classification with 96.05% accuracy using 6 datasets. The approach also exhibits a high interpretability level of 0.04.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Leite, Fernando Gomide, Ronald Yager
Summary: The paper examines the structure of fuzzy rule based models by relating membership grades of inputs with rule outputs through a function, which is further generalized by an approach using input and output data to produce output functions of the fuzzy rules. The data driven method suggested in the paper provides an easy and efficient way to develop and process fuzzy models based on output functions constructed from level sets and input and output data, showing significant effectiveness compared to alternative modeling techniques.
2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2022)
Article
Computer Science, Software Engineering
Leticia Decker, Daniel Leite, Francesco Minarini, Simone Rossi Tisbeni, Daniele Bonacorsi
Summary: This paper introduces a general-purpose solution for anomaly detection in computer grids using unstructured, textual, and unsupervised data. The study compares different algorithms and finds that isolation forest performs the best in terms of fault detection accuracy, achieving 69.5%.
INTERNATIONAL JOURNAL OF EMBEDDED AND REAL-TIME COMMUNICATION SYSTEMS (IJERTCS)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Leite, Leticia Decker, Marcio Santana, Paulo Souza
2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Leticia Decker, Daniel Leite, Luca Giommi, Daniele Bonacorsi
2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Charles Aguiar, Daniel Leite
2020 IEEE INTERNATIONAL CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS)
(2020)
Article
Mathematics, Interdisciplinary Applications
Marcio Wladimir Santana, Daniel Furtado Leite
Article
Computer Science, Artificial Intelligence
Fabricio Lucas, Pyramo Costa, Rose Batalha, Daniel Leite, Igor Skrjanc
Article
Computer Science, Artificial Intelligence
Daniel Leite, Igor Skrjanc, Fernando Gomide