Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: This study uses a multi-granular fuzzy linguistic model to evaluate the different features of health care systems and recommend hospitals based on user preferences. By assessing the opinions of real hospitals, the results of this approach outperform other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Multidisciplinary Sciences
Muhammad Qiyas, Muhammad Naeem, Lazim Abdullah, Muhammad Riaz, Neelam Khan
Summary: In this research, tools are provided to overcome information loss limitation in estimating results in the discrete initial expression domain. 2-tuples, consisting of a linguistic term and a numerical value calculated between [0.5,0.5), are used to express linguistic information. A novel linguistic multi-attribute group decision-making (MAGDM) approach with complex fractional orthotriple fuzzy 2-tuple linguistic (CFOF2TL) assessment details is developed.
Article
Mathematics, Applied
Muhammad Akram, Uzma Noreen, Mohammed M. Ali Al-Shamiri, Dragan Pamucar
Summary: This paper explores the strategic application of TOPSIS and ELECTRE-I methods in the 2-tuple linguistic m-polar fuzzy sets (2TLmFSs) context. The 2TLmF-TOPSIS method ranks alternatives using a relative closeness index, while the 2TLmF ELECTRE-I method selects the best alternative through an outranking decision graph. The structure of the proposed techniques is illustrated using a system flow diagram. Finally, two case studies demonstrate the correctness, transparency, and effectiveness of the proposed methods.
Article
Computer Science, Hardware & Architecture
Yuan Zhong, Guofa Li, Chuanhai Chen, Tongtong Jin, Yan Liu
Summary: This article proposes a reliability allocation method for the initial stage of product design based on the 2-tuple linguistic weighted Muirhead mean operator and 2-tuple linguistic best-worst method. It solves the problems of poor rationality of data use, difficulty of calculation using existing methods, low accuracy of results, and weak ability of experts to express and process fuzzy information.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Mathematical & Computational Biology
Muhammad Akram, Usman Ali, Gustavo Santos-Garcia, Zohra Niaz
Summary: Manufacturing plants generate toxic waste that can be harmful to workers, the population, and the atmosphere. The selection of solid waste disposal locations for manufacturing plants is a growing challenge in many countries. The research paper introduces a WASPAS method using 2-tuple linguistic Fermatean fuzzy set for the problem, which is based on simple and sound mathematics and can be applied to any decision-making problem.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Mathematics, Applied
R. Verma, A. Aggarwal
Summary: Experts prefer expressing their views in natural language rather than precise numerical form in decision-making problems. Linguistic representation models are widely used for solving problems with qualitative information, and game theory has successful applications in various areas. This paper extensively studies matrix games with qualitative payoffs, using 2-tuple intuitionistic fuzzy linguistic values for representation and developing mathematical solutions through linear/nonlinear programming problems.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Sundas Shahzadi, Rabia Bibi, Gustavo Santos-Garcia
Summary: The primary objective of this research article is to present two novel tactical approaches, 2-tuple linguistic Fermatean fuzzy TOPSIS (2TLFF-TOPSIS) and 2-tuple linguistic Fermatean fuzzy ELECTRE I (2TLFF-ELECTRE I), for multi-attribute group decision-making based on 2-tuple linguistic Fermatean fuzzy data. The proposed algorithm exploits the benefits of novel 2-tuple linguistic Fermatean averaging operator to combine the insightful viewpoints of decision-making experts. These methods demonstrate their practicality and application through numerical examples and comparative analysis.
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Uzma Noreen, Muhammet Deveci
Summary: The core objective of this article is to introduce a new multi-criteria group decision-making approach that combines the features of 2-tuple linguistic m-polar fuzzy sets with the accuracy of ELECTRE II. The resulting technique outlines the framework and construction of ELECTRE II model for problems with 2TLmF uncertainties. The approach is validated through selecting SUVs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Tanya Malhotra, Anjana Gupta
Summary: This article proposes a method to deal with unbalanced linguistic terms by using a specific algorithm and a 2-tuple model, aiming to assist experts in addressing difficulties in problem evaluation.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Feifei Jin, Shuyan Guo, Yuhang Cai, Jinpei Liu, Ligang Zhou
Summary: This paper proposes a 2-tuple linguistic decision-making method that incorporates a consistency adjustment algorithm and a 2-tuple linguistic data envelopment analysis model. It aims to retain the decision makers' initial preference information and improve the consistency and weight generation for alternatives with 2-TLPRs.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Uzma Noreen, Dragan Pamucar
Summary: The article introduces the PROMETHEE method and its application in multi-attribute decision making. A new approach of the PROMETHEE method, combining multi-polarity with linguistic information, is proposed. The method generates a preference ranking of alternatives by utilizing positive and negative outranking flows.
Article
Mathematics, Applied
Muhammad Akram, Sumera Naz, Gustavo Santos-Garcia, Muhammad Ramzan Saeed
Summary: This article introduces the recent addition of spherical fuzzy sets theory and its application in production design. It proposes a novel decision-making method utilizing 2-tuple linguistic T-spherical fuzzy numbers for selecting the best alternative in manufacturing a linear delta robot. The research also develops innovative operational rules and operators to address the problems of multi-attribute group decision-making environment. The suggested methodology provides an information-based approach for making rational decisions and preventing data loss.
Article
Computer Science, Artificial Intelligence
Ayesha Khan, Uzma Ahmad, Sundas Shahzadi
Summary: The primary goal of this study is to investigate and improve the ITARA and VIKOR techniques by using 2-tuple linguistic q-rung picture fuzzy sets. The VIKOR technique evaluates options based on their potential for group benefit and individual opponent regret. The ITARA method is expanded to accommodate 2-tuple linguistic q-rung picture fuzzy sets, and then combined with VIKOR to create the innovative approach known as 2TLq-RPF-ITARA-VIKOR.
Article
Computer Science, Interdisciplinary Applications
Alvaro Labella, Bapi Dutta, Luis Martinez
Summary: Multi-criteria group decision making (MCGDM) involves evaluating alternatives over several criteria, which has given rise to large-scale group decision making (LS-GDM) problems with challenges such as group formation and opinion polarization. Using linguistic variables and computing with words (CW) processes has been successful in addressing uncertainty in real world MCGDM problems. The Best-Worst method (BWM) reduces pairwise comparisons and inconsistency in decision makers' preferences, while an extended 2-tuple BWM aims to provide accurate and interpretable results in MCGDM scenarios, including polarization opinions and sub-group identification.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Mathematical & Computational Biology
Sumera Naz, Muhammad Akram, Mohammed M. Ali Al-Shamiri, Mohammed M. Khalaf, Gohar Yousaf
Summary: This article introduces the 2-tuple linguistic bipolar fuzzy set (2TLBFS), a new strategy for dealing with uncertainty in decision-making. It presents operational rules and fusion operators for 2TLBF numbers, as well as integrates the Heronian mean operator to analyze the correlation between decision factors. Furthermore, it develops an approach for multi-attribute group decision-making based on the proposed aggregation operators. A numerical illustration is provided to demonstrate the technique's adequacy.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Francisco P. Romero, Jose A. Olivas
Summary: This study introduces a fuzzy framework for computing user mood based on SenticNet and sentic patterns, guiding an ordered weighted averaging operator to provide insights on why certain aspects are rated more or less in a overall rating. The promising framework shows potential for application in tools like customized recommender systems or decision support systems.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: The study introduces an application for ranking hospitals based on user preferences and opinions on different services, classifying hospital aspects semi-automatically, calculating sentiment orientation, and representing polarity through intuitionistic fuzzy sets. The ranking of hospitals is done through user preferences, an aggregation operator, and a multicriteria decision-making algorithm. The methodology is validated using a large set of hospital reviews and comparison baselines, showing the soundness of the proposal.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Eusebio Angulo, Francisco P. Romero, Julio A. Lopez-Gomez
Summary: This study evaluates and determines the performance of goalkeepers using various soft-computing methods, including fuzzy multi-criteria decision-making and metaheuristic optimization algorithms, based on data from the 2020 European Men's Handball Championship. The results show that the metaheuristic-based method is helpful in quantifying expert assessments, while the other two techniques offer more easily interpretable results.
Editorial Material
Computer Science, Artificial Intelligence
Lorenzo Malandri, Carlos Porcel, Frank Xing, Jesus Serrano-Guerrero, Erik Cambria
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: Most hospital assessment systems fail to detect patient emotions accurately. This study utilized a deep learning architecture to detect multiple emotions from patient reviews, achieving an average accuracy of 95.82%. The combination of gated recurrent unit and multichannel convolutional neural network proved effective in exploiting semantic and syntactic characteristics of patient opinions.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Software Engineering
Jesus Serrano-Guerrero, Bashar Alshouha, Francisco P. Romero, Jose A. Olivas
Summary: This study compares the performance of shallow machine learning-based and deep learning-based algorithms in emotion detection for Arabic language. Translated lexicons were used to add emotional features and improve the algorithms' results. The findings show that semantic approaches outperform classical algorithms, with the BiLSTM algorithm performing the best when using word2vec.
JOURNAL OF UNIVERSAL COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: Measuring hospital services is difficult, so opinions from previous patients are crucial for deciding which services to choose. Many online platforms use aspect-based sentiment analysis techniques to analyze opinions. However, these techniques do not capture situations where both positive and negative aspects exist but the overall sentiment is indeterminate. This study presents a new application of simplified neutrosophic sets to hospital ranking, which outperforms other fuzzy logic-based approaches.
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
(2023)
Article
Mathematics
Catalina Lozano-Murcia, Francisco P. Romero, Jesus Serrano-Guerrero, Jose A. Olivas
Summary: Machine learning is a subfield of artificial intelligence that focuses on creating algorithms capable of learning from data and making predictions. However, in actuarial science, the interpretability of these models often poses challenges, leading to concerns about their accuracy and reliability. Explainable artificial intelligence (XAI) has emerged as a solution to address these issues by facilitating the development of accurate and comprehensible models.
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Mohammad Bani-Doumi, Francisco P. Romero, Jose A. Olivas
Summary: This study uses a multi-granular fuzzy linguistic model to evaluate the different features of health care systems and recommend hospitals based on user preferences. By assessing the opinions of real hospitals, the results of this approach outperform other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Julio Alberto Lopez-Gomez, Francisco P. Romero, Eusebio Angulo
Summary: This paper provides objective evaluation criteria and features for handball goalkeepers based on their actions during a match, and validates the effectiveness of these criteria and features through computer experiments and case studies.
Article
Mathematics, Applied
Francisco P. Romero, Catalina Lozano-Murcia, Julio A. Lopez-Gomez, Eusebio Angulo Sanchez-Herrera, Eduardo Sanchez-Lopez
Summary: The article proposes a data-driven approach for sports team performance by weighting and aggregating statistical indicators to select the most valuable player in each game. The study is divided into principal component analysis and meta-heuristic analysis, successfully predicting the Player of the Match in most games.
COMPUTATIONAL AND MATHEMATICAL METHODS
(2021)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
