Article
Computer Science, Artificial Intelligence
Sepehr Hendiani, Grit Walther
Summary: In this study, a new method called TOPSISort-L is proposed to classify alternative solutions under different circumstances by using the likelihoods of IVIFSs. By developing the conventional fuzzy TOPSIS technique with a newly proposed decision matrix, a novel selection mechanism for ideal solutions, and a likelihood-based closeness metric, this method can achieve accurate classification when characteristic profiles information is available and approximate classification when it is missing. Finally, the validity and adaptability of the method are demonstrated by comparing it with various existing methodologies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Aamir Mahboob, Tabasam Rashid, Muhammad Sarwar Sindhu
Summary: Multi-criteria group decision making plays a central role in selecting attributes for different firms, where traditional techniques for alternative selection are deemed insufficient. To address this issue, a weighted preference method utilizing HILD has been proposed and compared with TOPSIS method. Sensitivity analysis and time complexity are used to enhance the proposed technique. Following the calculation of the best ideal solution using the HILD preference technique, TOPSIS technique is applied, and a comparison between the two techniques is emphasized.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
M. Palanikumar, K. Arulmozhi, Omaima Al-Shanqiti, Chiranjibe Jana, Madhumangal Pal
Summary: A novel method for solving MADM under a sine trigonometric Pythagorean neutrosophic normal interval-valued set (ST-PyNSNIVS) is proposed. The identifying feature of ST-PyNSNIVS is its combination of PyNSIVS, PyNSS, and IVNSS. New concepts of ST-PyNSNIVWA, ST-PyNSNIVWG, ST-GPyNSNIVWA, and ST-GPyNSNIVWG are introduced, along with flowcharts and algorithms for MADM. The sine trigonometric aggregation operations using the PyNSNIV set technique are found to be more straightforward and practical, leading to accurate conclusions.
JOURNAL OF MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Wangyong Lv, Shijing Zeng, Jiao Zhou, Tingting Li, Arthur Sandor Voundi Koe
Summary: This paper introduces a standard Euclidean distance measure between IVPFLSs and an IVPFL-KPCA model to reduce dimensionality and obtain reasonable weight vectors, improving decision-making efficiency and reducing algorithm complexity. By applying the technique of order performance by similarity to ideal solution, the best emergency plan can be selected for low-dimensional decision data, demonstrating the feasibility and effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Mathematics
Jinhui He, Huirong Zhang, Zhenyu Zhang, Jiaping Zhang
Summary: A hidden property evaluation model based on the PL3W-MADM method was developed to classify judgment debtors into three categories more effectively than the strict and flexible PL3W-MADM models. The model considers the expressions of evaluation information using probabilistic linguistic term sets and probabilities given by expert judges.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Shouzhen Zeng, Zeeshan Ali, Tahir Mahmood, Huanhuan Jin
Summary: This study explores the conception, fundamental properties, aggregation methods, and advantages of the complex interval-valued q-rung orthopair 2-tuple linguistic set (CIVQRO2-TLS), as well as demonstrates the benefits of a multi-attribute decision-making system.
APPLIED ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: In this paper, a new multiple attribute group decision making method based on linguistic intuitionistic fuzzy numbers is proposed. The method overcomes the drawbacks of existing methods and provides a useful approach for decision-making in linguistic intuitionistic fuzzy environments.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Itzcoatl Bueno, Ramon A. Carrasco, Carlos Porcel, Gang Kou, Enrique Herrera-Viedma
Summary: The exponential growth in online data has led to a radical transformation in the tourism sector. Decision makers can benefit or be harmed by the large amount of information available, which can impact their satisfaction after purchase. A methodology integrating multiple decision-making techniques was proposed to rank hotels based on past client opinions, using the Recency, Frequency, Helpfulness model and a fuzzy linguistic approach. This methodology was verified through a business case applied to TripAdvisor data.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Biswajit Sarkar, Animesh Biswas
Summary: This article introduces a new family of linguistic Pythagorean fuzzy aggregation operations and discusses their necessary properties. A methodology for addressing multi-criteria group decision-making problems is proposed using weighted distance measures and entropy measures. Aggregation is done at the final stage to obtain the final ranking of alternatives.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Yuan Xu, Shifeng Liu, Jun Wang
Summary: This study focuses on the application of interval-valued q-rung orthopair fuzzy sets in multiple-attribute group decision-making problems, proposing a new definition of interval-valued q-rung orthopair uncertain linguistic sets and a model framework. Through comparative analysis, the performance and advantages of the new method are demonstrated.
Article
Automation & Control Systems
K. Janani, R. Rakkiyappan
Summary: This article introduces the concept of complex probabilistic fuzzy set to combine statistical and non-statistical uncertainties. It also develops various aggregation operators and extends them to the TOPSIS method for practical applications. The importance of this research lies in its ability to accurately depict real-life situations by incorporating different types of uncertainty.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: In this paper, a distance measure for linguistic intuitionistic fuzzy sets (LIFSs) is proposed, where the membership grade and non-membership grade are represented by linguistic intuitionistic fuzzy numbers (LIFNs). The validity and desirable properties of the proposed distance measure are proven, and a weighted distance measure for LIFSs is also introduced. Furthermore, a new group decision making approach in the environments of LIFNs is proposed using the weighted distance measure and the TOPSIS method. The proposed approach overcomes the drawbacks of existing approaches and provides a useful method for GDM problems in LIFNs environments.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ihsan Kaya, Ali Karasan, Betul Ozkan, Murat Colak
Summary: Modern era robots are versatile and social, increasing their importance in human life. Evaluating robots involves uncertainty and vagueness, requiring multi-criteria decision-making methods. This study proposes an integrated fuzzy MCDM methodology based on interval-valued Pythagorean fuzzy sets for social robot evaluation problem.
Article
Computer Science, Artificial Intelligence
Shahzad Faizi, Mubashar Shah, Tabasam Rashid
Summary: This paper presents a multi-criteria group decision-making (MCGDM) method and a modified VIKOR method based on hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs). The study also explores operational laws and aggregation operators for HIFLTSs. The research demonstrates that the proposed methods can accurately describe the fuzziness and uncertainty of experts, and provide effective and reliable ranking results.
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
I. Chamodrakas, I. Leftheriotis, D. Martakos
APPLIED SOFT COMPUTING
(2011)
Article
Computer Science, Artificial Intelligence
Ioannis Chamodrakas, Drakoulis Martakos
APPLIED SOFT COMPUTING
(2011)
Article
Computer Science, Artificial Intelligence
Ioannis Chamodrakas, Drakoulis Martakos
APPLIED SOFT COMPUTING
(2012)
Article
Computer Science, Artificial Intelligence
I. Chamodrakas, D. Batis, D. Martakos
EXPERT SYSTEMS WITH APPLICATIONS
(2010)
Proceedings Paper
Computer Science, Theory & Methods
Irene Koronaki, Maja Skrjanc, Tatsiana Hubina, Klemen Kenda, Kostas Kalaboukas, Simon Mokorel, George Markogiannakis, Steffen Nienke, Hamodrakas Giannis, Caterina Calefato
2015 6TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS AND APPLICATIONS (IISA)
(2015)
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)