Article
Green & Sustainable Science & Technology
Renan Felinto de Farias Aires, Luciano Ferreira
Summary: This study presents a new material selection approach based on the TOPSIS method, which solves the rank reversal problem in multi-criteria decision-making methods and applies to sustainable material selection.
Article
Computer Science, Artificial Intelligence
Siamak Kheybari
Summary: This paper proposes a new approach based on defining upper and lower expectation levels for each decision-making criterion to address the trade-offs in multi-criteria decision-making problems.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2021)
Article
Computer Science, Artificial Intelligence
Meysam Rabiee, Babak Aslani, Jafar Rezaei
Summary: This paper proposes an anti-biased statistical approach for group decision-making, including extreme, moderate, and soft versions. By eliminating biased decision-makers and assigning different weights to DMs, the approach effectively addresses biased decision-making in various scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Environmental Sciences
Qiushuang Wei, Chao Zhou
Summary: This paper provides insights into electric vehicle supplier selection from the perspective of government agencies and public bodies using an integrated multi-criteria decision-making framework. Through a case study and analysis, it determines the importance of criteria such as bad environmental record, cost, quality, service, and environmental initiatives in electric vehicle supplier selection.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Theory & Methods
Loubna Lamrini, Mohammed Chaouki Abounaima, Mohammed Talibi Alaoui
Summary: Nowadays, the online environment allows companies to have more options and opportunities due to the abundance of information. However, in the field of multicriteria decision-making, most tools have limitations in terms of the number of alternatives and criteria considered. This necessitates the use of screening or filtering methods, which can slow down the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments is crucial to handle the scalability of multicriteria decision-making solutions in Big Data contexts. In this study, a parallel implementation of the widely used TOPSIS method based on the MapReduce paradigm is proposed, which reduces response time and enables analysis of method robustness and sensitivity in high-dimension problems.
JOURNAL OF BIG DATA
(2023)
Article
Operations Research & Management Science
Jahangir Wasim, Vijay Vyas, Pietro Amenta, Antonio Lucadamo, Gabriella Marcarelli, Alessio Ishizaka
Summary: In group decisions, balancing different decision-makers' opinions and aggregating their evaluations are two key issues. This paper presents a new algorithm that considers the selection of weights in non-negotiable multi-group problems.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Lucas Falch, Clarence W. de Silva
Summary: This paper introduces the VIKOR method, a new normalization method, and a minimum weight margin. By using the modified VIKOR method for design decision making, it can overcome the weaknesses of the original VIKOR method and improve the stability of the solutions.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Energy & Fuels
Ander Zubiria, Alvaro Menendez, Hans-Jurgen Grande, Pilar Meneses, Gregorio Fernandez
Summary: This study formulates a multi-criteria decision making problem considering fifteen selection criteria and the opinions of five energy storage experts groups. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied to rank eighteen technologies. The results show that pump hydro storage is the most suitable for frequency regulation, time shifting, and seasonal storage applications, while flywheels are best for inertial response.
Article
Green & Sustainable Science & Technology
Slavisa Dumnic, Katarina Mostarac, Milena Ninovic, Bojan Jovanovic, Sandra Buhmiler
Summary: Personnel selection is crucial in human resource management, and fuzzy decision-making methods have gained popularity due to their ability to handle uncertainty. The Choquet integral, a commonly used aggregation operator, allows for consideration of interdependencies between criteria. This paper utilized the Choquet integral based on fuzzy measures for personnel selection.
Article
Engineering, Multidisciplinary
Shalabh Singh, Sonia Singh
Summary: This article addresses the issue of traditional assignment problems being unable to handle multiple cost and time entries due to different working modes of machines. It proposes a solution to model it as a multi-choice bi-objective assignment problem, with objectives being assignment cost and bottleneck time. Two solution procedures are presented based on the Decision-Maker's preference structure, one using goal programming and another using a two-step approach to find all Pareto-optimal time-cost pairs.
ENGINEERING OPTIMIZATION
(2022)
Article
Economics
Xingli Wu, Huchang Liao, Edmundas Kazimieras Zavadskas, Jurgita Antucheviciene
Summary: VIKOR is a well-defined MCDM method that reflects decision-makers' risk attitudes by measuring alternative performance overall and individually, but cannot handle inconsistent criteria and uncertain decision information. The proposed probabilistic linguistic VIKOR method combines probabilistic linguistic term sets to flexibly portray uncertain information.
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
(2022)
Article
Mathematics
Aleksey I. Shinkevich, Nadezhda Yu. Psareva, Tatyana V. Malysheva
Summary: The article presents a methodology for solving the multicriteria problem of choosing industrial zones for the development of chemical industries. The methodology uses an additive global criterion and includes multi-criteria selection, data scaling, convolution of criteria, and statistical analysis. The chosen industrial zones in the Russian region demonstrate above-average environmental sustainability but are affected by adjacent environmentally unfavorable industries. The article suggests that the proposed methodology can be applied in developing intelligent systems for monitoring and controlling the development of chemical industries while considering environmental safety.
Article
Mathematics
Eduarda Asfora Frej, Lucia Reis Peixoto Roselli, Alexandre Ramalho Alberti, Murilo Amorim Britto, Evonio de Barros Campelo Junior, Rodrigo Jose Pires Ferreira, Adiel Teixeira de Almeida
Summary: The COVID-19 pandemic has overwhelmed healthcare systems globally, and decision-makers are grappling with the question of who should be admitted to intensive care units (ICUs). This study proposes the use of Expected Utility Theory and Bayesian decision analysis to tackle this issue. A structured protocol based on the Sequential Organ Failure Assessment (SOFA) score is developed to estimate patient survival chances. The study also presents a portfolio selection approach that outperforms existing methods in terms of lives saved. To address the uncertainties in assessing survival probabilities, a Monte Carlo simulation is utilized. The implementation of this methodology in an online Information and Decision System called SIDTriagem offers great potential in resource allocation during public health emergencies.
Article
Computer Science, Artificial Intelligence
Mehmet Sahin
Summary: Material selection is crucial in product design and manufacturing. This study proposes an integrated approach of multi-attribute decision-making for material selection and demonstrates its feasibility and the advantage of comprehensive analysis through three different problem examples.
Article
Computer Science, Information Systems
Hongyan Wang, Hua Xu, Yuan Yuan, Zeqiu Zhang
Summary: This paper introduces Adaptive Batch-ParEGO, an adaptive batch Bayesian optimization method for expensive multi-objective problems. It extends the classical sequential ParEGO method to the batch mode, utilizing a bi-objective acquisition function and an adaptive solution selection criterion to balance exploration and exploitation.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ben Mrad, Veronique Delcroix, Sylvain Piechowiak, Philip Leicester, Mohamed Abid
APPLIED INTELLIGENCE
(2015)
Article
Automation & Control Systems
D. Gacquer, V. Delcroix, F. Delmotte, S. Piechowiak
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2011)
Article
Computer Science, Artificial Intelligence
Karima Sedki, Veronique Delcroix
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
(2012)
Article
Computer Science, Artificial Intelligence
Veronique Delcroix, Emmanuelle Grislin-Le Strugeon, Francois Puisieux
Summary: This study aims to establish an individual information database management system to maintain up-to-date information about elderly people medically followed for risks of fall, while dealing with various sources of uncertainty and maintaining information quality.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Chemistry, Analytical
Julia Greenfield, Veronique Delcroix, Wafae Ettaki, Romain Derollepot, Laurence Paire-Ficout, Maud Ranchet
Summary: This study analyzed the relationship between cortical activity and walking speed in elderly adults. The results showed that individuals with a slower preferred walking speed required a higher increase in cortical activity. Individuals in the fast cluster presented greater changes in cortical activation in the right hemisphere. This study demonstrates the importance of cortical activity in relation to walking speed and suggests that age may not be the most relevant factor.
Proceedings Paper
Computer Science, Artificial Intelligence
Ali Ben Mrad, Veronique Delcroix, Sylvain Piechowiak, Philip Leicester
PROBABILISTIC GRAPHICAL MODELS
(2014)
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)
