Article
Operations Research & Management Science
M. Gabriela Sava, Luis G. Vargas, Jerrold H. May, James G. Dolan
Summary: The decision-making process often involves limited and incomplete information. In order to understand the impact of additional information on the initial result, a new multi-dimensional stability analysis method was developed. This method allows for the observation of preference changes as criteria weights are perturbed, providing useful stability measures.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Julian Andres Castrillon-Gomez, Gerard Olivar-Tost, Johnny Valencia-Calvo
Summary: This research proposes an integrated approach for evaluating and prioritizing green projects using system dynamics modeling and the analytical network process. The methodology consists of three stages: obtaining citizen factors and data through community participation, consolidating and calibrating the system dynamics model to generate relevant information for experts, and translating the model into a complex network for decision making through peer review. The application of the methodology in a case study in Colombia demonstrates its effectiveness.
Review
Computer Science, Artificial Intelligence
Long Chen, Wei Pan
Summary: With the complex and uncertain nature of construction management, fuzzy multi-criteria decision making (FMCDM) has become popular for addressing diverse decision-makers' interests and conflicting objectives. This study comprehensively reviews FMCDM literature in construction management from 2007 to 2017 using a network approach, exploring the relationships between fuzzy sets (FSs), MCDM, and associated applications. The analysis of 165 published journal articles reveals the characteristics, strengths, and limitations of 37 single-hybrid and 17 multiple-hybrid FMCDM methods.
APPLIED SOFT COMPUTING
(2021)
Article
Biodiversity Conservation
M. Hamidah, I. Mohd Hasmadi, L. S. L. Chua, W. S. Y. Yong, K. H. Lau, I Faridah-Hanum, H. Z. Pakhriazad
Summary: This study proposes a methodology using MCDM-AHP for assessing the criterion weights of Malaysian IPA. The weights were determined through questionnaire surveys and the study found that threatened habitats, threatened species, endemism, and botanical richness are the most important criteria.
GLOBAL ECOLOGY AND CONSERVATION
(2022)
Article
Engineering, Industrial
Wuyang Sun, Yifei Zhang, Ming Luo, Zhao Zhang, Dinghua Zhang
Summary: The selection of cutting parameters is crucial in machining aviation parts with high performance requirements. This paper proposes a novel multi-criteria decision-making system to determine the optimal cutting parameters from multiple alternatives. The proposed system combines the technique for order preference by similarity to an ideal solution (TOPSIS) and adversarial interpretive structural modeling (AISM) to make the decision.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Hwa-Young Jeong
Summary: This research utilized accommodation information from Airbnb to create a personalized recommendation model and analyzed guest preferences using the AHP method, providing optimal room choices for guests.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Food Science & Technology
Sofie Schryvers, Thomas De Bock, Mieke Uyttendaele, Liesbeth Jacxsens
Summary: This study demonstrates the use and application possibilities of the multi-criteria decision analysis (MCDA) methodology in food safety risk management, specifically in the washing of minimally processed leafy greens. The study found that the washing procedure is critical in preventing cross-contamination of pathogens, and that the use of chemical sanitizers in produce wash water is inconsistent across EU member states. Through the MCDA methodology, the most appropriate washing method was determined to be using potable (ice) water.
Article
Agricultural Engineering
Niravkumar Kosamia, Arturo Sanchez, Sudip Rakshit
Summary: This study improves the decision-making process of large-scale succinic acid production by applying the hesitant fuzzy analytical hierarchy process (HFAHP) as a multi-criteria decision analysis method. It considers techno-economic and environmental criteria and performs weightage allocation and sensitivity analysis based on expert opinions.
INDUSTRIAL CROPS AND PRODUCTS
(2023)
Article
Computer Science, Artificial Intelligence
Jih-Jeng Huang, Chin-Yi Chen
Summary: This paper focuses on the issues of path restriction between criteria and the transition functions of the supermatrix in the ANP. It is found that a criterion with a longer path to another criterion should have less influence, which is similar to the conventional ANP. In addition, the inclusion of transition functions in the supermatrix allows for the consideration of non-linear transitions. The numerical examples demonstrate that the proposed method is a generalization of the ANP and reduces to the conventional ANP under certain conditions.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Fatma Onay Kocoglu
Summary: This study considers the problem of selecting the best classification model in the artificial learning process as a Multi-Criteria Decision-Making problem. A model based on machine learning has been proposed, which is simple, understandable, and can support users of all levels.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Mathematics
Chin-Yi Chen, Jih-Jeng Huang
Summary: This paper presents an innovative method that integrates dynamic Bayesian networks (DBNs) with the analytic hierarchy process (AHP) to model dynamic interdependencies between criteria in multi-criteria decision-making (MCDM) problems. The proposed method extends the AHP to accommodate time-dependent issues and reduces to the conventional AHP when ignoring specific information, making it a more general AHP model.
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
Cansu Dagsuyu, Ulviye Polat, Ali Kokangul
Summary: In this study, a method based on AHP and C-p values was developed to identify the significance level of criteria on surface quality in the surface protection processes, focusing on the immersion phosphating process. Important subprocesses affecting the final quality were identified through the developed approach, providing a systematic way to evaluate and improve the quality of surface protection processes.
Article
Environmental Sciences
Wenbin Ma, Yanlian Du, Kairui Zhang, Yijun Shen
Summary: This paper aims to evaluate the sustainability of deep-sea mining vertical transport plans using the fuzzy analytic network process. It considers technological, economic, environmental, and social factors and determines weights for each criterion through a questionnaire survey completed by experts. The research findings could be directly applied to the sustainability assessment of upcoming deep-sea mining projects, contributing to the industrialization of the entire deep-sea mining industry.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jin Qi, Jie Hu, Haiqing Huang, Yinghong Peng
Summary: Customer-Oriented Design Concept Evaluation (CDCE) helps companies select the best design concept from the customer's perspective. Previous studies have overlooked the customer's confidence attitude and design specifications. To address this, a new CDCE method called Improved Z-number-based Multi-criteria Decision Making (IZ-MCDM) is proposed, which better expresses and utilizes customer's uncertain opinion. Experimental results validate the significance of IZ-MCDM, showing that besides customer preference, confidence attitude and design specifications significantly impact the evaluation result.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Information Systems
Elif Haktanir, Cengiz Kahraman
Summary: This paper introduces a novel intuitionistic Z-fuzzy QFD method based on Chebyshev's inequality, which can effectively handle the vague information in customer needs, and applies it to the design and development of hand sanitizer for COVID-19.
Article
Computer Science, Artificial Intelligence
Sukran Seker, Cengiz Kahraman
Summary: The study developed a new hybrid multi-criteria decision-making method for selecting the most efficient vendor-supplied software package. The method combines two well-known MCDM approaches and uses fuzzy sets to manage uncertainty, subjectivity, and bias of decision makers. A real-life application is conducted to prove the efficiency and applicability of the proposed method.
Article
Green & Sustainable Science & Technology
Esra Ilbahar, Cengiz Kahraman, Selcuk Cebi
Summary: Investments and policies in the energy system play a significant role in maintaining sustainable development. Forecasting energy demands and evaluating the impact of renewable energy on the environment and society are crucial for effective energy planning. However, this is a complex issue with many interdependent factors that are often not well-defined in real-world problems. In this study, a method combining fuzzy cognitive mapping, weighted aggregated sum product assessment, and impact effort assessment is introduced to rank different sustainability scenarios and identify effective energy planning options.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Harish Garg, Cengiz Kahraman, Zeeshan Ali, Tahir Mahmood
Summary: Complex Pythagorean fuzzy set (CPFS) is an influential principle for managing ambiguity and inconsistent information in genuine life dilemmas. This study combines CPFS with interaction HM operators to propose CPFIHM, CPFIWHM, CPFIDHM, and CPFIWDHM operators, and analyzes their properties. The study also applies these operators in a decision-making strategy for determining security threats in computer systems. The consistency and flexibility of these operators are illustrated through examples.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Kiran Zahid, Cengiz Kahraman
Summary: This study focuses on the digitalization of the public transportation system in Istanbul to reduce environmental pollution and address climate change. Two important techniques are proposed for decision-making by considering the dominance and subordination between alternatives. The decision is made based on concordance, discordance and indifferent sets, and the evaluation of criteria weights is conducted using the spherical fuzzy analytical hierarchy process.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Kiran Zahid, Cengiz Kahraman
Summary: This study aims to develop two effective and practical multi-criteria group decision-making approaches using the PROMETHEE family of outranking methods. The proposed variants of the PROMETHEE method address complex decision-making problems with ambiguous information. Both approaches employ the Shannon's entropy formula to evaluate object weights and determine preference indices based on deviations between potential alternatives. The proposed techniques, spherical fuzzy PROMETHEE I and spherical fuzzy PROMETHEE II, compare outranking flows and eliminate incomparable pairs, respectively, to derive rankings. A case study in the medical field regarding the selection of a site for a shelter hospital in Wuhan is included to demonstrate the applicability of the proposed methodologies.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Kiran Zahid, Cengiz Kahraman
Summary: This study proposes a complex Pythagorean fuzzy ELECTRE III method, which effectively handles pseudo criterion and two dimensional imprecise data for authentic decision-making by taking advantage of the flexible structure of complex Pythagorean fuzzy sets.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Musa Bayir, Ertugrul Yucel, Tolga Kaya, Nihan Yildirim
Summary: Spot welding is a critical joining process in automotive manufacturing that requires control over welding parameters to optimize production efficiency and quality. By utilizing data analytics and machine learning algorithms, this study aimed to identify the root cause of welding defects and solve the problem of input value range. Real-time IoT data analysis was conducted to determine the best working range of welding parameters, providing guidelines for expulsion reduction and optimization in welding processes.
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Hafiza Saba Nawaz, Cengiz Kahraman
Summary: A rough set approximates a subset of a universal set based on a binary relation, and a Pythagorean fuzzy set provides information about truthness and falsity. This paper proposes a new combination of rough sets and Pythagorean fuzzy sets, called rough Pythagorean fuzzy sets, which can handle uncertainties in imprecise data. The manuscript presents a general framework for studying rough Pythagorean fuzzy approximations and discusses the properties of approximation operators induced from different binary relations. Algorithms for computing reduct family and rough Pythagorean fuzzy approximations are developed and applied to real-world examples.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Industrial
Elif Haktanir, Cengiz Kahraman
Summary: Risk adjusted interest rate (RADR) method and certainty equivalent (CE) method are popular methods for investment analysis under risky conditions. This study extends novel RADR and CE methods for use under uncertainty by using single valued intuitionistic fuzzy (IF) sets. The study analyzes high speed rail and nuclear power plant investments using IF-RADR and IF-CE methods. This study provides important insights on analyzing investments under risk with fuzzy sets extensions. The study also includes an IF sensitivity analysis for a nuclear power plant investment problem. Overall, the study is rated 8 out of 10 for its importance.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2023)
Article
Computer Science, Information Systems
Nursah Alkan, Cengiz Kahraman
Summary: Innovations in digital technology have a significant impact on the supply chain and logistics sectors. The concept of a digital supply chain has emerged as a new trend in manufacturing and services. However, organizations face uncertainties in adapting to digitalization and prioritizing criteria and strategies. To address these challenges, the study proposes a new method called the interval-valued fermatean fuzzy analytic hierarchy process (IVFF-AHP). This method helps determine the best strategy and criteria for digital transformation in the supply chain.
Article
Mathematics
Cengiz Kahraman, Selcuk Cebi, Basar Oztaysi, Sezi Cevik Onar
Summary: This paper extends the TOPSIS method to the IF TOPSIS with ordered pairs method and applies it to a supplier selection problem under fuzziness. By asking functional and dysfunctional questions in the ranking process, this method incorporates the accuracy and consistency of expert judgments, enhancing the decision-making process.
Proceedings Paper
Computer Science, Artificial Intelligence
Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi
Summary: This paper proposes a method for addressing the problem of cloud service provider selection, which involves handling uncertainties in assessments using interval-valued picture fuzzy sets and evaluating alternatives using the TOPSIS method. Comparative and sensitivity analyses are performed to validate the effectiveness and robustness of the proposed method.
INTELLIGENT AND FUZZY SYSTEMS: DIGITAL ACCELERATION AND THE NEW NORMAL, INFUS 2022, VOL 1
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Cengiz Kahraman, Alexander Bozhenyuk, Margarita Knyazeva
Summary: This paper discusses the concept of maximum intuitionistic internally stable vertex subset in a graph with fuzzy intuitionistic stability degree. The notion of an internally stable set is introduced as an invariant of an intuitionistic fuzzy graph. The paper proposes an approach for finding all maximal intuitionistic internally stable subsets of vertices, as an extension of the method for calculating all maximal internally stable subsets in a crisp graph. The method that allows estimating all maximal internally stable subsets in a fuzzy graph with the highest degree of stability is considered. An example illustrating the operation of the method for finding the internally stable set of the intuitionistic fuzzy graph is also presented in this article.
INTELLIGENT AND FUZZY SYSTEMS: DIGITAL ACCELERATION AND THE NEW NORMAL, INFUS 2022, VOL 1
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Basar Oztaysi, Cengiz Kahraman, Sezi Cevik Onar
Summary: Global warming is causing environmental and quality of life disruptions, largely due to greenhouse gas emissions. Electric vehicles are seen as a solution to reduce these emissions. However, the selection of electric vehicles involves multiple criteria and alternatives, and this study proposes a fuzzy SMART method to address this decision problem.
INTELLIGENT AND FUZZY SYSTEMS: DIGITAL ACCELERATION AND THE NEW NORMAL, INFUS 2022, VOL 1
(2022)
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)
