Article
Computer Science, Artificial Intelligence
Yan Sun, Xiaojun Zhou, Chunhua Yang, Tingwen Huang
Summary: Multi-attribute decision making (MADM) is widely used in real-world problems, but it imposes a cognitive burden on decision-makers to comprehend the decision-making process and select a satisfactory choice from conflicting alternatives. To solve this problem, a visual analytics approach for MADM (MADM-VA) is proposed. Experimental results show that this approach is efficient and reliable.
INFORMATION FUSION
(2023)
Article
Mathematics
Lihua Zeng, Haiping Ren, Tonghua Yang, Neal Xiong
Summary: This article proposes an improved intuitionistic fuzzy entropy based on the cotangent function and a new IF similarity measure. These methods are applied to the study of expert weight in group decision making and a new intelligent expert combination weighting scheme is summarized. The results show reasonable expert clustering and weighting, providing a new method for determining expert weight objectively and reasonably.
Article
Mathematics
Chia-Nan Wang, Ngoc-Ai-Thy Nguyen, Thanh-Tuan Dang, Chen-Ming Lu
Summary: The COVID-19 pandemic has accelerated the growth of e-commerce and significantly impacted global supply chains. Vietnam's logistics service sector has rapidly expanded as more businesses turn to third-party logistics providers for outsourcing. A hybrid multi-criteria method combining FAHP and FVIKOR has been used to evaluate and select the most efficient 3PLs, with reliability, delivery time, logistics cost, and quality of service identified as key factors influencing outsourcing decisions.
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: This paper proposes an improved intuitionistic fuzzy Einstein weighted averaging operator to overcome the drawbacks of existing operators, and introduces a new multiattribute decision making method based on it.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Naeem Jan, Jeonghwan Gwak, Dragan Pamucar, Luis Martinez
Summary: The objective of this study is to address challenging decision-making issues by proposing innovative concepts. The study focuses on evaluating the importance of the operating system in computer systems using complex intuitionistic fuzzy soft relations. The proposed methodology provides a fuzzy-based solution for OS-related problems.
INFORMATION SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Mohamed Abdel-Basset, Abduallah Gamal, Mohamed Elhoseny, Ripon K. Chakrabortty, Michael Ryan
Summary: Reverse logistics involves the gathering and redeployment of goods from consumers to manufacturers for reutilization, disposal, or remanufacturing, posing challenges in social, environmental, risk, and safety aspects of sustainable development. A novel hybrid multiple-criteria decision-making framework can help businesses select the most suitable third-party reverse logistics providers.
Article
Green & Sustainable Science & Technology
Muhammad Hamza Naseem, Jiaqi Yang, Tongxia Zhang, Waseem Alam
Summary: Digital technologies like blockchain, the Internet of Things, and smart warehouses have been developed due to the fourth industrial revolution, or Industry 4.0. The adoption of blockchain technology can have a huge impact on a company's reverse logistics, accelerating processes by decentralizing, tracking, and overseeing the delivery of items to final consumers. This study identified 16 impediments to the adoption of blockchain technology and highlights the need for careful evaluation and addressing of barriers to ensure successful implementation and enhance supply chain management.
Article
Operations Research & Management Science
Ruchi Mishra, Rajesh Kr Singh, Mani Venkatesh
Summary: This study proposes a decision-making framework for prioritizing solutions to overcome barriers in omnichannel adoption in logistics. An empirical case study validates the effectiveness of this framework. The proposed framework incorporates various methods to capture human thinking and subjectivity, offering guidelines for practitioners in successfully adopting omnichannel retailing.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Jin Ye, Jianming Zhan, Zeshui Xu
Summary: This paper proposes a novel decision-making method based on fuzzy rough sets to transform uncertain data into intuitionistic fuzzy data, establish a new MADM method, and introduce intuitionistic fuzzy weights and global intuitionistic fuzzy thresholds.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Busra Buran, Mehmet Ercek
Summary: This study presents a business model canvas framework for public transportation organizations and evaluates the proposed model using fuzzy analytic hierarchy process method and two extensions. The results show that the internal environment is the most important criterion and IF-AHP and SF-AHP provide similar weight results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Xiaofeng Chen, Yanting Fang, Junyi Chai, Zeshui Xu
Summary: This paper investigates the integration of intuitionistic fuzzy (IF) sets and Analytical Hierarchy Process (AHP) to maximize advantages. Quantitative differences between AHP weights and normalized defuzzified IF-AHP weights are illustrated, revealing qualitative and quantitative disparities between AHP and IF-AHP. The study identifies conditions and strategies for utilizing IF-AHP over AHP, with data experiments and case studies for validation.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Construction & Building Technology
Vidhi Vyas, Ajit Pratap Singh, Anshuman Srivastava
Summary: The paper introduces a novel decision-making methodology for integrated condition assessment and maintenance of airfield pavements using non-destructive testing, by considering a variety of performance indicators and methods. It adopts a combination of FAHP and entropy method to prioritize maintenance and repair strategies for airfield pavement sections, and utilizes the SWOT model and its hybrid forms to prioritize pavement maintenance policy alternatives based on technical feasibility, durability, financial viability, and reliability. This approach offers flexibility for customization and implementation based on specific problems and data availability, providing decision-makers, planners, and implementation agencies with a wide range of options to address diverse issues and execute strategies promptly.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Amir Baklouti
Summary: In multiple-attribute decision-making problems, ranking the alternatives is crucial for making optimal decisions. Intuitionistic fuzzy numbers are an effective tool for dealing with uncertainty and vagueness in these problems. However, current ranking methods for intuitionistic fuzzy numbers fail to consider the probabilistic dominance relationship, resulting in inconsistent and inaccurate rankings. This paper proposes a new ranking method based on the probabilistic dominance relationship and fuzzy algebras, which can handle incomplete and uncertain information and produce consistent and accurate rankings.
Article
Computer Science, Interdisciplinary Applications
Aalok Kumar, Ramesh Anbanandam
Summary: This study proposes a hierarchical framework for assessing freight transport companies based on environmentally responsible transport practices (ERTPs), focusing on areas such as environmental knowledge sharing, quality of human resource, collaborative green awareness training programs, promoting environmental awareness program for employees, and compliance of government transport emission law and practice. The analysis shows that these ERTPs significantly contribute to the environmental sustainability of the freight transport industry, and the proposed framework can be used for assessing the performance of freight transportation companies based on ERTPs.
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
(2021)
Article
Green & Sustainable Science & Technology
Shuyang Yu, Dan Wang
Summary: This research aims to provide a reference strategy for Open Universities in China to develop non-academic education during their transition period. The study found that, despite some drawbacks, favorable national policies and citizens' learning needs have created good opportunities for the development of Open Universities.
Article
Materials Science, Multidisciplinary
Omid Mirzaee, Isabelle Huynen, Mohsen Zareinejad
Summary: The study focused on designing single and double layer microwave absorbers using FeCoNi@C metal organic framework and MWCNTs. It was found that the double-layer absorber showed wider absorption bandwidth and lower reflection loss compared to the single-layer absorber, while the single-layer absorber also exhibited good absorption performance at certain frequencies.
Article
Computer Science, Artificial Intelligence
Ignacio Revuelta, Francisco J. Santos-Arteaga, Enrique Montagud-Marrahi, Pedro Ventura-Aguiar, Debora Di Caprio, Frederic Cofan, David Cucchiari, Vicens Torregrosa, Gaston Julio Pineiro, Nuria Esforzado, Marta Bodro, Jessica Ugalde-Altamirano, Asuncion Moreno, Josep M. Campistol, Antonio Alcaraz, Beatriu Bayes, Esteban Poch, Federico Oppenheimer, Fritz Diekmann
Summary: In response to overwhelming health emergencies, accurate prediction models using scientific evidence are essential for guiding healthcare centers, especially for high-risk populations. The developed hybrid prediction model offers high accuracy in predicting severe COVID-19 progression, outperforming other competing models and assisting in patient-centered resource management for COVID-19.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Editorial Material
Computer Science, Artificial Intelligence
Debora Di Caprio, Francisco Javier Santos Arteaga
Article
Computer Science, Artificial Intelligence
Debora Di Caprio, Francisco J. Santos-Arteaga, Madjid Tavana
Summary: The study designed an information retrieval algorithm that mimics the behavior of decision-makers when evaluating alternatives displayed by an online search engine. Experimental results show that stability of click-through rates prevails among top-ranked alternatives in relatively reliable scenarios, but drops when initial reliability decreases.
APPLIED INTELLIGENCE
(2022)
Article
Environmental Sciences
Ahmadreza Afrasiabi, Madjid Tavana, Debora Di Caprio
Summary: The formalization and solution of supplier selection problems based on sustainable indicators have become essential in strategic analysis and maximizing competitive advantage in the supply chain process. The study proposes a hybrid fuzzy multi-criteria decision making method to solve sustainable-resilient supplier selection problems and demonstrates its applicability through a real-life application. The results show the influential criteria in studying SRSSPs related to the manufacturing industry and validate the robustness of the proposed framework through sensitivity analysis methods.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Debora Di Caprio, Francisco J. Santos-Arteaga, Madjid Tavana
Summary: This study uses a stochastic information retrieval algorithm to analyze the effects of search engine rankings and user impatience on information retrieval behavior, finding that impatience significantly affects click-through rates.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Francisco J. Santos-Arteaga, Debora Di Caprio, Madjid Tavana, Emilio Cerda Tena
Summary: Multiple criteria decision-making (MCDM) methods often overlook the strategic evaluations of experts, which can lead decision makers (DMs) to blindly trust the rankings without considering the credibility of the evaluations. To address this issue, we propose a game-theoretical approach that incorporates hesitant fuzzy numbers and allows for restrictions on credibility and potential manipulation by experts. We demonstrate the significance of strategic incentives in MCDM techniques by extending a real-life study case and illustrating the interactions between reporting strategies and counteracting tools for DMs.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Business
Francisco J. Santos-Arteaga, Debora Di Caprio, Madjid Tavana
Summary: Firms worldwide have adopted Information and Communication Technologies (ICTs) due to their positive impact on performance. However, decision makers face uncertainty when selecting local firms to interact with. This study examines the decision-making process in selecting Decision Making Units (DMUs) based on their efficiency determined through Data Envelopment Analysis (DEA) and considers the uncertainties in inputs and outputs. The study also presents a case study on the efficiency of European countries' ICT development.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Debora Di Caprio, Francisco J. Santos-Arteaga
Summary: It is assumed that MADM rankings are definitive, but they do not consider the potential consequences of choices. This paper aims to design an integrated MADM framework that allows decision makers to modify their initial choices based on observed characteristics. The combinatorial decision environment arising from defining and evaluating sequences of choices is analyzed, and the TOPSIS method is used to design the integrated evaluation framework. A case study is presented to demonstrate how the selection of countries and their order can vary substantially when accounting for complementarities, influencing the selection process and subsequent decisions.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Samira Vazifeh-Noshafagh, Vahid Hajipour, Sajjad Jalali, Debora Di Caprio, Francisco Javier Santos-Arteaga
Summary: This paper explores the strategies for maturing the Scrum framework in portfolio management through a case study approach. It proposes a heuristic scoring technique and a multi-level refinement structure to enhance team performance monitoring and the realization rate of release planning.
Article
Business
Francisco J. Santos-Arteaga, Madjid Tavana, Debora Di Caprio
Summary: We present an equilibrium model where market demand determines the strategic incentives of firms when considering the introduction of technologically superior products (TSPs). The behavior of decision-makers and their sequential acquisition of product information are key factors in shaping market demand. Firms can signal the introduction of TSPs, but only experimental decision-makers update their beliefs when selecting a product.
JOURNAL OF INNOVATION & KNOWLEDGE
(2022)
Meeting Abstract
Surgery
I. Revuelta, F. Santos-Arteaga, D. Di Caprio, E. Montagud-Marrahi, F. Cofan, J. Torregrosa, M. Bodro, A. Moreno, P. Ventura-Aguiar, D. Cucchiari, N. Esforzado, G. Pineiro, J. Ugalde-Altamirano, J. Campistol, A. Alcaraz, B. Bayes, E. Poch, F. Oppenheimer, F. Diekmann
AMERICAN JOURNAL OF TRANSPLANTATION
(2021)
Article
Operations Research & Management Science
Debora Di Caprio, Francisco J. Santos-Arteaga
Summary: This paper explores the information retrieval incentives of decision makers in evaluating alternatives displayed by online search engines, analyzing the differences between search engine rankings and subjective evaluations of DMs. Comparing various evaluation models, it is found that artificial agents require more complex combinatorial abilities than actual decision makers to better approximate heuristic choices in online evaluation environments.
OPERATIONS RESEARCH PERSPECTIVES
(2021)
Meeting Abstract
Surgery
Ignacio Revuelta, Francisco-Javier Santos-Arteaga, Debora Di Caprio, Enrique Montagud-Marrahi, Frederic Cofan, Jose V. Torregrosa, Marta Bodro, Asuncion Moreno, Pedro Ventura-Aguiar, David Cucchiari, Nuria Esforzado, Gaston Pineiro, Jessica Ugalde-Altamirano, Josep M. Campistol, Antonio Alcaraz, Beatriu Bayes, Esteban Poch, Federico Oppenheimer, Fritz Diekmann
TRANSPLANT INTERNATIONAL
(2021)
Article
Health Policy & Services
Francisco Javier Santos Arteaga, Debora Di Caprio, David Cucchiari, Josep M. Campistol, Federico Oppenheimer, Fritz Diekmann, Ignacio Revuelta
Summary: The study applies Data Envelopment Analysis (DEA) to evaluate the efficiency of 485 patients undergoing kidney transplantation, classifying patients based on specific and interrelated variables and identifying potential improvements on a per patient basis.
HEALTH CARE MANAGEMENT SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
