Article
Green & Sustainable Science & Technology
Oguz Emir, Sule Onsel Ekici
Summary: In recent years, waste management has gained attention due to sustainability concerns and the depletion of natural resources. Food waste management is particularly important given the growing population and hunger crisis. Integrated assessment models (IAMs) have been commonly used to study food waste and provide insights to policymakers, while the Fuzzy Cognitive Map (FCM) extended with intuitionistic fuzzy sets offers a framework for analyzing interactions between factors and prioritizing policies.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Kirti Sharma, Vishnu Pratap Singh, Ali Ebrahimnejad, Debjani Chakraborty
Summary: Various optimization approaches have been developed and used for generating optimal solutions for different industry related optimization problems. The semantic representation of imprecise coefficients and various types of uncertainties arising in real life optimization problems are still a challenging task and require attention of academicians as well as professionals.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Business
Joao F. C. Rodrigues, Fernando A. F. Ferreira, Leandro F. Pereira, Elias G. Carayannis, Joao J. M. Ferreira
Summary: Digital transformation has had a significant impact on global business processes, including in the banking industry. However, the determinants of successful banking digitalization and their cause-and-effect relationships are still unclear. This study used problem structuring methods to develop a conceptual model for analyzing digitalization in the banking industry.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2022)
Article
Environmental Studies
Rahim Tavakolifar, Himan Shahabi, Mohsen Alizadeh, Sayed M. Bateni, Mazlan Hashim, Ataollah Shirzadi, Effi Helmy Ariffin, Isabelle D. Wolf, Saman Shojae Chaeikar
Summary: This study compared the predictive capacities of fuzzy logic-ANP (FLANP) and fuzzy logic-TOPSIS (FLTOPSIS) for mapping landslide susceptibility along the Saqqez-Marivan main road in Kurdistan province, Iran. The FLTOPSIS method showed better prediction accuracy with an AUCROC of 0.983 compared to 0.938 for the FLANP method. The susceptibility map developed through the FLTOPSIS method is suitable for informing management and planning in landslide-prone areas in mountainous regions.
Article
Computer Science, Artificial Intelligence
V. P. Singh, Kirti Sharma, Debjani Chakraborty, Ali Ebrahimnejad
Summary: This paper presents an optimization method based on the TOPSIS method to solve a multi-objective model of a bi-level linear programming problem with intuitionistic fuzzy coefficients. The method simplifies the problem to a conventional multi-objective bi-level linear programming problem using an accuracy function and employs a modified TOPSIS method to solve it. The proposed method utilizes various linear/non-linear membership functions to represent the flexibility of decision-makers at both the leader and follower levels.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
F. Tanhaie, M. Rabbani, N. Manavizadeh
Summary: This study investigates the use of mixed-model assembly line (MMAL) technology to address make-to-order (MTO) challenges, achieving superior performance through a multi-objective particle swarm optimization (MOPSO) algorithm.
ENGINEERING OPTIMIZATION
(2021)
Article
Computer Science, Artificial Intelligence
Istvan A. Harmati, Miklos F. Hatwagner, Laszlo T. Koczy
Summary: Fuzzy cognitive maps are an effective modeling tool for complex systems. Global stability is not always essential, as multiple fixed points are preferred in many applications.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Svajone Bekesiene, Aidas Vasilis Vasiliauskas, Sarka Hoskova-Mayerova, Virgilija Vasiliene-Vasiliauskiene
Summary: This paper presents the application of Fuzzy AHP-TOPSIS hybrid method in distance learning quality assessment surveys. Thirty-four judges with specific knowledge and skills were chosen to evaluate three alternatives by fourteen criteria, and statistical analysis was used to process the data. The study further provides useful guidelines for the development of an easily understandable hierarchy of criteria model reflecting the main goal of study quality assessment.
Article
Social Sciences, Interdisciplinary
Pengfei Li, Seyyed Ahmad Edalatpanah, Ali Sorourkhah, Saziye Yaman, Nasreen Kausar
Summary: Evaluating and ranking schools are important for parents and upstream institutions, but the current evaluation process ignores the perspective of parents and qualitative indicators. This study captures the perspectives of key stakeholders by using the opinions of parents and experienced school administrators. In addition, the study adds qualitative criteria that are less noticed and eliminates less influential sub-criteria. The proposed methodology significantly impacts school ranking and highlights the importance of considering different stakeholders' views.
Article
Computer Science, Artificial Intelligence
G. Prathyusha, K. N. Udaya Kumara, G. A. Vatsala
Summary: By applying the solid transportation problem to milk distribution, the bi-objective solid transportation problem is solved using hierarchical order goal programming technique to optimize total transportation cost and time, resulting in improved efficiency of milk distribution.
Article
Mathematics, Applied
Ya Qin, Rizk M. Rizk-Allah, Harish Garg, Aboul Ella Hassanien, Vaclav Snasel
Summary: This study proposes a novel approach, TOPSIS-IFS, for solving multi-criterion optimization problems (MCOP). The approach integrates the TOPSIS technique with the intuitionistic fuzzy set (IFS) to characterize conflicting objectives and obtain a solution model. The approach is validated through an illustrative example and has been applied to solve the practical problem of multi-objective transportation. The impacts of IFS parameters on the solution are analyzed using the Taguchi method.
Article
Automation & Control Systems
Alireza Eydi, Pardis Shirinbayan
Summary: This study aims to introduce a multi-commodity multi-model hierarchical hub location problem with uncertain demand, and employs intuitionistic fuzzy variables for estimation. The objective of this problem is to minimize total transportation costs in the network by determining the optimal hub location, allocating non-hub nodes to hubs, and identifying the type of vehicles required in each route. The study presents a mathematical model with a hierarchical structure and solves it using GAMS optimization software.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Chemistry, Multidisciplinary
Takaaki Kasuga, Tsuguyuki Saito, Hirotaka Koga, Masaya Nogi
Summary: This study reports a simple and flexible strategy for constructing sophisticated hierarchical structures using electrophoretic and electrochemical deposition. By adjusting the applied voltage, cellulose nanofibers (CNFs) are oriented and deposited on an anode, forming anisotropic mechanical properties and complex hierarchical structures. This technique is expected to be applicable to various materials and contribute to fields such as biomimicry, functional nanomaterials, and sustainable and functional moldings.
Article
Computer Science, Artificial Intelligence
Divya Chhibber, Pankaj Kumar Srivastava, Dinesh C. S. Bisht
Summary: This study proposes a new approach, called intuitionistic fuzzy TOPSIS, to solve a non-linear multi-objective intuitionistic fuzzy problem. The effectiveness of the approach is demonstrated through the solution of illustrative examples.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Huai-Wei Lo, James J. H. Liou
Summary: This article discusses a research paper by Nilashi et al. (2019) on factors influencing medical tourism adoption in Malaysia, highlighting errors and misguided use of fuzzy TOPSIS in their study.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Operations Research & Management Science
Adil Baykasoglu, Nurhan Dudakli, Kemal Subulan, A. Serdar Tasan
Summary: This paper explores the importance of fleet planning in intermodal transportation and proposes a holistic approach to address the complexity of fleet planning in comparison with unimodal systems. Through a comprehensive mixed-integer linear programming model, the optimization of fleet planning is achieved.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Fehmi Burcin Ozsoydan, Adil Baykasoglu
Summary: The Flower Pollination Algorithm (FPA) simulates the pollination behavior of flowers to solve global optimization problems, and has been extended and modified to improve performance. Modifications include adjustments to the pollination mechanism and convergence control, as well as the extension to a species-based algorithm for independent search of promising regions. These modifications show significant improvements in solving various optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Cybernetics
Ilker Golcuk
Summary: This paper proposes an integrated IT2F-FMEA model that aims to address the issue of loss of information due to aggregation of expert opinions in risk assessment. By integrating fuzzy inference system, best-worst method, and weighted aggregated sum-product assessment methods, a comprehensive failure mode and effect analysis model is presented.
Article
Computer Science, Artificial Intelligence
Adil Baykasoglu, Fatma S. Madenoglu
Summary: This study proposed a GRASP algorithm for simultaneous dynamic scheduling of operations and preventive maintenance activities in flexible job shops, considering various dynamic events and maintenance strategies. The results demonstrated that this approach is effective in improving performance in dynamic flexible job shop scheduling environments.
Article
Computer Science, Artificial Intelligence
Adil Baykasoglu, Elif Ercan
Summary: The paper examines the rank reversal problem in MADM methods, focusing on the WASPAS method. Computational experiments show that traditional normalization techniques lead to rank reversal problems in WASPAS, but using modified normalization techniques can mitigate this issue.
Article
Engineering, Industrial
Fehmi Burcin Ozsoydan, Ilker Golcuk
Summary: This paper introduces a cooperative approach using swarm intelligence algorithm and linear programming solver to solve the capacitated facility location problem. The proposed strategy decomposes the problem into two sub-problems and utilizes an improved algorithm and optimization model for solving. The experimental results show promising performance of the proposed strategy.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Ilker Golcuk, Esra Duygu Durmaz, Ramazan Sahin
Summary: This paper presents a new interval type-2 fuzzy multiple attribute decision-making model for evaluating facility layout alternatives. The proposed model extends the Full Consistency Method to interval type-2 fuzzy sets and incorporates Activity Relationship Charts for expressing preferences between departments. The results of a case study demonstrate the feasibility of the proposed model in practical applications.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Cybernetics
Adil Baykasoglu, Burcu Felekoglu, Ceylin Unal
Summary: The usage of learning management systems (LMSs) has become widespread due to the disruption caused by the COVID-19 pandemic. Selecting a suitable LMS is a complex decision-making problem that involves considering multiple criteria and inputs from different parties. Usability evaluation of LMS is a critical step in the decision-making process. This study proposes an axiomatic design procedure (ADP) based approach for the perceived usability evaluation of SAKAI-LMS. The ADP method allows easy data fusion and setting performance targets. A questionnaire is developed to collect data on usability criteria and their importance from system users. The proposed approach provides an easy and practical evaluation of perceived usability of LMSs for decision makers. It has been verified through a real-life case study at an academic department.
Article
Engineering, Multidisciplinary
Fehmi Burcin Ozsoydan, Lker Golcuk
Summary: Artificial Neural Networks (ANNs) have unique opportunities in various research fields, but the training process has some limitations. A training algorithm based on a hyper-heuristic framework is proposed, which utilizes multiple metaheuristic algorithms and learns through a feedback mechanism, resulting in better training results and avoiding local minima issues.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2022)
Article
Automation & Control Systems
Fehmi Burcin Ozsoydan, Ilker Golcuk
Summary: Machine learning and computational intelligence show efficiency in solving optimization problems, but studies combining these approaches are lacking. This paper introduces a Q-learning reinforcement learning strategy for binary optimization problems and utilizes a variety of optimization algorithms to avoid local optima. The experimental results demonstrate the effectiveness of this approach in solving the set-union knapsack problem.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Kemal Subulan, Bilge Varol, Adil Baykasoglu
Summary: This paper introduces a new unequal-area capability-based facility layout design problem and proposes a MINLP model and a heuristic decomposition-based iterative solution approach to solve it. Computational results demonstrate the effectiveness and applicability of the proposed approach through illustrative examples and a real-life application.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Manufacturing
Cengiz Baykasoglu, Adil Baykasoglu, Erhan Cetin
Summary: The objective of this study is to find the optimum design configurations for functionally graded lattice structure filled aluminum tubes under multiple impact loading conditions. The optimal design is sought for maximizing specific energy absorption and minimizing peak crush force by considering base strut diameter, draft angle, and aspect ratio as filler material design parameters. Finite element simulations, regression meta-models, and an attraction-repulsion algorithm were employed to establish the design space, estimate objective function values, create design alternatives, and seek their optimum combinations. The results showed that the crashworthiness performance of hybrid structures can be effectively enhanced by selecting appropriate lattice filler parameters, with a potential improvement of up to 76% in specific energy absorption for square tubes. This study provides a guideline for the optimum design of functionally graded lattice structure filled thin-walled structures under multiple impact loading conditions.
INTERNATIONAL JOURNAL OF CRASHWORTHINESS
(2023)
Article
Computer Science, Artificial Intelligence
Ilker Golcuk, Fehmi Burcin Ozsoydan, Esra Duygu Durmaz
Summary: This paper introduces an improved Arithmetic Optimization Algorithm (AOA) for training artificial neural networks (ANNs) in dynamic environments. The proposed algorithm optimizes the connection weights and biases of the ANN under concept drift, outperforming state-of-the-art metaheuristic optimization algorithms in training ANNs for dynamic classification tasks. The findings demonstrate the potential of the improved AOA for dynamic data-driven applications.
KNOWLEDGE-BASED SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Adil Baykasoglu, Mumin Emre Senol
Summary: This study demonstrates the improvement of metaheuristic algorithms performance through the application of Levy flight to the WSAR algorithm. The experimental results show that the Levy flight WSAR algorithm performs better than other algorithms in constrained design optimization problems.
PROCEEDINGS OF 7TH INTERNATIONAL CONFERENCE ON HARMONY SEARCH, SOFT COMPUTING AND APPLICATIONS (ICHSA 2022)
(2022)
Article
Sociology
Nurhan Dudakli, Burcu Felekoglu, Adil Baykasoglu
Summary: This paper explores the reverse innovation process of multinational enterprises in emerging markets and identifies the key factors for successful reverse innovation, including the quality of innovation ideas, collaboration between MNEs and local enterprises, and unique diffusion strategies.
INNOVATION-THE EUROPEAN JOURNAL OF SOCIAL SCIENCE RESEARCH
(2022)
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)
