Article
Green & Sustainable Science & Technology
Xi Yang, Zhihe Chen
Summary: Reasonable water resources management is crucial for sustainable development. This study proposes a decision-making framework combining interval TOPSIS and a multi-sensitivity strategy for robust water resources management under uncertainty.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Automation & Control Systems
Novak Zagradjanin, Dragan Pamucar, Kosta Jovanovic, Nikola Knezevic, Bojan Pavkovic
Summary: This paper presents exploration strategies based on Multi-Criteria Decision-Making (MCDM) methods for autonomous robots to perform tasks in unknown environments. The simulation results show that TOPSIS method performs well in high-risk environments, while there is no significant difference among the MCDM methods in low-risk environments. Additionally, MCDM-based exploration strategies achieve better results than single-criterion strategies in any environment.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Farah Azaliney Binti mohd Amin, Saiful Hafizah Binti Jaaman
Summary: The stock performance of 10 Shariah-compliant companies listed by the Securities Commission of Malaysia (SCM) was investigated based on key financial ratios. The study found that NESTLE and PETGAS were the top-performing stocks, while MISC, PMETAL, and AXIATA performed the worst.
Article
Materials Science, Characterization & Testing
Mustafa Ay, Furkan Sarsilmaz
Summary: Two different aluminum alloy pairs were joined using friction stir welding method, and the effects of different welding parameters on the welded joint zones were evaluated. Optimum welding parameters were determined using multi-criteria decision making techniques and the performances of different methods were compared.
Article
Computer Science, Artificial Intelligence
Ihsan Kaya, Ali Karasan, Betul Ozkan, Murat Colak
Summary: Modern era robots are versatile and social, increasing their importance in human life. Evaluating robots involves uncertainty and vagueness, requiring multi-criteria decision-making methods. This study proposes an integrated fuzzy MCDM methodology based on interval-valued Pythagorean fuzzy sets for social robot evaluation problem.
Article
Computer Science, Artificial Intelligence
Sepehr Hendiani, Grit Walther
Summary: In this study, a new method called TOPSISort-L is proposed to classify alternative solutions under different circumstances by using the likelihoods of IVIFSs. By developing the conventional fuzzy TOPSIS technique with a newly proposed decision matrix, a novel selection mechanism for ideal solutions, and a likelihood-based closeness metric, this method can achieve accurate classification when characteristic profiles information is available and approximate classification when it is missing. Finally, the validity and adaptability of the method are demonstrated by comparing it with various existing methodologies.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Bingchao Zhao, Han Wang, Zhihao Huang, Qianqian Sun
Summary: This research provides an automated Multi-Criteria Decision Making (MCDM) technique with geographical information system (GIS) to solve the intricate nature of location identification and prioritization difficulty caused by the availability of numerous indicators, such as economic and environmental technical, social, and risk criteria. The F-TOPSIS outperforms the other methods with the highest performance ratio of 98.78% when compared to others.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Energy & Fuels
Ankur Dwivedi, Anoop Kumar, Varun Goel
Summary: This paper presents a systematic framework based on multiple attribute decision-making tools for selecting suitable nanoparticles as heat transfer promoters. A hybrid GRA-TOPSIS approach is proposed to evaluate, compare, and rank different alternatives. The study determines objective weights using CRITIC and entropy weighing methods, and subjective weights using the AHP. Al2O3 nanoparticles are found to be the most acceptable alternative. The sensitivity analysis demonstrates the robustness and feasibility of the decision framework.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(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
Thermodynamics
Reza Shahraki Shahdabadi, Akbar Maleki, S. Haghighat, Mohammad Ghalandari
Summary: Biomass is a convenient type of renewable energy resource with the location of a biomass power plant being crucial. This study applied Multi-Criteria Decision-Making Methods to assess the feasibility of biomass energy generation in Iran, prioritizing cities based on various criteria. The results showed Mashhad as the most suitable location for a biomass power plant, while Nehbandan was deemed unsuitable due to population and distance from electrical power substation.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2021)
Article
Economics
Katarzyna Halicka, Dariusz Kacprzak
Summary: In the past 20 years, there have been significant changes in the age structure of the global population, with a decreasing percentage of working-age population and an increasing number of retired people, leading to a more aging society. The growing number of seniors has highlighted the need for institutional support in the form of care. Gerontechnology, which uses technology to cater to the aspirations and abilities of older adults, is considered a key element in helping seniors.
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
(2021)
Article
Green & Sustainable Science & Technology
Jianjun Wang, Jikun Huo, Shuo Zhang, Yun Teng, Li Li, Taoya Han
Summary: The article discusses the importance of flexible transformation of coal-fired thermal power units in China according to the country's economic green development requirements, and establishes a decision-making evaluation index system for reference. Through empirical research using a hybrid evaluation method, targeted recommendations for the flexibility transformation of coal-fired thermal power units are provided.
Article
Thermodynamics
Claude Ziad El-Bayeh, Khaled Alzaareer, Brahim Brahmi, Mohamed Zellagui, Ursula Eicker
Summary: This paper proposes an original multi-criteria decision-making algorithm based on Rank-Weight-Rank concept for selecting the best solar panels. Compared to TOPSIS method, our approach demonstrates advantages in terms of simulation time and selection accuracy.
Article
Engineering, Multidisciplinary
Padmanabha Raju Chinda, Ragaleela Dalapati Rao
Summary: The appropriate placement of flexible AC transmission systems (FACTS) is crucial in power systems. This study proposes a method combining multi-attribute decision making techniques and a particle mobility honey bee algorithm to determine the optimum locations for dynaflow devices and maximize efficiency.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Materials Science, Ceramics
V. P. Srinivasan, P. K. Palani, S. Balamurugan
Summary: The study explores the optimal set of machining parameters for EDM of Si3N4-TiN ceramic composites using GRA and TOPSIS methods, with focus on material removal rate, electrode wear rate, surface roughness and geometrical tolerances.
CERAMICS INTERNATIONAL
(2021)
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)