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
Computer Science, Artificial Intelligence
S. Haseena, S. Saroja, T. Revathi
Summary: Due to busy lifestyles, people easily adapt to unhealthy diets, leading to various health problems. This study proposes a system that ranks diet plans based on personal information to meet individual nutritional needs.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Shubham Dutt Attri, Shweta Singh, Atul Dhar, Satvasheel Powar
Summary: This paper presents the application of multi-criteria decision-making in the assessment of wastewater treatment technologies and provides a comparison study of six technologies based on sustainability parameters. The study proposes a reliable decision-making method for selecting sustainable wastewater treatment technologies.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Chemical
Morteza Cheraghi, Aliakbar Eslami Baladeh, Nima Khakzad
Summary: The Hazard and Operability (HAZOP) study generates a list of recommendations for system safety improvement, but due to resource limitations, it is necessary to prioritize and implement specific recommendations based on expert evaluation. This study aims to develop a comprehensive hierarchy of factors for recommendation evaluation, utilizing the Best Worst Method (BWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the recommendations and improve accuracy.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Mathematics, Applied
Chihkang Kenny Wu, Chia-Nan Wang, Thi Kim Trang Le
Summary: A hybrid fuzzy multi-criteria decision model was used to select the most suitable agritourism location in Vietnam for long-term investment, ultimately choosing Can Tho (A8) as the best investment site. The model estimated relative criteria ratings through fuzzy analytic hierarchy process and ranked potential locations using fuzzy technique for order preference, aiming to maximize resource utilization and local benefits.
Article
Computer Science, Artificial Intelligence
Nabilah Abughazalah, Majid Khan, Mohsin Iqbal
Summary: The robustness of modern information confidentiality algorithm relies on its individual components, with modern block ciphers relying on confusion and diffusion achieved through substitution and permutation boxes. This article introduces the IVPF-TOPSIS method to select the optimal S-box for nonlinear confusion component in block cipher. Cryptographic analyses were performed on standard S-boxes using various characteristics, and a decision-making technique was applied to classify suitable S-boxes for modern block cipher construction based on interval-valued Pythagorean fuzzy TOPSIS.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Huibing Cheng, Shanshui Zheng, Jianghong Feng
Summary: This study aims to address the issue of selecting a sustainable ferry operator for the Zhuhai municipal government. It proposes an integrated MCDM framework model that combines the fuzzy analytic hierarchy process (FAHP), entropy weight (EW) method, and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS). The proposed method is validated through a real case study on the Wanshan Islands in Zhuhai, and the results demonstrate its effectiveness in evaluating and selecting ferry operators.
Article
Energy & Fuels
Ander Zubiria, Alvaro Menendez, Hans-Jurgen Grande, Pilar Meneses, Gregorio Fernandez
Summary: This study formulates a multi-criteria decision making problem considering fifteen selection criteria and the opinions of five energy storage experts groups. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied to rank eighteen technologies. The results show that pump hydro storage is the most suitable for frequency regulation, time shifting, and seasonal storage applications, while flywheels are best for inertial response.
Article
Mathematics, Applied
M. B. Dowlatshahi, A. Hashemi
Summary: This article proposes a fuzzy multi-criteria decision-making method for unsupervised feature selection, utilizing an ensemble of unsupervised feature selection rankers to evaluate features. It is the first time a fuzzy multi-criteria decision-making approach has been used for this problem, and multiple comparisons are made to demonstrate its optimality and effectiveness.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2023)
Review
Environmental Studies
Farhad Samimi Namin, Aliakbar Ghadi, Farshad Saki
Summary: Mining Method Selection (MMS) is an important decision in mining design. Empirical models have limitations, thus Multi Criteria Decision Making (MCDM) is considered more effective. This research reviews literature on MCDM applications in MMS and presents decision methods and the importance of criteria in different ore bodies.
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
Materials Science, Multidisciplinary
Aamir A. A. Rahim, S. Numaya Musa, S. Ramesh, Ming K. Lim
Summary: This study introduces a fuzzy-TOPSIS MCDM model for material selection that integrates safety, health, and environment risk assessment, aiming to provide a method suitable for the manufacturing sector. The approach utilizes fuzzy logic to assist designers in selecting, evaluating, and ranking material alternatives with strong justification.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS
(2021)
Article
Energy & Fuels
Pedro Ponce, Citlaly Perez, Aminah Robinson Fayek, Arturo Molina
Summary: With the increasing demand for electrical energy due to population growth and factory automation, solar energy has been installed to meet the increased energy needs. Evaluating a solar panel company is a complex task, with multiple criteria to consider and a need for holistic solutions. The Fuzzy TOPSIS method has proved efficient in selecting the best solar panel company to meet the needs of manufacturing companies.
Article
Computer Science, Information Systems
Shu-Ping Wan, Wen-Chang Zou, Jiu-Ying Dong, Luis Martinez
Summary: The paper introduces the concept of lowest consensus threshold and develops a new two-stage CRP method for MCGDM with LIFVs. Simulation experiments show that the value of lowest consensus threshold decreases with increasing number of decision makers, and the first stage in CRP can enhance the value of this threshold.
INFORMATION SCIENCES
(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)
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)