Article
Mathematics, Applied
Mesut Karabacak
Summary: This article introduces a method that combines interval neutrosophic sets with Aczel-Alsina aggregation operators, and proposes a multi-criteria group decision-making model based on Aczel-Alsina operators. The model takes into account the objective and subjective evaluations of decision makers and determines the weights of decision criteria using the DAMATEL method. A detailed comparison analysis is provided to demonstrate the accuracy and operability of the new model.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Tapan Senapati, Guiyun Chen, Ronald R. Yager
Summary: This paper describes new intuitionistic fuzzy aggregation operators based on Aczel-Alsina operations, introduces new operations and aggregation operators for IFSs, and demonstrates their properties, followed by the design of new techniques dependent on these operators to solve multiattribute decision making problems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Muhammad Rizwan Khan, Kifayat Ullah, Hanen Karamti, Qaisar Khan, Tahir Mahmood
Summary: This article aims to develop the Aczel-Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. The q-ROFSs can adjust the information region by fluctuating the restriction ≥ 1. PAOs have the advantage of vanishing the influence of awkward data from the final results.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Mathematics, Applied
Saba Ijaz, Kifayat Ullah, Maria Akram, Dragan Pamucar
Summary: This research derives the prioritized PF Aczel-Alsina average operator and prioritized PF Aczel-Alsina geometric operator by analyzing the theory of averaging and geometric aggregation operators (AOs) in the presence of the Aczel-Alsina operational laws and prioritization degree based on picture fuzzy (PF) information. The properties of these operators are examined and evaluated, and they are used to create a system for controlling the multi-attribute decision-making problem using PF information. The effectiveness of the approach and the validity of the operators are demonstrated through a numerical example and comparative analysis.
Article
Energy & Fuels
Maria Akram, Kifayat Ullah, Goran Cirovic, Dragan Pamucar
Summary: This paper focuses on multi-criteria group decision-making and proposes two new aggregation operators based on q-rung orthopair fuzzy information and Aczel-Alsina t-norm and t-conorm. The operators, q-rung orthopair fuzzy prioritized Aczel-Alsina averaging and q-rung orthopair fuzzy prioritized Aczel-Alsina geometric operators, consider the priority weights of the information. The proposed operators have a wider range for handling information and show advantages compared to other operators.
Article
Computer Science, Information Systems
Tahir Mahmood, Ubaid Ur Rehman, Zeeshan Ali
Summary: Bipolar complex fuzzy information can effectively represent ambiguous and uncertain real-life problems. It combines two different theories, bipolar fuzzy set and complex fuzzy set, to form a new concept. The theory focuses on analyzing the interrelationship among multiple attributes and provides operational laws for computation. The proposed BCFAAWA, BCFAAOWA, and BCFAAHA operators offer solutions for weighted averaging and decision-making in a multi-attribute setting.
INFORMATION SCIENCES
(2023)
Article
Multidisciplinary Sciences
Abrar Hussain, Kifayat Ullah, Mohammed Nasser Alshahrani, Miin-Shen Yang, Dragan Pamucar
Summary: In this article, new types of Pythagorean fuzzy AOs are developed using Aczel-Alsina t-norm and Aczel-Alsina t-conorm. The characteristics of these AOs are elaborated, and their effectiveness is demonstrated through solving a MADM example and comparing with existing AOs in different fuzzy environments.
Article
Computer Science, Artificial Intelligence
Tapan Senapati
Summary: This article describes the aggregation operators of single-valued neutrosophic sets using the Aczel-Alsina operations. It extends the AA t-norm and t-conorm to single-valued neutrosophic scenarios and introduces new SVN operations such as AA sum, AA product, AA scalar multiplication, and AA exponentiation. These operations are used to generate useful SVN aggregation operators and a multiple attribute decision-making method. The article provides an illustration to demonstrate the effectiveness of the proposed operators and strategy, and conducts a comprehensive analysis of the new framework.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Tahir Mahmood, Zeeshan Ali, Samruam Baupradist, Ronnason Chinram
Summary: This analysis discusses a theory that is suitable for evaluating complex, risk-illustrating, and asymmetric information. It introduces the principle of Aczel-Alsina (AA) t-norm and t-conorm to handle ambiguity and inconsistency in real-life problems. The major contribution of this analysis is the analysis of AA operational laws under complex intuitionistic fuzzy settings. Furthermore, new concepts such as CIFAA weighted averaging and CIFAA ordered weighted averaging are introduced, along with their beneficial results. The analysis also explores the superiority and feasibility of the developed works through a multi-attribute decision-making technique.
Article
Mathematics, Applied
Tapan Senapati
Summary: This article introduces the aggregation strategies of Picture Fuzzy Numbers (PFNs) using Aczel-Alsina operations. The Aczel-Alsina t-norm and t-conorm are extended to PF situations, and several new operations and aggregation operators are developed based on PFNs. The characteristics and relationships of these operators are explored, and an application in multiple attribute decision-making with PF data is presented, along with a comparative analysis.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Abrar Hussain, Kifayat Ullah, Dragan Pamucar, Izatmand Haleemzai, Dusan Tatic
Summary: The MADM approach is effective for handling ambiguous information, and aggregation operators are important mathematical tools. This article explores the theory of intuitionistic fuzzy IF sets and triangular norms, and presents new approaches for solving real-life problems using solar panel systems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Mathematics, Applied
Haolun Wang, Tingjun Xu, Liangqing Feng, Tahir Mahmood, Kifayat Ullah
Summary: A series of novel aggregation operators integrated by the Hamy mean and Aczel-Alsina operations are proposed to solve T-spherical fuzzy multi-criteria decision-making (MCDM) issues. The proposed operators include the T-spherical fuzzy Aczel-Alsina Hamy mean (TSFAAHM) operator, T-spherical fuzzy Aczel-Alsina dual Hamy mean (TSFAADHM) operator, and their weighted forms, i.e., the T-spherical fuzzy Aczel-Alsina-weighted Hamy mean (TSFAAWHM) and T-spherical fuzzy Aczel-Alsina-weighted dual Hamy mean (TSFAAWDHM) operators. The effectiveness of this study is verified through a numerical example and comparison with available methods, along with a parameters influence test.
Article
Automation & Control Systems
Jun Ye, Shigui Du, Rui Yong
Summary: This paper proposes the Aczel-Alsina operations and weighted aggregation operators of neutrosophic Z-numbers (NZNs) to solve the flexible decision-making problem by adjusting different parameter values. The new operations based on the Aczel-Alsina t-norm and t-conorm show the advantage of flexible operations. A numerical example is provided to verify the influence of different parameter values on decision-making results.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dragan Pamucar, Muhammet Deveci, Ilgin Gokasar, Dursun Delen, Mario Koppen, Witold Pedrycz
Summary: Sharing economy transportation applications contribute to environmental sustainability by reducing car ownership and single-vehicle occupancy. Metaverse, a promising new technology, combines sharing economy applications with transportation networks. The integration of these two approaches can improve the sustainability of sharing economy applications.
DECISION SUPPORT SYSTEMS
(2023)
Article
Automation & Control Systems
Bo Chen, Qiang Cai, Guiwu Wei, Zhiwen Mo
Summary: The shift from MADM to MAGDM has resulted in an increase in data volume and a trend towards multi-attribute large group decision-making. In this context, operators are needed to integrate the data and also consider the psychological impact of decision-makers. Linguistic Z-number (LZN) is commonly used for evaluating fuzzy information, but current aggregation operators in the LZN environment have limitations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Operations Research & Management Science
Dragan Pamucar, Ali Ebadi Torkayesh, Sanjib Biswas
Summary: This paper proposes a novel decision-making approach for supplier selection during the COVID-19 pandemic. The approach utilizes MACBETH and a new combinative distance-based assessment method, and incorporates fuzzy rough numbers to handle the uncertainty of decision-making problems. A case study of a hospital in Istanbul validates the applicability of the approach.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Muhammet Deveci, Arunodaya Raj Mishra, Ilgin Gokasar, Pratibha Rani, Dragan Pamucar, Ender Ozcan
Summary: The article introduces a decision-making framework for prioritizing sustainable public transportation in the Metaverse using q-rung orthopair fuzzy set context. The framework includes the development of aggregation operators, weight assessment models, and a combined weighting model. The presented method is demonstrated through a case study and proves to be effective in recommending feasible performance in the face of influencing factors and input uncertainties.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Operations Research & Management Science
Ilgin Gokasar, Alperen Timurogullari, Sarp Semih Ozkan, Muhammet Deveci
Summary: Advancements in autonomous vehicle technology and communication technologies have enabled the development of connected and autonomous vehicles. The proposed IDILIM algorithm, based on CAV, regulates CAV and traffic speeds based on dynamic and predicted shockwave speeds. Compared to variable speed limits (VSL) management, IDILIM reduced density values in the critical region by 89.32%.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Operations Research & Management Science
Z. K. Mohammed, A. A. Zaidan, H. B. Aris, Hassan A. Alsattar, Sarah Qahtan, Muhammet Deveci, Dursun Delen
Summary: Metaverse is a new technology expected to drive economic growth in Industry 5.0. The current bitcoin networks offer significant potential for future metaverse developments with anonymity and privacy. This study explores a modelling process using the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets combined with multiobjective optimization to determine the optimal approach for metaverse implementation in Industry 5.0.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Dragan Pamucar, Adis Puska, Vladimir Simic, Ilija Stojanovic, Muhammet Deveci
Summary: The COVID-19 pandemic has led to an increase in healthcare waste (HCW), prompting the need for re-evaluation of HCW management treatment. This study analyzed six HCW management treatments based on twelve criteria, using a fuzzy rough approach to address inaccuracies in determining values. The results showed that Environmental Impact (C4) received the highest weight, while Automation Level (C8) received the lowest. The application of the Aczel-Alsina function ranked microwave treatment (A6) as the best and landfill treatment (A5) as the worst.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Ghasemzadeh, Dragan Pamucar
Summary: Motivated by a dairy supply chain case, this study examines the management of deteriorating inventories in a three-echelon supply chain network with a local retailer having multiple customers. The customers are divided into two clusters in the downstream with different, uncertain demand functions: many small ordinary customers and few large premier customers. The goal is to determine the optimal inventory policy considering the time-sensitive deterioration rate of the product. The model is formulated using queuing theory and finite-horizon Semi-Markov process, and an integrated inventory system at the network level. Two solution approaches, adaptive Invasive Weed Optimization and Adaptive Heuristic Method, are designed to solve the nonlinear and nonconvex problem, with the former providing better solution quality and the latter being faster in computation. Sensitivity analyses show that perishability has a greater impact than uncertainty when facing both factors.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Tamara Zivkovic, Bosko Nikolic, Vladimir Simic, Dragan Pamucar, Nebojsa Bacanin
Summary: Software testing is a crucial part of software development, but it is often neglected or not detailed enough due to time constraints, leading to various risks. This paper proposes a modified version of the XGBoost model called HARSA for defect prediction, which exhibits excellent performance in the experiments.
APPLIED SOFT COMPUTING
(2023)
Article
Business
Ali Ala, Amir Hossein Sadeghi, Muhammet Deveci, Dragan Pamucar
Summary: With the increasing preference for large retail chains among modern consumers, contemporary shopping complexes are becoming more prevalent. IoT provides the foundation for smart retailers to monitor inventory levels and enhance customer experience. Research shows that improving machine learning algorithms and developing secure consumer applications can improve the performance of smart deals systems.
ELECTRONIC COMMERCE RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Sarbast Moslem, Danish Farooq, Domokos Esztergar-Kiss, Ghulam Yaseen, Tapan Senapati, Muhammet Deveci
Summary: Enhancing road safety by understanding drivers' behavior is crucial. This study identified key drivers' behavior factors related to road safety in Budapest, Hungary. The findings showed that 'Lapses' and 'Errors' were crucial factors for experienced and young drivers, while foreign drivers prioritize 'Errors' and 'Violations.' Aggressive violations were prevalent across all driver groups, and driving with alcohol use was a significant factor. The study also revealed differences in agreement levels among hierarchical driver groups.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Ismail Onden, Dragan Pamucar, Muhammet Deveci, Yakup As, Batin Birol, Feride Suheda Yildiz
Summary: This study aims to analyze the decision-making process in strategic positioning of railway stations and introduces a novel model that can consider Geographic Information System (GIS) outputs and capacity calculations together. The study finds that geographical locations, ease of integration to the existing transportation system, and complementary sectors in the neighborhood play important roles in strategic positioning.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yangyang Sun, Zhu-Jun Wang, Muhammet Deveci, Zhen-Song Chen
Summary: This study utilizes the Hotelling model to investigate the optimal strategy selection and its impact on profits in the context of different solution strategies in the enterprise software market. The findings suggest that in a high-cost rental environment, the on-premise strategy outperforms the SaaS and dual version strategies, while in a low rental fee and widening value gap context, the SaaS strategy is the optimal choice.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Mohammed Al-Samarraay, Omar Al-Zuhairi, A. H. Alamoodi, O. S. Albahri, Muhammet Deveci, O. R. Alobaidi, A. S. Albahri, Gang Kou
Summary: Semiconductor materials are crucial for optoelectronics and power devices, but evaluating and selecting them is a multi-attribute decision-making problem. This study proposes an integrated fuzzy multi-measurement decision-making model (IFMMDMM) for evaluating and selecting optimization techniques for semi-polar III-V semiconductor materials.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Iman Mohamad Sharaf, A. H. Alamoodi, O. S. Albahri, Muhammet Deveci, Mohammed Talal, A. S. Albahri, Dursun Delen, Witold Pedrycz
Summary: This study proposes a novel multi-criteria decision-making solution to address the challenges in evaluating and selecting 5G-RAN architectures. By integrating fuzzy sets and Type-2 neutrosophic fuzzy environment, a more robust evaluation and decision platform is established. The weighting and selection methods determine the most important evaluation criteria and the optimal RAN architecture.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Peng Wang, Yingxin Fu, Peide Liu, Baoying Zhu, Fubin Wang, Dragan Pamucar
Summary: This study explores the ecological governance of the Yellow River and proposes a multi-perspective evaluation method. By using BWM and IGR methods to calculate the weights of the indicators and combining multiple methods for comprehensive evaluation, policy recommendations for the ecological governance of the Yellow River Basin are obtained.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Business
Mustapha Cheikh-Ammar
Summary: This investigation proposes a theory of IT desirability to explain the human experience of IT in today's highly digitized world. The theory takes into account the actions that IT artifacts enable and the tasks that users want to perform, as well as the users' personal values and what the artifact represents for them. The concept of Person-IT Fit is introduced to conceptualize the match between a person's values and the spirit of an IT artifact. The study illustrates the connections between Person-IT Fit and IT Desirability, user behavior, and reasoned appraisals of IT through empirical studies.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Wojciech Czakon, Karolina Mania, Monika Jedynak, Aneta Kuzniarska, Michal Choinski, Marina Dabic
Summary: The study finds that firms' activity on social media can enhance stakeholder engagement and improve communication with customers. By analyzing micro-interactions on social media, the study explores organizational identity and identifies differences in communication strategies with different stakeholders.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Joao Estevao, Jose Dias Lopes
Summary: This study examines renewable energy consumption in the Eurozone from 2000 to 2019 using panel data analysis and the Driscoll-Kraay technique. The study confirms that GDP is inversely related to renewable energy consumption, while Foreign Direct Investment (FDI) and energy imports are negatively associated with renewable energy consumption. Additionally, there is a direct correlation between R&D expenditure and renewable energy consumption. Comparing two models, the disaggregated data model shows a stronger association between renewable energy consumption and electricity production volumes differentiated by energy sources. This study highlights the importance of using disaggregated energy production data for understanding the renewable energy transition.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Nur Azira Norzelan, Intan Salwani Mohamed, Maslinawati Mohamad
Summary: This research aims to investigate the technology acceptance of artificial intelligence (AI) among the heads of finance and accounting units in the shared service industry, using the Theory of Planned Behavior (TPB) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings show that performance expectancy, attitude, skill, and technical capability have a major impact on AI technology acceptance. However, there is no link between AI technology acceptance and effort expectancy, social influence, or facilitating conditions. The findings provide insights on important areas to prioritize when using AI in business, especially in finance and accounting.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Shubo Yang, Atif Jahanger, Muhammad Usman
Summary: Smart cities are a crucial strategy for China's high-quality development, promoting green innovation at the firm level. The smart city pilot policy is particularly important for non-state-owned enterprises, heavily polluting industries, and non-resource-based cities.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Andrew Adewale Alola, Jaana Rahko
Summary: This study examines the impact of climate change technologies on greenhouse gas emissions in the industrial and energy sectors of Nordic countries. The results show that both domestically developed environmental technologies and technology spillovers from foreign economies help mitigate emissions, particularly in the industrial sector. Economic growth also plays a vital role in emissions.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Tobias Buchmann, Patrick Wolf
Summary: This study examines the driving forces behind the creation of breakthrough patents. By comparing the solar PV and wind industries, it is found that closeness to science is more important in the wind industry, while technological specialization is advantageous for breakthrough creation in the photovoltaics industry.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Meiyang Zhang, Xuezhong Zhu, Rui Liu
Summary: The extension of patent length through fast-tracking patent applications policy has a significant positive impact on corporate innovation, particularly in companies with political resources, belonging to industries with a higher patent tendency, and facing fierce market competition.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Francis Kyere, Dongying Sun, Gertrude Dotse Bampoe, Naana Yaa Gyamea Kumah, Dennis Asante
Summary: This research examines the acceptance and resistance of household-based solar PV systems in Ghana. The findings reveal that household values and attitudes, cost implications, performance expectancy, technological complexity, and market design are key factors influencing the adoption or resistance of solar PV systems.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Yuhai Lu, Mincheng Gong, Linzhuo Lu, Yaqin Wang, Yang Wang
Summary: Improving energy efficiency is crucial for environmental protection and resource conservation. This study measured the impact of urban polycentrism on total-factor energy efficiency in 332 prefecture-level cities in China, using satellite data and panel data analysis. The results provide insights into the relationship between urban spatial distribution and energy efficiency.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Joynal Abdin, Abhijit Sharma, Rohit Trivedi, Chengang Wang
Summary: This study examines the relationship between financing constraints, intellectual property rights protection, and firm innovation within transitional economies. The findings suggest that both financing constraints and intellectual property rights have adverse effects on firm innovation, with varying impacts across industries. Additionally, the study proposes that in transition economies, firms tend to adopt an imitational innovation strategy due to resource constraints, highlighting the importance of intellectual property rights protection for firm-level innovation.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Sean Kruger, Adriana Aletta Steyn
Summary: The purpose of this study is to explore how innovation mechanisms can be employed to foster stronger innovation capabilities within a university ecosystem, particularly in the African context. Using a case study methodology, the research findings reveal that innovation mechanisms such as makerspaces within universities provide critical support for innovation and collaboration.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Tiffany Hui-Kuang Yu, Kun -Huang Huarng
Summary: This research examines the causal complexity of Sustainable Development Goal (SDG) achievements by using popular indices as antecedents. The results show multiple relationships for both 2020 and 2021, and suggest that countries with different competitive advantages can choose the proper causal relationship to facilitate their own SDG achievements.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Kyriaki I. Kafka, Pantelis C. Kostis
Summary: Uncertainty has a significant impact on innovation outcomes, which is influenced by economic institutions. Advanced economies counter the negative effects of uncertainty through increased R&D spending, while developing countries struggle to mitigate uncertainty and require support from economic institutions to foster innovation production.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
Article
Business
Jesrina Ann Xavier, Md Imtiaz Mostafiz, Ganeshsree Selvachandran, Shio Gai Quek
Summary: This study uses artificial intelligence to analyze data and finds that ethnic entrepreneurial enterprises orchestrate resources to expand and develop their businesses during generational change. The study emphasizes the importance of resource orchestration for product innovation, market growth, and business development in ethnic entrepreneurial enterprises.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2024)
