The digitalization of the architecture, engineering, and construction (AEC) industry has demonstrated the revolutionary potential of integrating blockchain technology with building information modelling (BIM). However, the selection of the most appropriate blockchain solution is a multiple-criteria decision-making (MCDM) problem, which is usually influenced by conflicting criteria and deep uncertainty. To overcome this, the present study proposes an extended Fermatean fuzzy weighted aggregated sum product assessment (Extended FF-WASPAS) model. Unlike existing Fermatean fuzzy WASPAS (FF-WASPAS) methods, which are based on a single expert and may be biased, the proposed model incorporates the evaluations of multiple decision makers (DMKs) through a consensus-driven mechanism to ensure unbiased and accurate results. A case study is conducted to evaluate five leading blockchain platforms, Hyperledger Fabric, Polkadot, Tezos, Ethereum, and Algorand, under eight BIM-related criteria. The result indicates that Ethereum is the best blockchain platform to digitalize BIM compared to the other platforms because it is scalable, interoperable, secure, and has a wide range of applications in the real world. Sensitivity analysis over a wide range of parameter values, as well as DMKs assigned with different weight sets, confirmed the stability of the ranking. Furthermore, a quantitative comparative analysis with FF multiple criteria group decision-making (FF-MCGDM) and FF-WASPAS approaches, as well as a qualitative analysis with existing models in various fuzzy environments, confirms the robustness and reliability. Overall, the study provides a strong, interpretable, and consensus-based decision-support framework with high practical value for AEC stakeholders who wish to deploy secure, transparent, and efficient blockchain-enabled BIM solutions.
The rapid progress in artificial intelligence technologies in recent years has been largely driven by advances in reinforcement learning (RL). RL methods have proven to be highly effective in solving many practical problems. Distributed ledger technologies are finding wide application in the internet of things (IoTs), providing new approaches to solving problems of traditional IoT systems. Consensus is a fundamental component of distributed ledger technologies, responsible for ensuring data consistency between nodes, its security and accuracy. This paper is devoted to the study of the optimal choice of blockchain consensus protocol for IoT networks based on a combination of multi-criteria decision making (MCDM) and RL methods. The paper discusses the potential of merging MCDM and RL methods for selecting blockchain consensus protocols in IoT networks. It suggests a combined framework for effective protocol selection and management.
Open access
Blockchain Technology Applications and Security
Advanced Research in Systems and Signal Processing
Francisco J. Quesada, Francisco Moya, Mercedes Rodríguez-García, Bapi Dutta
Tendering processes aim to provide transparency in the trade of services or goods but often fall short, leading to corruption and loss of trust. The emergence of Distributed Ledger Technologies (DLTs), such as blockchain, has prompted research into their application for enhancing transparency in tendering. However, adopting DLT usually incurs extra costs, network fees, and high carbon footprints. This paper conducts a Multi-Criteria Decision Making (MCDM) process to select the most suitable DLT for tendering processes. As a result, a novel tendering process based on IOTA is proposed, which improves transparency, ensures ecological sustainability, and avoids extra costs. The IOTA-based approach also fosters collaboration between human and computer capabilities in selecting the tender winner. Our method is compared with existing approaches, demonstrating the highest transparency.
Abstract Decentralized finance is upending the financial system through innovative, open, and interoperable financial solutions. Decentralized finance is a rapidly emerging field based on distributed ledger technology. Decentralized banking protocols are witnessing a perfect storm in terms of growth. However, because these financial innovations pose specific risks to consumers, creators, regulators, and other stakeholders, this emerging subject demands careful investigation. The current study tries to categorize and rate the risks connected with decentralized finance. The current study seeks to identify the multiple risks associated with decentralized finance through a thorough literature analysis. Data gathered from specialists in prior research were incorporated into the study used for empirical analysis. As MCDM techniques, IVFF-based DEMATEL, AHP, and TOPSIS are first used, and then the IVFF-ARAS method and sensitivity analysis are used for performance evaluation and verification. The findings of this study have several ramifications for legislators, businesspeople, technologists, and practitioners. These stakeholders can concentrate on these weaknesses in the future and provide longer-lasting solutions.
Weizhong Wang, Yu Chen, Yi Wang, Muhammet Deveci · 6 authors
Abstract Many attempts have been made to identify barriers to blockchain adoption in supply chain; however, barriers to blockchain adoption in supply chain finance (SCF) are underexplored. This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant. We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants. To determine the barriers, we conducted a literature review. We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy (IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS (ranking sum) methods to determine the barrier weights. To evaluate the barrier level of each alternative participant in SCF, the integrated IVFF-RAFSI (Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval) model is presented to rank the barrier, which uses a power-weighted aggregation operator to fuse experts’ opinions. A case study demonstrates the practicality of the integrated IVFF-RAFSI model. The results show that uncertain and competitive markets (weighted at 0.0676) are the most significant barriers. This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption. Sensitivity and comparative analyses validate the steadiness and competency of the proposed model. These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.
This study explores the integration of Plithogenic Hypersoft Sets (PHSS) in Multi-Criteria Decision Making (MCDM) for cryptocurrency investment analysis. Given the volatile and unpredictable nature of the cryptocurrency market, traditional decision-making models often fail to capture the complexities and uncertainties present. By incorporating the advanced concept of PHSS, which accommodates multiple membership degrees (fuzzy, intuitionistic fuzzy, neutrosophic), we propose a more robust framework for investment decision-making. The study normalizes cryptocurrency data and applies PHSS to a set of cryptocurrencies (e.g., BTC, ETH, BNB, SOL, and XRP), analyzing key attributes such as price changes, market cap, volume, and circulating supply. Our results demonstrate that PHSS can enhance the accuracy and reliability of financial decision-making, providing valuable insights for investors.
Momna Akhtar, Muhammad Rahim, Agaeb Mahal Alanzi, Sadique Ahmad · 7 authors
This paper presents novel operational laws forp,q,r–spherical fuzzy sets (p,q,r–SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidatep,q,r–spherical fuzzy (p,q,r–SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical example is offered. Furthermore, we conducted a comparative study to validate the efficacy of the suggested approach. Finally, we discuss both the advantages and limitations of this innovative approach.
Environmental impact and sustainability challenges in the cryptocurrencies has become increasingly examined in the literature. However, studies of the multiple attribute group decision making (MAGDM) method for major selection of cryptocurrencies in advancing sustainability are still at an early stage. In particular, research on the fuzzy-MAGDM method in the evaluation of sustainability in cryptocurrencies is scarce. This paper adds contributions by developing a novel MAGDM approach to evaluate the sustainability development of major cryptocurrencies. It proposes a similarity measure for interval-valued Pythagorean fuzzy numbers (IVPFNs) based on whitenisation weight function and membership function in grey systems theory for IVPFNs. It further developed a novel generalised interval-valued Pythagorean fuzzy weighted grey similarity (GIPFWGS) measure approach to provide a more rigorous evaluation in complex decision marking problem with embedding ideal solution and membership degree. It also conducts a sustainability evaluation model of major cryptocurrencies as a numerical application and performs a robustness assessment with different variations of the expert's weight to test how different values of parameter θ can affect the ranking results of alternatives. The results suggest that Stellar is the most sustainable cryptocurrency, while Bitcoin with its intensive energy consumption, high mining cost and high computing power provides the least effective support for its sustainable development. A comparative analysis with the average value method and Euclidean distance method was performed to validate the reliability of the proposed decision-making model and provides evidence that the GIPFWGS has better fault tolerance.
The cryptocurrency, often known as virtual or digital currency, is one of the most significant breakthroughs brought about by digitalization. It has been mentioned recently, an innovative system for stockholders in particular. Cryptocurrencies of all kinds, including Bitcoin, Shib, Dogecoin, and Tether are decentralized. Categorization complicates decision-making (DM) as well as the transfer of uncertainty and digital currency validation. The multi-attribute group decision-making (MAGDM) method is defined in this article using the Frank weighted averaging and Frank weighted geometric aggregation operators (AOs). This research examines the uniqueness of the q-rung orthopair fuzzy hypersoft set (qROFHSS) that is responsive to volatilities, vagueness, doubt, and inaccurate data. Additionally, certain essential qROFHSS topics are covered in this study. The framework presented here is the greatest alternative for understanding electronic money. This research supports the difficulty of situations involving making choices that must take into account a variety of qualities and sub-attributes to select the best choice. We see that Bitcoin has a variety of applications and that cryptocurrencies have an excellent chance to emerge as a prominent benefit lesson in financial choice-making.
With the rapid development of e-commerce systems, the centralized service model gradually fails to meet the needs of SMEs. In the existing centralized e-commerce system, users’ transaction data and reputation scores are stored in a centralized cloud server, which has high storage cost, low processing efficiency, and the data is vulnerable to attacks and leaks. However, the existing decentralized e-commerce systems more than its reputation system to store the average credit score evaluation are receiving unfair evaluations against risk. The system of malicious nodes and no disciplinary measures, is not conducive to system development. To solve this problem, this paper proposes a blockchain-based decentralized e-commerce transaction system. The system commodity information is stored in the Interplanetary File System (IPFS) and the returned commodity addresses are stored in the blockchain to enhance the service performance. This paper proposes a reputation evaluation model based on multi-criteria decision making (MCDM), which can effectively resist unfair evaluation and collusion attacks, and proposes an incentive mechanism based on reputation value to reward and punish nodes, thus promoting the good circulation of the system. We implement the proposed system based on Ethereum. The experimental results show that the system has a small communication cost, accurately reflects the user’s reputation value, and has good availability and reliability.
Nada A. Nabeeh, Mohamed Abdel‐Basset, Abduallah Gamal, Victor Chang
A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production.
Ümit Cali, Muhammet Deveci, Shammya Shananda Saha, Ugur Halden · 5 authors
The modern power systems are evolving in parallel to the development of other technological trends such as decarbonization and digitalization. While the penetration of renewable energy resources is increasing within the national and regional energy mix, emerging digitalization technologies, such as artificial intelligence and blockchain technology are shaping modern power systems. Especially blockchain technology has a very high potential to disrupt the current and future energy sector landscape by enabling various use cases in this domain. This paper aims to prioritize different energy use cases where blockchain technology can actively be utilized to create additional value. This study proposes a Type-2 Neutrosophic Number (T2NN) based Evaluation based on Distance from Average Solution (EDAS) to evaluate and rank a set of existing use cases of an energy blockchain system. Testing and validation of the model is done through a comparison against one alternative T2NN based Multi-Criteria Decision Making (MCDM) model and an existing approach from literature. In addition, a sensitivity analysis is performed, revealing that changing criteria weightings do not affect the ranking order of the use cases of the energy blockchain system. Prioritizing the use cases can assist the companies, standardization bodies, and related government authorities to make better decisions for their operations, such as ranking the investment decisions.
Rana Muhammad Zulqarnain, Imran Siddique, Sayed M. Eldin, Shahid Hussain Gurmani
One of the substantial innovations achieved through digitalization is cryptocurrencies, also known as simulated or digital currencies, which have been deliberated in the modern era as a new platform particularly suitable for financiers. Several cryptocurrencies, such as Bitcoin, Ethereum, Binance Coin, and Tether, do not trust a dominant expert. The classification and conduction of insecurity and the confirmation of digital currencies complicate decision-making. q-rung orthopair fuzzy hypersoft sets are an emerging arena of research intended to report the confidential restrictions of q-rung orthopair fuzzy soft sets on multiparameter indefinite functions. Such a function maps a tuple of sub-parameters to a power set of the universe. It emphasizes allocating attributes to their corresponding sub-attribute values in disjoint sets. These structures sort it an innovative systematic tool for addressing the obstacles of hesitancy. The q-rung orthopair fuzzy hypersoft set (q-ROFHSS) expertly compacts with tentative and ambagious facts equated to the existing q- rung orthopair fuzzy soft set and Pythagorean fuzzy hypersoft set (PFHSS). It is the most compelling mode for enlarging imprecise data in decision-making (DM). This investigation’s ultimate impartiality is presenting interactional algebraic operational laws for q-ROFHSS. Furthermore, some interaction aggregation operators (AOs) have been anticipated via our proposed operational laws, such as q-rung orthopair fuzzy hypersoft interactive weighted average (q-ROFHSIWA) and q-rung orthopair fuzzy hypersoft interactive weighted geometric (q-ROFHSIWG) operators with their essential properties. In reality, a mathematical illustration of DM obstacles is pondered to substantiate the proven technique’s dominance. Based on the projected interaction AOs, robust multi-criteria group decision-making (MCGDM) design has been offered, which carries the most practical consequences associated with predominant MCGDM methods. The significance spectacle is that the intentional methodology is more operative and steady in bearing weird facts based on q-ROFHSS.
Salma Khan, Muhammad Gulistan, Nasreen Kausar, Sajida Kousar · 6 authors
One of the most important innovations brought by digitization is the cryptocurrency, also called virtual or digital currency, which has been discussed in recent years and in particular is a new platform for investors. Different types of cryptocurrencies such as Bitcoin, Ethereum, Binance Coin, and Tether do not depend on a central authority. Decision making is complicated by categorization and transmission of uncertainty, as well as verification of digital currency. The weighted average and weighted geometric aggregation operators are used in this article to define a multi‐attribute decision‐making approach. This work investigates the uniqueness of q‐rung orthopair fuzzy hypersoft sets (q‐ROFHSS), which respond to instabilities, uncertainty, ambiguity, and imprecise information. This research also covers some fundamental topics of q‐ROFHSS. The model offered here is the best option for learning about electronic currency. This study validates the complexity of decision‐making problems with different attributes and subattributes to obtain an optimal choice. We conclude that Bitcoin has a diverse set of applications and that crypto assets are well positioned to become an important asset class in decision making.
This study assesses supplier selection at the beginning of project management to establish an evaluation system corresponding to blockchain tracing anti-counterfeiting platforms (BTAP). First, this paper determines 20 evaluation criteria from the four dimensions of platform overview, core technology, application support, and operations management. On this basis, multi-criteria decision making (MCDM) based on customer needs is proposed, which consists of three main steps. First, quality function deployment (QFD) and the best and worst method (BWM) are used to evaluate the four dimensions of the BTAP and specific evaluation criteria from the perspective of customers to obtain the criteria weight. Then, this method uses the extended Vlse Kriterjumska Optimizacija I Kompromisno Resenje (VIKOR) approach to sort the alternatives. Finally, the improved decision making trial and evaluation laboratory (DEMATEL) method is used to analyse the relationships between the 20 criteria in the four dimensions. The feasibility and effectiveness of this method are verified by an example. According to the sensitivity analysis and comparative analysis, the results show that this method can evaluate blockchain anti-counterfeiting enterprises. The main conclusions are as follows: the core technology is the most important factor influencing the choice of a BTAP project, and the role of application support in evaluation cannot be ignored.
This study presents a novel multi-attribute decision-making (MADM) model on the basis of Pythagorean fuzzy linguistic information measures. To do so, we first present a new concept of Pythagorean fuzzy linguistic sets to describe fuzziness and inconsistent information, in which the Pythagorean fuzzy linguistic values (PFLVs) are represented by the linguistic membership degree and linguistic non-membership degree. Then, we introduce two axiomatic definitions of information measures for PFLVs, including Pythagorean fuzzy linguistic entropy and the Pythagorean fuzzy linguistic similarity measure, to measure the uncertainty degree of PFLVs and the similarity degree between among PFLVs. In addition, based on the logarithmic function, we construct two new information measure formulas and verify that they satisfy the axiomatic conditions of the Pythagorean fuzzy linguistic entropy and similarity measure, respectively. We further explore the relationship between the Pythagorean fuzzy linguistic entropy and similarity measure. Finally, we present a novel Pythagorean fuzzy linguistic MADM model with the Pythagorean fuzzy linguistic entropy and similarity measure. A numerical example of selecting the most desirable sustainable blockchain product is given, and a comparison with the existing approach was performed to validate the reliability of the developed decision-making model.
D. Nagarajan, M. Lathamaheswari, Said Broumi, J. Kavikumar
Blockchain Technology (BCT) is a growing and reliable technology in various fields such as developing business<br> deals, economic environments, social and politics as well. Without having a trusted central party this technology, gives the<br> guarantee for safe and reliable transactions using Bitcoin or Ethereum. In this paper BCT has been considered using Bitcoins.<br> Also Blockchain Single and Interval Valued Neutrosophic Graphs have been proposed and applied in transaction of Bitcoins.<br> Also degree, total degree, minimum and maximum degree have been found for the proposed graphs. Further, comparative<br> analysis is done with advantages and limitations of different types of Blockchain graphs.
The blockchain technology is a new innovation with the potential to disrupt the\nworld as we currently know it, despite several limitations and challenges to overcome.\nOne of these challenges for software producing organizations is selecting the\nright technology for their case. In this research, we haveve identified this selection\nprocess as a multi-criteria decision making problem. Based on this we have created\na Decision Support System which aids developers during the technology selection\nprocess of blockchain platforms. Contemporary solutions to this problem were only\nrather simplistic decision-trees, which struggle with complexity and adaptations.\nThe novelty of this Decision Support System lies in being a feature-based artifact\nwhich incorporates ISO Software Quality Aspects and feature prioritization based\non the MoSCoW-technique. These contemporary generic blockchain features have\nbeen gathered through nine interviews with blockchain experts. Based on prioritized\nfeatures as input, the Decision Support System gives a score for feasible solutions\n(e.g. Ethereum or Hyperledger) as result. This Decision Support System was\nevaluated in three different case-studies for organization creating blockchain-based\nsolutions. In addition to this, the artifact has been validated by a blockchain-domain\nexpert. The main difficulties and obstacles of this whole research were grounded in\nthe immaturity of the blockchain domain as a whole.
The decision of selecting the appropriate procurement/delivery system for large investment construction projects is a critical and challenging task for clients, and therefore a significant factor for the project’s success. Complex projects as electric power plants can involve managing multiple contracts or subcontracts simultaneously or in sequence. The aim of this paper is to develop, and analyze a decision support tool to select the most efficient procurement/delivery system for multiple contracts Combined Cycle Power Plants (CCPP) constructed in Egypt and funded by the publicsector. This process involved the identification of various procurement routes, followed by the utilization of quantitative values developed in accordance with the requirements of the multi-criteria decision analysis technique known as simple multi-attribute rating technique (SMART). Results revealed that the procurement/delivery system with the highest score, for all contractual packages, is the integrated project delivery method (IPD) under which other procurement/delivery methods could be utilized such as performance-based contracting (PBC), and construction management (CM). Further in this research, a sensitivity analysis approach was adopted to validate the IPD selection, and to determine the most critical criterion and the most critical measure of performance for each contractual package.
The minmax regret problem for combinatorial optimization under uncertainty\ncan be viewed as a zero-sum game played between an optimizing player and an\nadversary, where the optimizing player selects a solution and the adversary\nselects costs with the intention of maximizing the regret of the player. The\nexisting minmax regret model considers only deterministic solutions/strategies,\nand minmax regret versions of most polynomial solvable problems are NP-hard. In\nthis paper, we consider a randomized model where the optimizing player selects\na probability distribution (corresponding to a mixed strategy) over solutions\nand the adversary selects costs with knowledge of the player's distribution,\nbut not its realization. We show that under this randomized model, the minmax\nregret version of any polynomial solvable combinatorial problem becomes\npolynomial solvable. This holds true for both the interval and discrete\nscenario representations of uncertainty. Using the randomized model, we show\nnew proofs of existing approximation algorithms for the deterministic model\nbased on primal-dual approaches. Finally, we prove that minmax regret problems\nare NP-hard under general convex uncertainty.\n
The important energy requirements for the desalination process impose especially in autonomous and decentralized plants supplied by Renewable Energy Sources (RES). In this paper, five alternative energy generation topologies of Reverse Osmosis desalination process are evaluated. The proposed topologies assessed in terms of economic, environmental, technological and societal indices are compared using multi-criteria analysis, namely the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE). Ranking of topologies resulted in the selection of direct connection and hybrid configuration as optimum solutions. In case economic priorities prevail diesel generation should also be considered.