Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance

Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow.Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow

Homeomorphism Problems of Fuzzy Real Number Space and The Space of Bounded Functions with Same Monotonicity on [-1,1]

In this paper, based on the fuzzy structured element, we prove that there is a bijection function between the fuzzy number space ε1 and the space B[−1, 1], which defined as a set of standard monotonic bounded functions with monotonicity on interval [−1, 1]. Furthermore, a new approach based upon the monotonic bounded functions has been proposed to create fuzzy numbers and represent them by suing fuzzy structured element. In order to make two different metrics based space in B[−1, 1], Hausdorff metric and Lp metric, which both are classical functional metrics, are adopted and their topological properties are discussed. In addition, by the means of introducing fuzzy functional to space B[−1, 1], we present two new fuzzy number’s metrics. Finally, according to the proof of homeomorphism between fuzzy number space ε1 and the space B[−1, 1], it’s argued that not only does it give a new way to study the fuzzy analysis theory, but also makes the study of fuzzy number space easier

NFT-based identity management in metaverses: challenges and opportunities

Abstract A considerable number of people worldwide start their second lives in the digital world soon. The 3D Internet reflects the digital world. Metaverse, the most famous example of the 3D Internet, is very popular and practical in people’s daily lives. However, combining Metaverse with newly-emerging technologies (e.g., blockchain) provides new user-friendly features such as autonomy, accessibility, removing central authorities, etc. Despite the mentioned attractive features, blockchain-based metaverses suffer various challenges, such as one user with multiple identities, certificate issuing for users in Metaverse, authentication-related issues, and arresting malicious users. Generally, identity management in a distributed environment where no central authority exists is a challenging issue. This study focuses on the challenge of distributed identity management in Metaverses to strike a balance between users’ privacy and regulation. The study proposes the use of Non-Fungible Tokens (NFTs) as a tool for managing identities in metaverses, as they are considered an excellent choice for this purpose. In addition to explaining the importance of this idea, this paper identifies its challenges, including distributed identity management, authentication issues, and security and privacy aspects. It then proposes possible solutions (e.g., using cryptographic tools). Despite existing challenges, there are many opportunities in the popularization of Metaverse, such as relying on blockchain technology, emerging many Metaverse-related jobs, in-Metaverse investments for huge revenues, and applying digital twins to provide realistic senses. This study also highlights the critical role of artificial intelligence (AI) in metaverses

Blockchain-based reporting protocols as a collective monitoring mechanism in DAOs

As a recent decentralized business model, Distributed Autonomous Organization (DAO) challenges the management paradigms of conventional organizations and represents the future of democracy-based systems. However, DAOs are facing challenges that can be divided into two categories, including policy-based and technical challenges. Detecting misbehaving nodes is a crucial challenge that is considered in both categories. Designing mechanisms to report malicious behaviors while maintaining anonymity is essential for the survival and efficiency of DAOs. In this paper, we clarify the problem, and introduce Blockchain-Based Anonymous Reporting (BBAR) as a collective monitoring mechanism in DAOs with its requirements and implementation methods

Provide appropriate services to potential customers by data mining techniques in e-banking area

During the past two decades, perspective of competition in the banking industry has been changed significantly. This is due to forces such as new laws, globalization, and grows of technology and becoming bank's services to products, and significant grows in customer's demands. Therefore banks try to offer appropriate service to potential customers, and new services offer with better analysis so that with customer behavior analysis, we can predict risk of new investment and obtain appropriate segmentation of customer demand for these services. With such as analysis, company can prevent additional cost of marketing and also increase acceptance of services. Whereas decision making environment made of different factors, we need models so we can analysis more variables and also enable capability of information provision for decision making. In this paper, we present a model for analyzing the behavior of customers with self-organizing map, and discovering association rule among bank's services

Internet of things empowering operations management; A systematic review based on bibliometric and content analysis

This paper reviews the Internet of Things' use and its impact on operations management through an analysis of the extensive literature. The purpose of this research is to analyze a collection of scientific articles (from a bibliographic and content perspective) that are indexed with the keywords ''Internet of things'' and ''Operations management'' in the Scopus and WOS databases. This study compiles and analyses 1623 articles using different approaches such as bibliometric, life cycle analysis, and text analysis to classify the Internet of Things applications in operations management. This research shows that 5 clusters of digitization, digital operations, monitoring system, tracking, and Smartification are controversial areas in Internet of things applications in operations management. Among the findings of this study, we can mention identifying the most constructive, influential, and contributing researchers, universities, and countries. This study increases scientific assistance in investigating the effects of the Internet of things on operational management

Bullwhip effect reduction map for COVID-19 vaccine supply chain

The growing COVID-19 virus pandemic outbreak causes an urgent need to produce its vaccine. Pharmaceutical companies would encounter a massive wave of unforeseen demand for the COVID-19 Vaccine after the vaccine production that could lead to a bullwhip effect in the COVID-19 Vaccine Supply Chain (CVSC). The main objective of this study is to design a cognitive map based on the influential factors on the Bullwhip Effect Reduction (BER) of CVSC. Hence, in the first step, the affecting factors on the BER of CVSC are identified and ranked based on their importance from the pharmaceutical experts using the AHP technique. In the second step, 13 out of 18 identified factors are considered for further analyzing and understanding their relationship by Fuzzy Cognitive Mapping (FCM) technique. Furthermore, three different forward scenarios and three backward scenarios are carefully constructed to find the optimal solution for BER on pharmaceutical organizations. The obtained results show that the flexibility factor is the starting point of the backward scenario, which reduces the bullwhip effect in CVSC. Beside, by improving the inventory management and reliability factor, it would be effectively possible to control the lead-time factor and consequently, overcome the bullwhip effect in CVSC

A New Intrusion Detection Approach Using PSO based Multiple Criteria Linear Programming

AbstractIntrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers