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نتیجه جستجو - منطق فازی نوع 2

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector
رویکرد منطق فازی نوع 2 به هوش مصنوعی قابل توضیح برای انطباق با مقررات ، نتایج عادلانه مشتری و ثبات بازار در بخش مالی جهانی-2020
The field of Artificial Intelligence (AI) is enjoying unprecedented success and is dramatically transforming the landscape of the financial services industry. However, there is a strong need to develop an accountability and explainability framework for AI in financial services, based on a risk-based assessment of appropriate explainability levels and techniques by use case and domain. This paper proposes a risk management framework for the implementation of AI in banking with consideration of explainability and outlines the implementation requirements to enable AI to achieve positive outcomes for financial institutions and the customers, markets and societies they serve. The work presents the evaluation of three algorithmic approaches (Neural Networks, Logistic Regression and Type 2 Fuzzy Logic with evolutionary optimisation) for nine banking use cases. We review the emerging regulatory and industry guidance on ethical and safe adoption of AI from key markets worldwide and compare leading AI explainability techniques. We will show that the Type-2 Fuzzy Logic models deliver very good performance which is comparable to or lagging marginally behind the Neural Network models in terms of accuracy, but outperform all models for explainability, thus they are recommended as a suitable machine learning approach for use cases in financial services from an explainability perspective. This research is important for several reasons: (i) there is limited knowledge and understanding of the potential for Type-2 Fuzzy Logic as a highly adaptable, high performing, explainable AI technique; (ii) there is limited cross discipline understanding between financial services and AI expertise and this work aims to bridge that gap; (iii) regulatory thinking is evolving with limited guidance worldwide and this work aims to support that thinking; (iv) it is important that banks retain customer trust and maintain market stability as adoption of AI increases.
Keywords: Regulatory Compliance | Accountability and Explainability | Type-2 Fuzzy Logic | Neural Networks
مقاله انگلیسی
2 Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI
سیستم های منطق فازی نوع 2 یادگیری عمیق ترکیبی برای هوش مصنوعی قابل توضیح-2020
The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models.
Keywords: Explainable Artificial Intelligence | Interval Type-2 Fuzzy Logic System | Deep Learning
مقاله انگلیسی
3 An interval type-2 fuzzy logic based framework for reputation management in Peer-to-Peer e-commerce
یک نوع چارچوب بازه ای مبتنی بر منطق فازی نوع 2برای مدیریت شهرت در تجارت الکترونیک نظیر به نظیر-2016
During the last two decades, the Internet has changed people’s habits and improved their daily life activities and services. In particular, the emergence of e-commerce provided manufactures and vendors with more business opportunities. This allowed customers to benefit from a global, quicker and cheaper shopping environment. However, e-commerce is evolving from a centralised approach, where consumers directly purchase products and services from businesses, to a Peer-to-Peer (P2P) perspective, in which customers buy and sell goods amongst themselves. In P2P scenarios, it is crucial to protect both buyers and sellers (the peers) from being victimised by possible fraud arising from the uncertainties, vagueness and ambiguities that characterise the interactions amongst unknown business entities. For this reason, the so-called reputation models are becoming a key architectural component of any e-commerce portal. These systems are intended to evaluate the basic features of each entity (buyer, seller, goods, etc.) involved in a given trading transaction in order to assess the trust level of the given transaction and minimise fraud. However, in spite of their wide deployment, the reputation models need to be enhanced to handle the various sources of uncertainties in order to produce more accurate outputs which will allow to increase the trust and decrease the fraud levels within e-commerce systems. In this paper, we present an interval type-2 fuzzy logic based framework for reputation management in (P2P) e-commerce which is capable of better handling the faced uncertainties. We have carried out various experiments based on eBay®-like transaction datasets which have shown that the proposed type-2 fuzzy logic based system can provide better performance (in terms of malicious peer detection and exchanged message overhead) when compared to the other well-known and heavily used approaches like the eBay® approach, EigenTrust, PeerTrust as well as the type-1 fuzzy based counterpart approach.
Keywords: Type-2 fuzzy sets | E-commerce | Trust management | Reputation management systems
مقاله انگلیسی
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