Search results

This chapter argues that the near-universal exclusion from the academy of the Shakespeare Authorship Question (or SAQ) represents a significant but little-understood example of an internal threat to academic freedom. Using an epistemological lens, this chapter examines and critiques the invidious and marginalizing rhetoric used to suppress such research by demonstrating the extent to which it constitutes a pattern of epistemic vice: that, by calling skeptics “conspiracy theorists” and comparing them to Holocaust deniers rather than addressing the substance of their claims, orthodox Shakespeare academics risk committing acts of epistemic vice, injustice and oppression, as well as foreclosing potentially productive lines of inquiry in their discipline. To better understand this phenomenon and its implications, the chapter subjects selected statements to external criteria in the form of the Association of College and Research Libraries’ 2015 Framework for Information Literacy for Higher Education, which provides a set of robust normative dispositions and knowledge practices for understanding the nature of the scholarly enterprise. The analysis reveals that the proscription against the SAQ constitutes an unwarranted infringement on the academic freedom not only of those targeted by this rhetoric, but – by extension – of all Shakespeare scholars as well.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

The aim of this book is to understand the causes and consequences of new scales and forms of territorial and spatial restructuring in a context of accelerated globalization by focusing on a diverse array of urban megaproject developments that, in various forms and with various objectives, are transforming the global urban landscape at the outset of the 21st century. The contributions to this volume explore the architectural design, planning, management, financing, and impact of urban megaprojects, as well as the social actors and innovations driving them. The contributions also articulate the various socioeconomic, political, and cultural causes and consequences of UMP development, thus providing a context to understand the reconfiguration of urban spaces in the new millennium.

In this paper we present some results of our exploratory research about Hungarian tender buyers activity and culture. We try to formulate some questions for future research. Hungarian bidding habits and behavior are not yet deeply researched. The goal of this pilot study is to take a snapshot of the Hungarian tendering processes from the point of view of the seller-buyer interaction. Content analysis has been chosen as the methodological framework because of its potential for examining not well structured, symbolic, mainly behavioral or qualitative data. The most important findings drawn from analysing 515 Hungarian calls for tender published in May and June 1996 are as follows: • ⊎buyers use a tendering process mainly if it is obligatory by law; • ⊎vernmental and institutional buyers represent more than 75 percent of bids; • ⊎prequalification tenders have a law rate; • ⊎projects are the most frequented objects of bids