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Analysis of the publisher's behavior plays a vital role in identifying fraudulent publishers in the pay-per-click model of online advertising. However, the vast amount of raw user click data with missing values pose a challenge in analyzing the conduct of publishers. The presence of high cardinality in categorical attributes with multiple possible values has further aggrieved the issue.

In this paper, gradient tree boosting (GTB) learning is used to address the challenges encountered in learning the publishers' behavior from raw user click data and effectively classifying fraudulent publishers.

The results demonstrate that the GTB effectively classified fraudulent publishers and exhibited significantly improved performance as compared to other learning methods in terms of average precision (60.5 %), recall (57.8 %) and f-measure (59.1%).

The experiments were conducted using publicly available multiclass raw user click dataset and eight other imbalanced datasets to test the GTB's generalizing behavior, while training and testing were done using 10-fold cross-validation. The performance of GTB was evaluated using average precision, recall and f-measure. The performance of GTB learning was also compared with eleven other state-of-the-art individual and ensemble classification models.

The problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.

To overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.

Empirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.

The FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

This paper aims to explore the applicability of mudharabah (partnership) based crowdfunding as an alternative fund to support the book publishing industry, particularly for self-publishers and small publishers.

This is an exploratory qualitative study whereby the data are obtained from library research and empirical studies. As for empirical data, it is sourced from semi-structured interviews with three types of groups: the book industry, the crowdfunding platform and Shari’ah experts.

The study found that mudharabah crowdfunding could overcome the book publishing industry’s financial problems. However, this requires special requirements for applicants (writers or publishers) to avoid fraudulent cases, as well as committed management in running the platform and a substantial crowd of loyal funders to maintain the platform. Simultaneously, even though mudharabah is a risky instrument, the risk can be mitigated by closely monitoring the progress of the project. As a result, this study proposes a special framework for mudharabah based crowdfunding to fund self-publishers and small publishers in Malaysia.

This is an exploratory study, in which its findings may not be generalised due to the limited number of participants.

A special model for mudharabah based crowdfunding can be established through an online platform to support book publishing in Malaysia.

As this mudharabah crowdfunding model has the potential to support the book industry financially, it could also nurture talented young writers while also preserving knowledge.

This study highlights a fresh and in-depth discussion both in theory and practice in proposing a special Islamic crowdfunding framework based on mudharabah as an alternative fund for the book industry, particularly to support self- and small publishers.

Implementation of research evaluation policies based on neoliberal orientations of performativity has transformed higher education institutions globally, reshaping academic work and the academic profession. Most lately, the mantra of “publish or no degree” has become the norm in many contexts. There has been little empirical research into the unintended consequences of this neoliberal academic performativity for inexperienced researchers. This article focuses on the role institutional research evaluation policies play on doctoral students and early-career doctoral graduates’ publication practices and on their decision to sometimes publish in journals with ethically “questionable” publishing standards in particular through the concept of figured worlds.

The study was conducted in a higher education setting employing a variety of research incentive schemes to boost research productivity where “publish or no degree” policy is the norm. It employs qualitative approach and involves in-depth interviews with nine doctoral students and seven early career academics who have been working part-time or full-time for five years following PhD completion.

Findings demonstrate publishing in journals with ethically “questionable” publishing standards is not always simply the result of naivety or inexperience. Some authors choose these journals in order to retain a sense of self-efficacy in the face of rejection by more highly ranked journals. Under institutional pressure to publish, they are socialized into this “shadow academia” through (existing) academic networks, conferences and journal special issues.

It is often assumed that scholars are trapped into “questionable” journals through the use of unsolicited emails. This paper challenges this assumption by demonstrating the crucial role research evaluation policies based on neoliberal orientations of performativity and contextual dynamics play on the publication practices of doctoral students and early-career doctoral graduates on their decision to submit to journals with “questionable” publication practices. It introduces the concept of unethical publication brokering, an informal network of ties promising fast and easy publication in outlets that “count”.

This study aims to assess the relationship between internal control weakness and different types of auditor opinions in fraudulent and non-fraudulent firms. The study's main objective is to investigate fraud in business firms and analyze internal controls and types of proposed opinions by the auditor about his desired firm. The outbreak of fraud in firms is of utmost importance to a broad spectrum of society. Internal controls and the auditor's role in preventing and detecting frauds should not be taken for granted.

The present study's statistical population includes 179 listed firms on the Stock Exchange selected as the study sample using the systematic elimination method during 2012–2019. As the study's dependent variable (the type of auditor’s opinion), research hypotheses were analyzed using the Logit regression model.

The results show that the relationship between internal control weakness and opinion type is significantly different in fraudulent and non-fraudulent firms. Moreover, the relationship between internal control weakness and type of auditor opinion in fraudulent firms and the relationship between internal control weakness and type of auditor opinion in non-fraudulent firms are significant.

By assessing the related literature, the authors have found no study to directly assess the comparative relationship between internal control weakness and the type of auditor opinion, which can be named as the main objective of the study.

The purpose of this study is to explore how the risk of management motives for fraud can be assessed in external audits.

Semi-structured interviews were conducted with 26 experienced external auditors to explore their perspectives on the methods they employ to assess the risk of management motives for fraud.

The study identifies six methods external auditors can use to assess management motives for fraud. It emphasises that assessing management motives requires auditors to go beyond understanding these motives and necessitates a sceptical and analytical mindset. Auditors need to identify the accounts most vulnerable to management manipulations, observe management attitudes and assess the credibility of management assertions. The auditors in this study highlight specific accounts frequently manipulated by management. Still, manual year-end journal entries are the most vulnerable to management manipulations as they are subject to fewer controls. They recommend increasing the sample size to 100% and assigning more experienced staff, particularly, those with qualifications in fraud examination or anti-fraud training, to audit these vulnerable accounts thoroughly. They also provided examples of how auditors can identify management motives for fraud, observe management attitudes and assess the credibility of management assertions.

Audit standards (e.g. ISA 240, SAS99) lack explicit guidance on assessing management motives for fraud, but auditors are required to consider it in fraud risk assessment. This study proposes guidance recommendations to improve auditors' ability to assess this risk, which could be integrated into professional audit standards and training materials to improve auditors' professional scepticism, ability to challenge management and skills in fraud risk assessment.

Assessing the risk of management motives for fraud in external audits has received limited attention in the literature. To the best of the authors’ knowledge, this study is the first to address this knowledge gap.

This guide is compiled in order that banks may see the extent of the overall problem of fraud and money laundering in documentary credit transactions. It also contains advice on how banks and bankers may protect themselves and their staff from the consequences of fraudulent attacks against the system.

Academic corruption and fraudulent practices have become problematic in recent years. Governments around the world have introduced dedicated higher education commissions to regulate higher education providers. The purpose of this paper is to design a system for the detection and prevention framework of fraudulent behaviour in higher education.

This paper performs a survey on academic misconduct practices and expands the survey by analysing the accreditation process. This study further identifies common corrupt practices in the accreditation process with reference to particular accreditation standards or laws. If the accreditation process is as thorough as, this paper is led to believe, a higher institute may stop being compliant immediately after the accreditation process. playing a catch-me-if-you-can at the next accreditation cycle. The survey of the accreditation process and identification of corrupt practices lead to an identification of preventative and detective measures.

The review of accreditation procedures and conditions identifies that fraudulent practices can occur at every part of any policy and procedure. The framework prevents repudiation and allows for spontaneous investigations internally and externally. The blockchain prevented changes to the system and allow for auditing of changes. A system such as this could suppress accreditation fraud and minimise its corrupt impact. Not to mention identify with relative ease the severity and life of corrupt practice.

Contributions are made in the framework for detecting and preventing corrupt practices in Higher Education using blockchain immutable transactions. This enables real-time accreditation compliance checks and monitoring of conditions. External complaints or reviews can be conducted with minimum interactions from higher education providers.

This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection.

Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection.

The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud.

A cross-sectional design is a limitation.

NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks.

The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field.