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The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.

One key issue of maintaining Web information systems is to guarantee the consistency of their knowledge base, in particular, the rules governing them. There are currently few methods that can ensure that rule bases management can scale to the amount of knowledge in these systems environment.

In this paper, the authors propose a method to detect correct dependencies between rules. This work represents a preliminary step for a proposal to eliminate rule base anomalies. The authors previously developed a method that aimed to ameliorate the extraction of rules dependency relationships using a new technique. In this paper, they extend the proposal with other techniques to increase the number of extracted rules dependency relationships. The authors also add some modules to filter and represent them.

The authors evaluated their own method against other semantic methods. The results show that this work succeeded in extracting better numbers of correct rules dependency relationships. They also noticed that the rule groups deduced from this method’s results are very close to those provided by the rule bases developers.

This work can be applied to knowledge bases that include a fact base and a rule base. In addition, it is independent of the field of application.

Expert systems are being effectively applied to a variety of engineering problems. A growing number of languages and development tools are available for their building. Expert systems building tools (shells) are not so flexible as the high‐level languages, but they are easier to use. The problem is that there are too many development tools on the market today, no standards for their evaluation are available, so it is quite difficult to choose the ‘best’ tool for the developer's/user's needs. This paper is an attempt to review the situation on the confused market. Eighty‐six development tools are described in a table form for easy comparisons. Tools implemented on the AI machines only are not included in this survey.

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

This study aims to evaluate whether the Big-4’s commenting efforts influence the characteristics of Financial Accounting Standards Board’s (FASB’s) Final_Standards using the content of their comment letters. Whether auditors lobby standard-setters to help their clients or to help themselves and whether they are successful are questions highly relevant to issues of auditor independence and audit effectiveness.

Based on components of Mergenthaler (2009), this study develops a rules-based continuum change score to measure how much more (less) rules-based a Final_Standard is compared to its exposure draft to evaluate the influence of the Big-4 on the FASB’s standard-setting for 63 accounting standards.

The findings show that extensive comment letters and increased uncertainty language are associated with increases in the rules-based attributes included in Final_Standards. These results suggest the Big-4 prioritize a reduction in their own litigation risk over the possible preferences of their clients for less rigid standards. Moreover, the results are consistent with their comment letters influencing the FASB’s decision to include more rules-based attributes in Final_Standards.

This study develops a potential proxy for audit risk by assessing the changes in the rules-based characteristics of proposed accounting standards and using the content of the comment letters to evaluate whether the Big-4 accounting firms may influence the FASB’s Final_Standards. Overall, this study provides a unique perspective on the influence of constituents on the FASB’s standard-setting.

The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation.

Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models.

Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs.

The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications.

The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

The purpose of this paper is to use the framework of rule-based and relation-based governance to examine the evolution of governance environment in the East Asian region including China, South Korea and Taiwan.

Both qualitative and quantitative evidences are presented to demonstrate the paths these East Asian countries take in their transitions from relation-based governance to rule-based governance. Based on the framework, this analysis sheds light on the debate on whether East Asian economies will eventually move away from relation-based governance to rule-based societies.

The authors find that relation-based governance has helped East Asian countries achieve rapid economic growth in the early stages of their development. However, as the scale and scope of East Asian economies expand, continuing to rely on it may hinder their further development and therefore these countries should adopt a rule-based governance system in order to be efficient and competitive in the world market. While South Korea and Taiwan have made substantial progress in this transition, China has just embarked on the process.

This paper is among the first to systematically review the theories and evidence of the transition and the challenges East Asian countries face during the process.