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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 primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

Expert systems technology as an area of artificial intelligence is coming to the field of structural mechanics. A number of expert systems have been developed or are under development. This paper consists of two parts. A brief discussion of the basics of expert systems and their concepts is given in the first part. The second part reviews the prototype of expert systems developed as an aid for finite element analysis and design optimization. Twelve different expert systems are described. A partial list of books on expert systems in general is given in the Appendix.

A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues.

The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given.

The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time.

WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.

In this work, the authors propose to harness the advantages of behavioral programming as a new technique for designing rule-based conversational agents.

To examine the study’s hypotheses, the authors perform a first-of-its-kind user study through which the authors examine how potential designers, both expert designers, computationally-oriented designers, and otherwise, leverage behavioral programming (BP) and dialog graphs for designing conversational agents (CAs). The authors also use two standard CA settings common in the literature: designing a CA representative for a user in an online dating service and a non-character player in a role-playing game (RPG).

The study’s results indicate that BP can be successfully utilized by computationally-oriented designers, with or without prior knowledge in CA design, and can facilitate the design of better CAs (i.e. more accurate and more robust). However, to capitalize on these potential advantages, designers may be required to devote more time to the design process and are likely to encounter higher temporal demand levels. These results suggest that BP, which was initially proposed and evaluated in the general context of software design, can constitute a valuable alternative to the classic rule-based CA design technique commonly practiced today.

An important limitation of this study is the relatively small participant pool. While the authors do plan to extend this study in the future, the current coronavirus disease 2019 (COVID-19) situation makes it ever more complex to conduct formal user studies of this kind. It is, however, important to note that despite the low number of participants, many of the results are found to be statistically significant.

The authors plan to continue this line of work and conduct human studies for additional design techniques in other popular agent-based settings. Specifically, the authors seek to explore how people of different backgrounds should design agents for various tasks such as automated negotiation (e.g. how should a person design a representative agent to negotiate on her behalf?) and social choice (e.g. how should a person design a voting bot to represent her in online voting systems?).

People are increasingly interacting with conversational agents in various settings and for a variety of reasons, as the market size of those agents keeps on growing every year. Through a first-of-its-kind human study (N = 41), consisting of both expert designers, computationally-oriented designers, and otherwise, the authors demonstrate a few key advantages and limitations of BP in the realm of conversational agents and propose its consideration as an alternative to the classic dialog graph technique.

Specialist physicians, specialist nurses and personnel in the intensive care unit (ICU) work to treat patients. Compared with other hospital units, ICUs are full-fledged and provide 24-h services. However, patients are admitted to this unit because of conditions such as severe trauma, organ failure or dysfunction and poisoning. The life expectancy of such patients is considerably low.

This study developed a reduced rule-based expert system to determine life expectancy. The purpose of this study is to determine the life expectancy of patients using ten variables. The rules were determined by evaluating 210 = 1,024 results. These rules were minimized using Boolean rules for simplification.

Conclusions were drawn by evaluating the minimized cases rather than evaluating 1,024 different cases. ICU patients were evaluated using this method, and the mean success rate was 63.75 per cent.

No rule-based expert system has been designed to determine the death risks of patients in the ICU.

The development of a rule‐based expert system (ES), driven by a discrete event simulation model, that performs dynamic shop scheduling is described. Based on a flowtime prediction heuristic that has been developed and base‐line runs to establish the efficacy of scheduling strategies such as shortest processing time (SPT), critical ratio, total work, etc., a rescheduling‐based dispatching strategy is investigated in a dynamic job shop environment. The results are discussed and analyzed.

This article describes how the n‐Cube expert system development tool can assist in item classification using the Universal Decimal Classification (UDC) standard. The n‐Cube makes use of a tree classification structure with associated rules and default inheritance features. Any information known about a particular classification is a combination of the defaults known about that class, as well as the defaults associated with any of its parent classes. As a result, many of the problems associated with simple rule‐based systems are overcome.

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.