An expert system for machine screws selection in engineering design

Development of a prototype expert system for identification and control of insect pests

Ameliorating the Pregnant Man problem: a verification tool for personal computer based expert systems

The requirements for corrosion expert systems have never been clearly stated, though many expert system developers must have had a “typical” user in mind, when designing their application. Reviewing some of the developed systems has shown that there exist quite some different approaches. In this paper, some general guidelines will be presented which pertain both to the development of expert systems in general, as well as to the development of expert systems for corrosion, in particular. Most of these guidelines stem from experience with the development of a large-scale expert system for materials selection and corrosion analysis, PRIME. The following topics will be discussed: concepts that affect design and construction, architectural issues, the selection of requisite expert system components in conjunction with user requirements, design issues affected by task type and organizational constraints.

Cost and gross profit calculations of agricultural products are a comprehensive and difficult process. Many different parameters, such as soil cultivation, pruning, spraying, irrigation, storage, drying, harvesting, transportation, and marketing, need to be evaluated together. Currently, cost calculations of agricultural products are made with classical programs and simple mathematical methods. For this reason, the results vary depending on the person and organization making the evaluation. This creates some limitations, making cost planning, which is a dynamic process, unreliable. In the study, a rule-based expert system was developed for the cost and gross profit calculation of apricots. The expert system developed is called Apricot Information System (APRIS). The developed expert system aims to provide more accurate solutions by adding the precision and speed of the computer to the calculation process. The main contribution of this study is the development of a complete decision support set covering all processes from tillage to marketing and the design of rule-based user-system interaction. The designed system found the apricot variable cost calculation to be 1126.36 TL/da for 2020. This value largely overlaps with the values obtained from the field in the previous year. The developed expert system will be a platform that can be used in cost calculations of many agricultural products.

Introduction. Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports. Methodology. Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases. Result. DsSafe gives satisfactory results.

Rule-based systems are relatively easier to explain to end users because rules can be expressed as if/else conditions. Indeed, at times there are multiple if/else conditions. Expert systems are deterministic in nature and very accurate, because they are driven by well-established rules and conditions. The need for explainability arises when multiple rules either conflict or are combined in such a way that it is difficult to interpret. In the field of artificial intelligence, an expert system is a program designed to simulate a real decision making process. Expert systems are designed to solve complex problems by simple reasoning and are represented as if/else/then conditions. Expert systems are designed to act like an inference engine. The role of an inference engine is to make decisions. Sometimes expert systems are simple, like a decision tree, and sometimes expert systems are very complex. Complex expert systems require a knowledge base. Sometimes these knowledge bases are an ontology, which is a form of a body of knowledge, through which a particular rule can be framed and inferencing can be done.

SUMMARY A microcomputer-based, Turbo Prolog expert system for selection of industrial trucks is described. Details of the industrial truck information supporting the development of the expert system are provided. The structuring of this information into a usable knowledge base and design of the inference mechanism to specifically address the truck selection problem are overviewed. Implementation details are discussed and the use of various AI concepts as applied within the expert system is described. Advantages and limitations of the expert system are suggested.

In this study, a part of a complete Expert System (ES) for powder technology is presented. It deals with the powder selection according to predetermined recommended or required material properties and specific powder characteristics. This leads to the determination of the most common powder production (i.e., processing) method, which satisfies the specified powder requirements for a specific application. For this purpose a detailed functional prototype expert system is developed using a rule-based knowledge representation model. The expert system is designed to acquire knowledge from the user then give recommendations. The inference engine, EXSYS-CORVID, is used as the development tool. The forward and backward chain technique is utilized, thus the reverse process is also be possible. Therefore, the system will determine or assign some primary powder characteristics and production method to satisfy or achieve the required product and vice versa. The developed expert system is flexible, easy to be implemented, modified and extended. The system displayed excellent performance and typical illustrative examples are shown. The proposed system is believed to assess in increasing productivity in the field of powder technology and increases the profitability and competitiveness of the process in a modern ever increasing industries.

DECADE - A hybrid expert system for catalyst selection

Computer-Aided Knowledge-Based Monitoring and Diagnostic System for Emulsion Polymerization

This paper presents a planning methodology using an application of a mathematical and a rule-based expert system (ES) to expand the transmission network in a deregulated environment. In this methodology, the ES suggests a realistic set of generation additions with proper economic signals to the participants, before proceeding with the transmission expansion. A feasible list of transmission alternatives is then assumed to accommodate the proposals for generation. A mathematical method is performed based on marginal cost allocation to optimize the location for the new generation and its transmission expansion scheme simultaneously for each alternative. The optimum alternative that minimizes the overall system's cost function and satisfies the future demand is obtained. The ES interacts with the power system planning tools to produce the optimum expansion plan. A practical application is given to demonstrate the effectiveness of the developed prototype system.

Background Large language models (LLMs) such as GPT-4 can interpret free text, but unreliable answers, opaque reasoning, and privacy risks limit their use in healthcare. In contrast, rule-based artificial intelligence (AI) provides transparent and reproducible results but struggles with free text. We aimed to combine the strengths of both approaches to test whether such a hybrid system can autonomously and reliably extract clinical data from diagnostic imaging reports. Methods We developed a neuro-symbolic AI that connects GPT-4 with a rule-based expert system through a semantic integration platform. GPT-4 extracted candidate facts from free-text reports, while the expert system verified them against medical rules, producing traceable, deterministic labels. We evaluated the system on 206 consecutive prostate cancer PET/CT scan reports, requiring extraction of 26 clinical parameters per report, generating 5356 data points, and answering three study questions: study inclusion, recurrent cancer identification, and prostate-specific antigen (PSA) level retrieval. Outputs were compared against physician-derived references, and discrepancies were reviewed by a blinded adjudicator. Results Here we show that neuro-symbolic AI outperforms GPT-4 alone and matches physicians in structuring and analysing reports. GPT-4 alone achieves F1 scores of 0.63 for study inclusion and 0.95 for recurrence detection, with 96.6% correct PSA values. Physicians reach F1 scores of 1.00 and 0.99, with 98.1% PSA accuracy. The neuro-symbolic AI scores twice 1.00 with 100% PSA accuracy and delivers always an auditable chain of reasoning. It intercepts two intentionally introduced reports with residual identifiers, preventing unintended transfer of sensitive data. Conclusions Unlike standalone LLMs, neuro-symbolic AI can safely automate data extraction for clinical research and may provide a path toward trustworthy AI in healthcare practice.Medical doctors often write reports as free text, which is hard to reuse for research or care. A large language model is software that reads and writes text by imitating large networks of brain cells. This type of artificial intelligence can extract and organize important information from medical reports. But its reasoning is opaque, answers can be wrong, and it raises privacy concerns. Rule-based artificial intelligence is transparent, responds correctly, and is privacy-protecting but struggles with free text. We combined both artificial intelligence types, so each offsets the other’s weaknesses. We tested the system on 206 prostate cancer imaging reports, where it extracted information correctly, showed how it reached its answers, and protected sensitive data. Pairing large language models with rule-based systems could make artificial intelligence safer, more trustworthy, and more useful in healthcare.

Artificial intelligence (AI) techniques using sophisticated computer software are increasingly being used in the analysis of biological systems and for the design of biological models. The term artificial intelligence refers to machine-based processes that are functionally equivalent to human cognitive tasks for analyzing data in the domain for which they were designed. They are considered intelligent because of human-like reasoning processes. The expert system and the artificial neural network are two examples of such techniques (1). We are in the process of adapting these new computer information processing techniques into a knowledge-based modelling system which will be used to selectively search, analyze, retrieve and display thermoregulatory data in response to operator requests. We have developed a prototype system in which a rule-based expert system integrates its knowledge base with our accumulated historical database of experimentally obtained information on thermoregulation to provide new retrieval and analysis capabilities which will be useful for both research and teaching. This enables you to address otherwise unmanageable problems. Heuristics are often used. These types of systems are able to work with uncertainty, reason about what is unknown, and handle multivalued information with multiple conclusions. They are very effective at handling the problems that they were designed to solve, and are excellent for situations in which many people require access to a body of knowledge. Expert systems or neural networks when used alone are not optimal. The deficits in each can be filled by designing an integrated hybrid system in which neural networks are embedded within the rule based expert system and so both synergistically fill the gaps in each others technology.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

The problem of verifying external adequacy in expert systems is addressed in this paper. External adequacy is defined in terms of a formal specification of question relevance, that expresses natural data dependencies among the external facts used in the ontology of the domain of expertise. A method is presented for verifying the expert system, at any stage of development, against the formal specification. The method is based on transforming the knowledge base plus inference engine into a partially ordered set of support graphs. The complex problem of verifying the external adequacy of expert systems is then translated into a simpler path-finding problem in support graphs.