A Prototype Rule-Based Expert System for Travel Demand Management

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.

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

Various numerical techniques have been developed for modeling electromagnetic field propagation in various novel complex media. The validity of these techniques is usually verified by comparison to the exact solutions of canonical problems. Recently, research has focused on chiral media, a subclass of materials known as bianisotropic materials, and numerical techniques have been developed in order to calculate the interaction of electromagnetic fields with chiral objects. One canonical problem for these techniques is plane-wave scattering from a chiral sphere. This work presents a software package that displays and saves the calculated data for the scattering from a chiral, dielectric, or perfectly conducting sphere using a friendly graphical user interface (GUI).

TheGESTsoftware has been developed for the solution of molecular crystal structures from powder diffraction data. In this package, a genetic algorithm is used for global optimization to search for the structural model. For more efficient calculation time, a modified BraggRfactor is used as the evaluation function for the genetic algorithm. Applications of this program for structure determination of molecular crystals from powder diffraction data are demonstrated with known and previously unknown structures. A friendly graphical user interface (GUI) is used to generate the control file and run the program. TheGESTprogram with GUI, for Linux and Windows platforms, is freely available from the authors upon request.

An Expert System for Stress Management (ESSM) is a system that mimics the role played by a psychologist or counselor of stress expert to provide a virtual consultancy. Counselor always uses these systems manually to determine level of stress and personality interest of individual. However, ESSM is aimed to change these two very different manual systems into computerized one, by extracting data from a career test and relates them together with stress test to determine and recommend best stress management for particular individual based on their activity interest. ESSM is applied through psychometric method where user is prompted to complete two set of questionnaire. ESSM combines stress test and personality test that is extracted from Holland's Self Directed Search Model (RIASEC), which is used as Career Test by career counselor/consultant. Extraction from Holland's Model is aimed to determine human's behavior and interests which existing Websites only use stress test to determine stress disorder, and randomly recommend solutions for user, no matter what type of personality they are. ESSM contains recommendation based on interest activity by combining conclusion from both stress and personality test. Personality based on interest is extracted from RIASEC. The prototype of this system is rule-based expert system technique. The success of implementation and development of this project is expected to help in reducing time consumed and human power in determining best recommendation and solution for stress management.

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.

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.

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

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

Propagation prediction software for wireless communications is developed based on efficient ray-tracing simulation. The software has a friendly graphical user interface (GUI) and provides calculations for propagation parameters including path loss, delay spread, and angles of arrival/departure. Users can upload AutoCAD files of indoor or outdoor environments and assign wall geometries and electrical parameters easily. User defined radiation patterns can also be incorporated in the simulation. The software provides colored graphs for the simulated results.

Magnetoencephalography(MEG) is a functional, noninvasive technology for recording neuronal activity of human brain in real-time. MEG has been approved as an efficient and powerful functional detection method which has been used in clinical practices relate with epilepsy, depression, and other diseases that cannot be well-diagnosed by traditional structural methods, e.g., CT and MRI. By using MEG, very weak magnetic signals typically in the range of 10 femtotesla (fT)-1 picotesla (pT) that originate from electric currents flowing in neurons, can be recorded. Compared with scalp EEG, MEG provides much better spatial resolution. Meanwhile, MEG is a noninvasive technology for preoperative workup for epilepsy surgery, which means it may replace traditional invasive methods. As the development of high-sampling rate MEG systems as well as the discovery high-frequency oscillations (HFOs) in the brain, MEG is now typically digitally analyzed in very high-frequency range. Although MEG has been clinically used for two decades, it is not so widely-used as EEG. The lack of a powerful software platform is an important reason which hinders the development of MEG. A MEG-specific software, MEG processor, was released here. With the friendly graphical user interface (GUI) of MEG Processor, researcher can easily identify/mark HFOs and project the signal to MRI receiving a precise 3D reconstruction. We hope such a platform will help and encourage more clinicians to uncover the magnificent features of MEG.

Refinery advanced process control planning system

A constraint-based querying system for exploratory pattern discovery

The paper presents a graphical blockdiagram based programming tool, which is a new software development system for digital signal processing (DSP). This system provides a blockdiagram editor; therefore, the DSP system can be easily built by connecting functional blocks as described in the signal-flow graph. A block library is set up, which includes basic signal processing functions and some frequently used high level signal processing functions like FIR, IIR, FFT, etc. A friendly graphic user interface (GUI) based on the ‘X-Window’ system is developed to support each design step. Users can then improve their design easily according to the GUI's response. To guarantee the correctness of the circuits designed, the debugging and verifying capabilities are embedded in our system. The full system is combined with a structural silicon compiler, which we developed previously. Several designs will be illustrated as examples. It has been shown that this tool will be suitable for DSP system design by using a silicon compiler

Combining a neural network with a rule-based expert system approach for short-term power load forecasting in Taiwan