Expert Decision Support System Research Articles

AUTOMATED SYSTEMS FOR DIAGNOSING DISEASES: A REVIEW OF EXISTING TOOLS

Diagnostic expert systems are computer-based decision-support systems that are designed to assist healthcare professionals in diagnosing diseases. They are designed to provide a list of possible diagnoses based on a patient’s symptoms, medical history, and other relevant information. The basic operation of a diagnostic expert system involves capturing information from a patient, such as symptoms and medical history, and using that information to generate a list of potential diagnoses. The system then uses knowledge-based techniques, such as decision trees and rule-based systems, to narrow down the list of potential diagnoses to arrive at a final diagnosis. This research paper provides an overview of expert systems, including their history, architecture, knowledge representation. It also discusses the advantages and limitations of expert systems, as well as their applications in different fields. There are many research papers studied and research analysis done. There have been numerous research studies in the field of systems for diagnosing diseases, particularly in the area of medical expert systems and decision support systems.

An approach to describing the courses of air enemy action using intelligent information technologies

The article analyzes the approach to describing the courses of air enemy action for further automation of the decision-making process for air enemy destruction. The analysis has shown that the development of air attack capabilities justifies the necessity to increase the level of efficiency in the decision-making process by responsible persons through the introduction of intelligent information technologies, such as expert systems and decision support systems, into the automation complexes of the Air Force. It is proposed to use the calculation of first-order predicates to formalize knowledge and aggregate it in the form of a knowledge base that will allow the decision maker to deploy his air defense forces and means to effectively destroy means of air attack.

Application of Decision Support Expert Systems for Improved gasoline yield in Refinery Catalytic Cracking

The Catalytic Cracking Unit (CCU) stands out as the primary factor responsible for enhancing gasoline production in the refinery. This paper provides a deep insight on the complexity of the factors within this unit, explore their broader implications, and offer a rationale for their technical significance. A survey approach involving the use of Principal Component Analysis (PCA) facilitated by StatistiXL software package and Kendall Coefficient of Concordance (KCC) to measure agreement among the respondents was employed. A total of 32 variables were identified, and questionnaires were developed using Rensis Likert's 5-point scale. These questionnaires were then administered to 100 respondents. Prior to this step, the Kendall Coefficient of Concordance was utilized to establish the sequential order of importance among these identified factors. The results obtained showed an index of agreement among the judges in ranking the variables W, as 0.54, the null hypothesis of disconcordance among the judges was rejected at a p-value of 0.01. The study identified 14 significant variables based on the KCC results, including Catalytic Cracking Charge, Mid Riser Temperature, Riser Inlet Temperature, Riser Outlet Temperature, and Stripper Level. Additionally, Principal Component Analysis (PCA) effectively condensed the set of 32 variables into four manageable clusters. The integration of data analytics techniques, such as KCC and PCA, alongside expert opinions and collected data enabled the assessment of the influence of independent variables on gasoline yield and the categorization of these variables. This study offers valuable insights for both academia and the industry, especially in the context of the Catalytic Cracking Unit.

Expert decision support system to improve the analysis of airframe noise measurements

For the localization and estimation of aeroacoustic sources on complex aircraft models in wind tunnel measurements beamforming and related methods are well known. The task of the aeroacoustic experts is to identify and classify the relevant sources within these beamforming maps. In this paper, we present an expert decision support system to support this task. The system comprises three steps: The first step identifies aeroacoustic sources in sparse beamforming maps and extracts their corresponding spectra without manual definition of regions of interest. The second step calculates the acoustic properties of the sources based on spectral and spatial information. The third step clusters the identified sources based on these acoustic properties. In this paper, data are evaluated based on CLEAN-SC beamforming maps of two airframe half-model wind tunnel measurements. A variety of aeroacoustic features are proposed that capture the characteristics of the source spectra. The result of this expert decision support system is a list of all relevant sources clustered according to their acoustic behavior at different Mach numbers. The aim of the system is to support aeroacoustic specialists in analyzing beamforming results and identifying relevant and spurious sources in-situ.

Application of a Stochastic Extension of the Analytical Design of Aggregated Regulators to a Multidimensional Biomedical Object

The results of the application of the methods of the synergetic control theory to a high-dimensional immunology object with uncertainty in its descriptions are reported. The control here is the therapy treated as a problem for constructing an optimal cure program. The control object is presented in continuous and discrete forms, i.e., mathematical models given by a system of ordinary differential equations with a bounded disturbance and a system of stochastic difference equations, respectively. Two algorithms for deriving robust regulators applicable to a 10-dimensional nonlinear multi-loop system with unstable limit states, which models an immune response to the hepatitis B infection, are obtained. Analytical control design for a continuous model relies on the method of nonlinear adaptation on the target manifold. The second algorithm represents a stochastic extension of the method of analytical design of aggregated discrete regulators minimizing the variance of the target macro variable. The numerical simulation of the developed control systems indicates the performance of the designed control algorithms. The results of this study can be used as a component part of the mathematical tools of expert systems and decision support systems.

Fuzzy Logic and Its Application in the Assessment of Information Security Risk of Industrial Internet of Things

This article addresses the issue of information security in the Industrial Internet of Things (IIoT) environment. Information security risk assessment in the IIoT is complicated by several factors: the complexity and heterogeneity of the system, the dynamic nature of the system, the distributed network infrastructure, the lack of standards and guidelines, and the increased consequences of security breaches. Given these factors, information security risk assessment in the IIoT requires a comprehensive approach adapted to the peculiarities and requirements of a particular system and industry. It is necessary to use specialized risk assessment methods and to take into account the context and peculiarities of the system. The method of information security risk assessment in the IIoT, based on the mathematical apparatus of fuzzy set theory, is proposed. This paper analyzes information security threats for IIoT systems, from which the most significant criteria are selected. The rules, based on which decisions are made, are formulated in the form of logical formulas containing input parameters. Three fuzzy inference systems are used: one to estimate the probability of threat realization, another to estimate the probable damage, and a final one to estimate the information security risk for the IIoT system. Based on the proposed method, examples of calculating the information security risk assessment in the IIoT environment are provided. The proposed scientific approach can serve as a foundation for creating expert decision support systems for designing IIoT systems.

Computational approaches for solving two-echelon vehicle and UAV routing problems for post-disaster humanitarian operations

During and after Hurricane Maria in 2017, the island of Puerto Rico faced an impossible situation: the transportation, power, and communication networks were all damaged, leading to numerous people being trapped. Humanitarian logistics service providers have the responsibility to reach as many affected populations as possible within short periods of time. Their extremely difficult operations are further hindered by these failures in our built environment. Failures in transportation mean that some routes cannot be used by conventional ground vehicles; failures in communication mean that demands are uncertain, and the locations of said demands may be difficult to pinpoint. In this work, we propose a novel two-echelon vehicle routing framework for performing many of the humanitarian logistics operations using aerial uncrewed autonomous vehicles (drones) to address the issues associated with these infrastructure failures. In our proposed framework, we assume that ground vehicles cannot always reach the trapped population directly, but they can transport drones from a depot to some intermediate satellite locations. The drones launched from these locations can serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present an expert decision support system, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem with trucks as the first echelon vehicles and drones as the second echelon vehicles. Our framework utilizes two types of drones. Hotspot or communication providing drones have the capability of providing cell phone and internet reception, and hence, are used to capture demands and their locations. Delivery drones are subsequently employed to satisfy these obtained demands. To handle demand uncertainty, we decompose the decision problem into two stages: providing telecommunications capabilities in the first stage, thereby capturing demands precisely, and satisfying the resulting demands in the second stage. To solve the resulting models, we propose a matheuristic computational approach in the form of a decomposition algorithm inspired by column generation to identify optimal drone routes. To showcase the application of our proposed framework, we present results from numerical experiments on datasets created to simulate the demand for medical aid in Puerto Rico after Hurricane Maria.

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Lumbar spinal stenosis (LSS) is a condition that affects the quality of life of the 3 vertebrae, the disc and the canal in the lower back. In this region, the nerves in the canal may be subjected to pressure for various reasons, and disease occurs. Surgical intervention is required to treat canal stenosis, and the exact location and size of the spinal stenosis is critical to the surgery. The UNet model, which is an example of this network, can be further deepened with various deep learning networks. In this study, it will be the basis for creating a system that helps in the diagnosis of spinal stenosis by using a deeper network. The ResUNET model using ResNet as the backbone achieved an average IoU of 0.987. This study demonstrated that expert decision support systems using MR images can be used in the diagnosis of LSS.

INTELLIGENT SYSTEMS FOR FORMING PROJECT TEAMS IN SAFETY-ORIENTED SYSTEMS

Human security project teams are formed in higher education institutions that train qualified civil protection specialists. One of the tasks is to increase the efficiency of forming SOS project teams by HR services, as well as to improve the recruitment and selection procedure using modern information technologies. Developing human resources, improving the quality of higher education for its applicants. Information systems provide high speed data processing, information search, recruitment optimization and candidate analysis. Automation of HR processes helps to reduce the labor intensity and resource costs of tasks. New models, methods, and technologies for processing materials and information about applicants for the purpose of evaluating them for further involvement in the SOS project will contribute to the sustainable development of the organization. The subject of the study is models for automating the selection of personnel in safety-oriented systems with their subsequent formation into project teams of the civil protection service and law enforcement agencies. One of the components of human resource management is the implementation of a subsystem for the assessment and selection of human resources, the initial elements of which are the basis for making personnel decisions on the formation of project teams. The purpose of the study is to formalize the criteria for selecting candidates to reduce the subjectivity of the assessment; optimize and automate the selection process, design expert decision support systems for HR. The article sets the following tasks: to design an information system for the formation of project teams in safety-oriented systems, taking into account the parameters of personal human qualities and professional skills of candidates using information expert systems. To improve the quantitative assessment and qualitative indicators of applicants, it is advisable to formalize the criteria and their weighting coefficients using the theory of qualimetrics and personnel assessment methods. The following research methods were used: system analysis, empirical methods, and project management methodology. The following results have been obtained: a model-scheme of an iterative approach to the development of an information system for human resource management for higher education institutions, taking into account specific learning conditions, has been developed. Conclusions. The methods of evaluation and selection of human resources, as well as the criteria for their selection in complex socio-technical systems are analyzed. The possibilities of automating HR processes and integrating an information system to support the formation of project teams in a safety-oriented system are considered. description of the life cycle of complex systems of a safety-oriented system, namely, the life cycle of such complex socio-technical systems is described, taking into account their characteristics and environment.

Современные тенденции управления знаниями в организации

The article analyzes the main trends in the field of knowledge management. Currently, the fourth industrial rev-olution, which results in the digitalization of many management and production processes, is having a signifi-cant impact on the functioning of organizations in various areas of activity. As part of this revolution, automation elements are being developed, expert systems for management decision support and artificial intelligence are being introduced. These processes require the creation of a knowledge management system in the organiza-tion. Its presence allows the company to form and build up human capital as one of the main intangible re-sources of enterprise development. The analysis of theoretical provisions on this issue, as well as the study of the practice of modern organizations allowed to identify the key trends in knowledge management. It is deemed that one of the urgent problems in this area today is the autonomous application of social and digital tools, which significantly limits their potential.

Voice Traces of Anxiety: Acoustic Parameters Affected by Anxiety Disorder

Although the emotions and learning based on emotional reaction are individual-specific, the main features are consistent among all people. Depending on the emotional states of the persons, various physical and physiological changes can be observed in pulse and breathing, blood flow velocity, hormonal balance, sound properties, face expression and hand movements. The diversity, size and grade of these changes are shaped by different emotional states. Acoustic analysis, which is an objective evaluation method, is used to determine the emotional state of people’s voice characteristics. In this study, the reflection of anxiety disorder in people’s voices was investigated through acoustic parameters. The study is a case-control study in cross-sectional quality. Voice recordings were obtained from healthy people and patients. With acoustic analysis, 122 acoustic parameters were obtained from these voice recordings. The relation of these parameters to anxious state was investigated statistically. According to the results obtained, 42 acoustic parameters are variable in the anxious state. In the anxious state, the subglottic pressure increases and the vocalization of the vowels decreases. The MFCC parameter, which changes in the anxious state, indicates that people can perceive this situation while listening to the speech. It has also been shown that text reading is also effective in triggering the emotions. These findings show that there is a change in the voice in the anxious state and that the acoustic parameters are influenced by the anxious state. For this reason, acoustic analysis can be used as an expert decision support system for the diagnosis of anxiety.

INTELLIGENT SYSTEMS FOR FORMING PROJECT TEAMS IN SAFETY-ORIENTED SYSTEMS

Human security project teams are formed in higher education institutions that train qualified civil protection specialists. One of the tasks is to increase the efficiency of forming SOS project teams by HR services, as well as to improve the recruitment and selection procedure using modern information technologies. Developing human resources, improving the quality of higher education for its applicants. Information systems provide high speed data processing, information search, recruitment optimization and candidate analysis. Automation of HR processes helps to reduce the labor intensity and resource costs of tasks. New models, methods, and technologies for processing materials and information about applicants for the purpose of evaluating them for further involvement in the SOS project will contribute to the sustainable development of the organization. The subject of the study is models for automating the selection of personnel in safety-oriented systems with their subsequent formation into project teams of the civil protection service and law enforcement agencies. One of the components of human resource management is the implementation of a subsystem for the assessment and selection of human resources, the initial elements of which are the basis for making personnel decisions on the formation of project teams. The purpose of the study is to formalize the criteria for selecting candidates to reduce the subjectivity of the assessment; optimize and automate the selection process, design expert decision support systems for HR. The article sets the following tasks: to design an information system for the formation of project teams in safety-oriented systems, taking into account the parameters of personal human qualities and professional skills of candidates using information expert systems. To improve the quantitative assessment and qualitative indicators of applicants, it is advisable to formalize the criteria and their weighting coefficients using the theory of qualimetrics and personnel assessment methods. The following research methods were used: system analysis, empirical methods, and project management methodology. The following results have been obtained: a model-scheme of an iterative approach to the development of an information system for human resource management for higher education institutions, taking into account specific learning conditions, has been developed. Conclusions. The methods of evaluation and selection of human resources, as well as the criteria for their selection in complex socio-technical systems are analyzed. The possibilities of automating HR processes and integrating an information system to support the formation of project teams in a safety-oriented system are considered. description of the life cycle of complex systems of a safety-oriented system, namely, the life cycle of such complex socio-technical systems is described, taking into account their characteristics and environment.

Evaluation of the efficiency of the expert medical decision making system in diagnosis acute appendicitis

Evaluation of the efficiency of the expert medical decision making system in diagnosis acute appendicitis

ABOUT ONE IMPLEMENTATION OF AN EXPERT SYSTEM ON THE ANDROID PLATFORM

The article presents the development of an expert system for decision support on the Android platform. To create a knowledge base, a medical subject area was chosen using the example of determining the risk of coronary heart disease in a practically healt

Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19

COVID-19 has impacted billions of people and health care systems globally. However, there is currently no publicly available chatbot for patients and care providers to determine the potential severity of a COVID-19 infection or the possible biological system responses and comorbidities that can contribute to the development of severe cases of COVID-19. This preliminary investigation assesses this lack of a COVID-19 case-by-case chatbot into consideration when building a decision tree with binary classification that was stratified by age and body system, viral infection, comorbidities, and any manifestations. After reviewing the relevant literature, a decision tree was constructed using a suite of tools to build a stratified framework for a chatbot application and interaction with users. A total of 212 nodes were established that were stratified from lung to heart conditions along body systems, medical conditions, comorbidities, and relevant manifestations described in the literature. This resulted in a possible 63,360 scenarios, offering a method toward understanding the data needed to validate the decision tree and highlighting the complicated nature of severe cases of COVID-19. The decision tree confirms that stratification of the viral infection with the body system while incorporating comorbidities and manifestations strengthens the framework. Despite limitations of a viable clinical decision tree for COVID-19 cases, this prototype application provides insight into the type of data required for effective decision support.

Ending the suitability quantification dilemma: intelligent decision support system for modular integrated construction in a high-density metropolis

PurposeModular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.Design/methodology/approachThe KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.FindingsThe tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.Originality/valueThis study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

Equivalent Exchange Method for Decision-Making in Case of Alternatives with Incomparable Attributes

The paper is focused on searching for novel methods aimed at improving the performance and usability of a common decision-making process where a panel of experts are assisted by specialized software systems. An equivalent exchange method (EEM) is considered in the paper as a potential candidate for a versatile method applicable in expert decision-making process for solving problems in various subject domains. The method is formally described in the paper in the form of an iterative algorithm where each iteration leads to the reduce in the number of alternatives under consideration until it converges to the preferable one. The key feature of EEM consists in the fact that the original comparison between multiple alternatives described by many attributes measured in different units is replaced by the sequence of simple exchanges between pairs of alternatives where only two attributes are engaged at once. The numerical example illustrating the full run of the algorithm is thoroughly described, so the actions performed in the steps of the algorithm are explained. The case of the successful implementation of EEM as the module of Expert Decision Support System is also presented.

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.

Traditional and perspective systems of maintenance and repair of traction rolling stock

Various systems of maintenance and repair of traction rolling stock have been studied using the example of multiple unit rolling stock, and their comparison has been made. The analysis of existing, historically formed systems of preventive maintenance, and perspective maintenance and repair systems based on technical diagnostics data is carried out.As an example, the COMPACS® system is considered, which is a universal system for complex monitoring of the technical condition of equipment, which has a flexible distributed parallel-serial architecture that provides high speed and accuracy of information processing. The main advantage of COMPACS® is the availability of an automatic expert decision support system. The expert system is designed for automatic diagnostics and prediction of the technical condition of the equipment. The task of the expert system is to assist the personnel in making the necessary informed decisions on the management of the operating mode and the state of the equipment. The system of work is described, as well as the scope and principle of operation of this complex monitoring system.The author comes to the conclusion that it is necessary to create an integrated repair system with a scientifically substantiated regulatory effect on equipment, taking into account the actual technical condition of the units and assemblies of the electric locomotive.

Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional Neural Network.

The attention deficit hyperactivity disorder has a negative impact on the child's educational life and relationships with the social environment during childhood and adolescence. The connection between temperament traits and The attention deficit hyperactivity disorder has been proven by various studies. As far as we know, there is no machine learning study to diagnose. The attention deficit hyperactivity disorder in a dataset created using temperament characteristics. Machine learning-based semi-automatic/fully automatic expert decision support systems are frequently used for the diagnosis of various diseases. In this study, it was aimed to reveal the success of a semi-automatic expert decision support system in the diagnosis of attention deficit hyperactivity disorder by using temperament characteristics. The high classification success achieved is a resource for a potential diagnosis of attention deficit hyperactivity disorder expert decision support system. In this respect, this study includes original qualities and innovations. Many different deep learning methods were used in the research. Deep learning methods are models that achieve high success by using a large number of images in various image processing competitions. The images of the signals in the data set were first obtained by Continuous Wavelet Transform. The highest classification success in our data set was obtained with the Squeeze Net model with 88.33%. The model we propose shows that an automatic system based on artificial intelligence can be created, as well as revealing the relationship between temperament characteristics in the diagnosis of attention deficit hyperactivity in the data set we created.