Knowledge-based Expert Research Articles

PharmiTech: Addressing Polypharmacy Challenges through AI-Driven Solutions

Due to the rising prevalence of polypharmacy, pharmacists face more challenges in ensuring patient safety and optimizing medication management. This paper introduces PharmiTech, a Clinical Decision Support System that leverages Artificial Intelligence (AI) to tackle the growing need for efficient tools to assist pharmacists. The primary focus of the tool is to identify possible herb-drug interactions and instances of prescription drug abuse, combining an expert knowledge base with a supervised classification model and providing user-friendly alerts to pharmacists. To demonstrate the capabilities of the developed tool, this paper presents its functionalities through a case study involving simulated scenarios using de-identified information to maintain the confidentiality of real patients’ personal data. Tested in Portuguese pharmacies, PharmiTech enhances pharmaceutical care, safeguards patient data, and aids pharmacists in informed decision-making, making it a valuable resource for healthcare professionals.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Pengembangan Aplikasi Sistem Pakar Preferensi ‘Bahasa Cinta’ Manusia Berbasis Web

The purpose of this research is to produce a web-based expert system application to identify a person's love language using the nave Bayes classifier method. This expert system application to identify a person's love language is intended for people who have not realized how important it is to know which love language is relevant to them. This expert system application to identify a person's love language provides guidance in the form of questions about the type of love language. The answers to the questions asked will lead to the final decision on the results of the expert system diagnosis. Sources of expert system application knowledge base obtained from books and the internet. In making the application using the extreme programming software development method. Extreme programming consists of four stages, namely: planning, design, coding, and testing. The result of this research is the application of an expert system of love language identification that works on a web base.

A Demonstration of TENDS: Time Series Management System Based on Model Selection

The growth in sensor technologies, IoT devices, and information systems has opened up new opportunities for managing time series data across various domains. Despite significant progress, existing time series management systems face two crucial limitations: insufficient functionality and inadequate adaptability. This highlights the need for more comprehensive systems that not only improve data quality and analysis but also effectively manage the variety and volume of time series data. This paper presents TENDS, a time series management system based on model selection. TENDS uniquely combines advanced functionalities for imputation, prediction, and anomaly detection, offering a comprehensive analysis of time series data. It features (i) an effective model selection mechanism to adapt to various data types and to improve efficiency; (ii) fourteen state-of-the-art prediction methods and three state-of-the-art imputation methods; and (iii) a dynamic expert knowledge base for anomaly detection, evolving continuously with new data to ensure accuracy. TENDS boasts a comprehensive suite of visualization tools. With its configurable offline and online interfaces, TENDS (i) provides extensive flexibility in model selection and parameter adjustment, (ii) facilitates easy visualization of training results, and (iii) supports real-time documentation and statistical analysis of time series.

Модель процесса формирования каталога сценариев для анализа и оценки функциональной безопасности автоматизированных систем вождения автомобилей

In the development of highly automated and unmanned vehicles, the auto industry is faced with a new set of problems associated with the need to standardize elements of automated driving systems and determine the rules for their testing and certification. Due to the acceleration of scientific and technological progress and the development of new technologies, previously used standards began to become obsolete as the practice of their application accumulated, and sometimes even before the approval of a particular technical regulation. In these conditions, a promising technical and legal solution may be the use of fuzzy logic in tools for aggregation and management of expert knowledge in the processes of certification and testing of highly automated vehicles and elements of their automated control systems. Fuzzy decision-making models use typical fuzzy situations that form catalogs of scenarios for testing vehicles and their systems. In this way, an expert system knowledge base can be formed in which knowledge engineers apply a set of test scenario or experiment parameters for testing and simulation. Determining the parameters of a new scenario, their similarity with previously formalized scenarios and the decision to include a particular scenario in the scenario catalog remains with the experts. The approaches used at the present stage to the formalization of expert knowledge are not acceptable for creating knowledge bases controlled by big data management systems or artificial neural networks, which are in the near future in the development of expert systems. The article proposes a method by which a knowledge engineer administering an expert system can automate the creation of a catalog of “fuzzy” test scenarios and simulation modeling of systems, by automatically searching for the maximum value of the states of these systems belonging to maps and catalogs of scenarios, with a probability specified by the expert, with using machine learning methods.

Expert System for Extracting Hidden Information from Electronic Documents during Outgoing Control

For confidential and sensitive electronic documents within enterprises and organizations, failure to conduct proper checks before sending can easily lead to incidents such as security degradation. Sensitive information transmission has become one of the main ways of internal data leakage. However, existing methods or systems cannot extract hidden data and do not support mining the binary structure of hidden information in files. In this paper, an expert system for mining electronic document hidden information is designed for various office documents, compressed files, and image files. This system can quickly mine various forms of concealment in more than 40 common types of electronic documents and extract hidden information, such as file type tampering, encryption concealment, structure concealment, redundant data concealment, etc. Additionally, feature information in the binary structure of the document is extracted to form a feature information base. Subsequently, an expert knowledge base is constructed. Finally, a hidden information mining engine is designed using the knowledge base to realize the security control of corresponding outgoing files with good expansibility and integration. By controlling the exit of documents through scanning for sensitive information contained within them, the security level contents can be obtained effectively, avoiding data leakage by technical means while also facilitating forensics. The actual test result proves that this system can quickly mine various means used to conceal information, extract their respective information, and provide a fast, practical diagnostic way for outgoing control over these electronic documents.

MODIFICATION OF 3D BALANCE MODEL FOR BI-POLAR AGGREGATION OPERATORS VISUALISATION

Aggregation operators provide an overall assessment of an object or a process which is described with several quantitative criteria. An aggregation operator is constructed on the basis of expert knowledge and can take into account the types of criteria interdependencies, the partial order of criteria importance, the desired overall scores for distinct sets of the criteria values provided by an expert. However, in practice the aggregation operator construction process and its result appear to be difficult to perceive and understand by an expert. There is a balance model method for visualizing the aggregation operators in order to simplify evaluation of their conformity to expert preferences. This method allows visualizing an aggregation operator in an intuitively clear way as a mechanical system of weights on a rod. One limitation of the balance model method is that the weights can overlap each other on a rod which makes it difficult to perceive the aggregation result by the expert. A three-dimensional balance model overcomes this limitation but it can visualize unipolar aggregation operators only. This paper extends the existing three-dimensional balance model for bipolar aggregation operators. The extended model allows visualizing the aggregation operators of the criteria defined on bipolar scales of three different types: symmetric bipolar scales, homogeneous bivariate bipolar scales, heterogeneous bipolar scales. The model plane rotation angle constraints were updated to visualize the bipolar aggregation operators. The implementation of the extended model in the Unity game engine is presented. The application of the extended model is illustrated in a case study of the energy storage technology selection considering a trade-off between its performance and its environmental impact.

Optimizing Sensor-Controlled Systems with Minimal Intervention: A Fuzzy Relational Calculus Approach

This article describes an approach for optimizing sensor-controlled systems through minimal intervention, utilizing fuzzy linear systems of equations (FLSEs). Starting with a generalized model of the system behavior, we incorporate an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem faced by FLSEs. By achieving minimal intervention, we ensure that the system adjustments are effective, economically optimal, and non-intrusive. A MATLAB-based implementation is presented.

The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.

A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hypertension and some cancers. Advances in information computer technology offer the opportunity to provide personalised lifestyle advice directly to the individual through devices such as smartphones or tablets. The overall aim of the PROTEIN project (Wilson-Barnes etal., 2021) was to develop a smartphone application that could provide tailored and dynamic nutrition and physical activity advice directly to the individual in real time. However, to create this mobile health (m-health) smartphone application, a knowledge base of reference ranges for macro-/micronutrient intake, anthropometry, biochemical, physiological and sleep parameters was required to underpin the parameters of the recommender systems. Therefore, the principal aim of this emerging research paper is to describe the process by which experts in nutrition and physiology from the PROTEIN consortium collaborated to develop the nutritional and physical activity requirements, based upon existing recommendations, for 10 separate population groups living within the EU including, but not limited to healthy adults, adults with type 2 diabetes mellitus, cardiovascular disease, excess weight, obesity and iron deficiency anaemia. A secondary aim is to describe the development of a library of 24-h meal plans appropriate for the same groups and also encompassing various dietary preferences and allergies. Overall, the consortium devised an extensive nutrition and physical activity knowledge base that is pertinent to 10 separate EU user groups, is available in 7 different languages and is practically implemented via a library of culturally appropriate, 24-h meal plans.

Investigating Precise Decision-Making in Greenhouse Environments Based on Intelligent Optimization Algorithms

The precise control of a greenhouse environment is vital in production. Currently, environmental control in traditional greenhouse production relies on experience, making it challenging to accurately control it, leading to environmental stress, resource waste, and pollution. Hence, this paper proposes a decision-making greenhouse environment control strategy that employs an existing monitoring system and intelligent algorithms to enhance greenhouse productivity and reduce costs. Specifically, a model library is created based on machine learning algorithms, and an intelligent optimization algorithm is designed based on the Non-Dominated Sorting Genetic Algorithm III (NSGA-3) and an expert experience knowledge base. Then, optimal environmental decision-making solutions under different greenhouse environments are obtained by adjusting the greenhouse environmental parameters. Our method’s effectiveness is verified through a simulated fertilization plan that was simulated for a real greenhouse tomato environment. The proposed optimization solution can reduce labor and time costs, enable accurate decision-making in the greenhouse environment, and enhance agricultural production efficiency.

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Cobot selection using hybrid AHP-TOPSIS based multi-criteria decision making technique for fuel filter assembly process

The choice of a suitable collaborative robot (cobot) for a real-time industrial process is one of the obstacles to effective robot implementation in terms of energy and cost. The cobot selection process for an application have become more complex due to increasing sophisticated features and capabilities in cobots offered by the manufacturers. The paper presents a hybrid Multi-Criteria Decision-Making (MCDM) technique based on Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches for selecting cobots for fuel filter assembly operation. The product design methodology, manufacturing method, and associated cost are directly influencing the decision on cobot selection. The most appropriate robot to accomplish the desired task at the lowest possible cost and capability can be selected by AHP with prospective criterion weight for subsequent processing. The TOPSIS approach orders alternatives based on the prominence of criteria. A diesel fuel filter assembly process case was considered for validating the proposed technique of cobot selection process. An expert knowledge base was generated for 12 advocated cobots and the best cobot was selected using the proposed hybrid MCDM technique.

ESUAV-NI: Endogenous Security Framework for UAV Perception System Based on Neural Immunity

Unmanned aerial vehicles (UAV) represent an essential component of advanced intelligent equipment that can be used as an aerial perception system by installing various sensors such as vision, hearing, touch, taste and smell to achieve intelligently integrated perception of environments. However, these perception system with environmental information may be threatened by various internal and external attacks, causing a great challenge to the security of UAV. The original security system relied on an expert knowledge base to prevent attacks, but the weaknesses of lacking proactivity and flexibility are gradually exposed. The strong resistance and survivability of biological systems can be used to fill this capability gap and provide new ideas for the security of UAV perception system. Therefore, an endogenous security framework (ESUAV-NI) based on Neural System (NS) and Immune System (IS) is proposed in this study. Through breeding artificial intelligence (AI) vaccines and distributed neural hierarchical control, achieving security protection for UAV perception system. Moreover, we evaluated the AI vaccine breeding approach in the ESUAV-NI by conducting extensive experiments on internal threats and external aerial imagery camouflage data, respectively. The results show that the proposed approach has superior performance for UAV perception system.

Determination of Anchor Drop Sequence during Vessel Anchoring Operations Based on Expert Knowledge Base and Hydrometeorological Conditions

Presently, the most common technique for maintaining a ship’s location is dynamic positioning, which uses a series of thrusters to hold its position. This method is resilient to moderate hydro-meteorological conditions, eliminating the need for extensive preliminary steps before initiating positioning operations. An alternative approach involves station keeping using a set of anchors, where thrusters are not employed, necessitating careful planning of the anchorage in light of hydro-meteorological conditions. Presently, in vessels using this anchoring method, the captain determines the order of anchor drops, taking into account the prevailing weather conditions, the ship’s maneuvering abilities, and vessel capability plots. This article introduces a novel algorithm that uses sensor-acquired weather data and a cognitive knowledge base to establish the best sequence for anchor drops. This innovation represents a significant stride towards the automation of the anchoring process. By using the anchorage planning algorithm presented in this publication, it has been possible to reduce the time required for anchor deployment by about 52%, due to the preparation of the anchor deployment strategy in port. A reduction in energy consumption of about 8% was also achieved.

Technical research on rapid source term calculation and prediction technology of nuclear accidents based on Bayesian network

ABSTRACT How to evaluate and predict potential source terms quickly and effectively is very important for emergency response. In this paper, the traditional forward analysis calculation based on deterministic method was combined with probability theory calculation, and a large number of typical accident sequences were calculated with severe accident analysis software to obtain data of source terms. According to expert knowledge base, probabilistic safety analysis, and other information, a model of nuclear power plant was established based on Bayesian network algorithm, so as to carry out researches on rapid source term calculation and prediction methods. Based on the Bayesian network model of a specific power plant and the pre-calculated source term results of typical accident scenarios, the best available knowledge and observation results of events were used to estimate the relevant typical source terms and their possibilities, which provide a strong basis for effectively grasping and predicting the accident process, quickly predicting source terms and release characteristics of radioactive materials, and quickly and accurately evaluating the radioactive consequences.

Using fuzzy logic to compare species distribution models developed on the basis of expert knowledge and sampling records

BackgroundExperts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations.ResultsModels based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species.ConclusionsFuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

Weighing Geophysical Data With Trans‐Dimensional Algorithms: An Earthquake Location Case Study

AbstractIn geophysical inverse problems, the distribution of physical properties in an Earth model is inferred from a set of measured data. A necessary step is to select data that are best suited to the problem at hand. This step is performed ahead of solving the inverse problem, generally on the basis of expert knowledge. However, expert‐opinion can introduce bias based on pre‐conceptions. Here we apply a trans‐dimensional algorithm to automatically weigh data on the basis of how consistent they are with the fundamental hypotheses made to solve the inverse problem. We demonstrate this approach by inverting arrival times for the location of a seismic source in an elastic half‐space, assuming a point‐source and uniform weights in concentric shells. The key advantage is that the data do no longer need to be selected by an expert, but they are assigned varying weights during the inversion procedure.

Using Descriptive Logic in Intelligent Educational Systems

The purpose of the article is to study and solve the problems of using the basic principles of the modern formalism of descriptive (terminological) logic in intelligent learning systems that use knowledge bases of educational content. The research methods are the main methodological approaches and technological tools for the development of intelligent learning systems. In particular, systemic and comparative analyses to identify the features of intelligent learning systems, the method of expert evaluation, which involves the analysis of literature and information resources, interviews and surveys of experts in the field of information technology and mathematical logic, as well as the process of developing and testing an intelligent system. The scientific novelty of the study is the development of a system for obtaining expert knowledge for an intelligent system that combines the formation of an expert knowledge base, and the formalism for representing which is descriptive logic. The proposed approach of formalized knowledge representation using descriptive logic can be used in the development of authors’ intelligent learning systems in the field of online education and cognitive skills development. Conclusions. The scope of application of descriptive logic in intelligent systems is constantly expanding, increasing the capabilities of these systems to formally derive new knowledge, including in conditions of incomplete certainty. Descriptive logic, used in intelligent learning systems, provides ample opportunities for building a conclusion (i.e., forming an answer), in particular in situations such as forming and answering various types of queries to the system’s knowledge base (e.g., fully defined and clearly formulated, incompletely defined and vaguely formulated, completely vague, incompletely defined and clearly formulated, completely vague, completely vague and vaguely formulated), helping to specify the query. To do this, it is necessary to formulate a rigorous number system (to build an axiomatic theory). Thus, as a means of solving semantic and logical problems, descriptive logic has become a unique and powerful mechanism for representing knowledge. The successful use of descriptive logic in the author’s intelligent educational system will ensure the improvement of the quality of the educational process; monitoring of the learning process and control of its results; will facilitate the integration of this system into many modern information educational systems; will provide various consultations, advice, interpretations, explanations regarding the educational content or educational process, as well as a wide range of topics of educational content and tests of different levels.

An interval construction belief rule base with interpretability for complex systems

Complex system modeling using only subjective judgment is insufficient to address the needs of more extensive and complex actual systems. The expert knowledge base in the belief rule base (BRB) take part in complex system modeling well, but there are still the following problems. On the one hand, BRB cannot deal with the overabundance of combination rules caused by the excessive number of complex system attributes. On the other hand, parameters may lose their interpretability after BRB modeling, reasoning, and optimization. Given the above two problems, an interval construction belief rule base with interpretability for complex systems (IBRB-i) model is proposed. The IBRB-i model uses interval addition to create belief tables and adds rule reliability to prevent combination rule explosion in traditional BRB. In addition, interpretable constraints are added to the IBRB-i model to ensure its interpretability. In the case study, the validity and precision of the IBRB-i are analyzed and verified by two examples of power fault detection and liquid launch vehicle’s structure safety assessment.

Fault diagnosis of regenerative thermal oxidizer system via dynamic uncertain causality graph integrated with early anomaly detection

Regenerative thermal oxidizers (RTO) serve as pivotal equipment for the treatment of volatile organic compounds (VOCs). However, a diversity of faults of the RTO system may threaten personnel safety and lead to unscheduled downtime. In this work, we develop a traceability inference method based on dynamic uncertain causality graph (DUCG) to identify root fault cause of RTO at early anomaly alarm. A general expert knowledge base for the fault diagnosis of RTO system is constructed. And the anomaly alarm thresholds are modified according to historical data with n-sigma rules to improve the diagnostic accuracy. Results on different cases show advantages in both triggering early anomaly alarms and avoiding invalid alarms, which is crucial to improve the fault inference accuracy. By applying our method to 35 fault cases of a real RTO for pharmaceutical VOCs treatment, the fault detection rate reaches 94.29%, and the fault inference accuracy is 82.86%. The two scores are 3% and 21% higher than those with the conventional DUCG (with fixed alarms), respectively. This remarkable improvement underlines the significance of our proposed method for RTO maintenance, and it is expected to assure the system’s operational stability and reliability.