Knowledge-based System Research Articles

Rule-Based Verification of Autonomous Unmanned Aerial Vehicles

Automation enhances the capabilities of unmanned aerial vehicles (UAVs) by enabling self-determined behavior, while reducing the need for extensive human involvement. Future concepts envision a single human operator commanding multiple autonomous UAVs with minimal supervision. Despite advances in automation, there remains a demand for a “human in command” to assume overall responsibility, driven by concerns about UAV safety and regulatory compliance. In response to these challenges, a method for runtime verification of UAVs using a knowledge-based system is introduced. This method empowers human operators to identify unsafe behavior without assuming full control of the UAV. Aspects of automated formalization, updating and processing of knowledge elements at runtime, coupled with an automatic reasoning process, are considered. The result is an ontology-based approach for runtime verification, addressing the growing complexity of UAVs and the need to ensure safety in the context of evolving aviation regulations.

Gynaecological Artificial Intelligence Diagnostics (GAID) GAID and Its Performance as a Tool for the Specialist Doctor.

Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is such a system. Based on artificial intelligence (AI) argumentation technology, it was developed to incorporate, as much as possible, a complete representation of the medical knowledge in gynaecology and to become a real-life tool that will practically enhance the quality of healthcare services and reduce stress for the clinician. Our study aimed to evaluate GAIDS' efficacy and accuracy in assisting the working expert gynaecologist during day-to-day clinical practice. Knowledge-based systems utilize a knowledge base (theory) which holds evidence-based rules ("IF-THEN" statements) that are used to prove whether a conclusion (such as a disease, medication or treatment) is possible or not, given a set of input data. This approach uses argumentation frameworks, where rules act as claims that support a specific decision (arguments) and argue for its dominance over others. The result is a set of admissible arguments which support the final decision and explain its cause. Based on seven different subcategories of gynaecological presentations-bleeding, endocrinology, cancer, pelvic pain, urogynaecology, sexually transmitted infections and vulva pathology in fifty patients-GAID demonstrates an average overall closeness accuracy of zero point eighty-seven. Since the system provides explanations for supporting a diagnosis against other possible diseases, this evaluation process further allowed for a learning process of modular improvement in the system of the diagnostic discrepancies between the system and the specialist. GAID successfully demonstrates an average accuracy of zero point eighty-seven when measuring the closeness of the system's diagnosis to that of the senior consultant. The system further provides meaningful and helpful explanations for its diagnoses that can help clinicians to develop an increasing level of trust towards the system. It also provides a practical database, which can be used as a structured history-taking assistant and a friendly, patient record-keeper, while improving precision by providing a full list of differential diagnoses. Importantly, the design and implementation of the system facilitates its continuous development with a set methodology that allows minimal revision of the system in the face of new information. Further large-scale studies are required to evaluate GAID more thoroughly and to identify its limiting boundaries.

Epistemology in AI (Transdisciplinary AI)

A critical look at the evolution of AI strongly shows a sustained but stealth race to replace humans with AI. Early scientific literature and discourse on AI for some reasons (either to allow AI gain entry and acceptability in mainstream scientific and technological arena) vehemently deny this "human replacement agenda". This thinking pattern unknowingly shaped current scientific literature, discourse, general understanding of what AI is and its development and applicability (a reductionist thinking). This limits our understanding in both the beneficial and destructive capabilities of AI. But when considering a TD assessment on the developmental dynamic of AI, one would comfortably say and must be bold to admit, that indeed AI intends replacing humans and is on course for fulfilling this.
 
 We see this AI human replacement agenda in intensified R&D efforts dedicated to developing powerful AI system of systems which massively augment human reasoning, most times far better. The inexhaustible list includes the AI replacement of formerly considered human-centric jobs, advanced autonomous weapon systems, killer robots and AI in warfare, intelligent facial recognition, biometric monitoring, integrating AI on biological, nuclear and space-based weapons systems, etc. If this is the direction AI is taking, then a secondary aim would surely arrive at integrating epistemology in AI or "grant spirits" for AI systems. This is because a distinctive characteristic of a human is his spirit and one cannot replace humans with AI without creating proportionate or appropriate spirits for the AI systems. Sooner or later our AI systems would have epistemological functions and possess spirits. The place of the soul for such AI systems would be attained as well. If human knowledge, beliefs, voices, clips and laws can be preserved long after they are gone as is possible in smart digital technologies, then spirit-based AI would indeed cause these humans to live forever. If the feat of a spirit enamored AI is near, then why worry? Indeed when considering that humans possess good and bad spirits (from the epistemology of rational and irrational inertia) then these AI systems would of course have good or bad spirits and be bad or good AI.
 
 Would integrating a spirit into a rule-based or machine learning algorithmic structure of an AI system have benefits? Yes!, profound benefits too. A spirit-based AI would of course make possible the "possession of feelings" by AI systems, a feat unattainable in both algorithmic, operational and inferential basis of AI systems today. This inability of AI systems to have feelings has continued to remain a major setback in the acceptability (indeed trust) and utilization of AI. As we agree that the spirit in AI is possible, then overlooking efforts aimed at making this possible or allowing AI to attain this level unhindered (admitting dangers of human involvement in AI) could pose a dangerous threat which can become highly destructive to mankind. This calls for critical supervision (TD-based ethical policing) and the accompanying of the evolution, development and applicability of AI hence venerating the need for human mediation in AI both as a major TD research subject and applicable function.
 
 A discussion would be made on my approach which considers the synergy of critical systems heuristics (CSH) and systems engineering (Transdisciplinary Systems Engineering) to create "Transdisciplinary AI" which would formulate methods of integrating "human" epistemology in expert systems. Human epistemology is emphasized because by the maturity of this future nature of AI, there would be terms known as "AI or machine epistemology" or "AI or machine spirits". The investigation begins with creating expert systems (knowledge-based systems) with these functions with plans of moving into robotics and other machine learning arena. Finally, to move the needle on what is considered permissible epistemology or permissible spirit of/in AI is a critical component of the study of human mediation and AI which must be given critical attention. This would be discussed as well.

Unveiling the Dynamics of Entropy Generation in Enclosures: A Systematic Review

This extensive review aims to provide a thorough understanding of entropy generation (Egen) in confined conduits, or enclosures, by examining a vast array of peer-reviewed research. The review covers various studies on Egen in enclosures with different geometric configurations and highlights the significant effects of thermo-physical dynamics, such as temperature gradient, viscous dissipation, frictional drag, and magnetic field strength, on Egen characterization. The review covers a broad range of studies that investigate Egen in enclosures with diverse geometric configurations and different types of fluids, including air, water, and various types of nanofluids. Furthermore, the review also includes different enclosure structures, such as I, L, C, U, semicircular, triangular, square, rectangular, rhombic, trapezoidal, polygonal, and channel types, as well as wavy wall configurations. Notably, the review also encompasses both 2D and 3D cases to present a complete comprehension of Egen in confined conduits. In addition, the review carefully evaluates the validity methods utilized in numerical investigations, incorporating a diverse array of mesh types and sizes utilized in research. A thorough examination of the vast literature demonstrates that enclosures with obstacles, such as single or multiple rotating cylinders, exhibit a noticeable increase in Egen. Also, the review highlights that the use of nanofluids significantly increases Egen. These findings have important practical implications in the analysis of thermofluid systems, including but not limited to heat exchangers, chip cooling, food storage, solar ponds, and nuclear reactor systems. Based on the comprehensive review conducted in this study, several future research directions have been proposed for the emerging field of Egen in enclosures. This study explores the intricate mechanisms of Egen in enclosures and highlights potential avenues for further investigation in this area. These insights will contribute to the advancement of the knowledge base and practical applications of thermofluid systems, including heat exchangers, chip cooling, food storage, solar ponds, and nuclear reactor systems.

Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach

ABSTRACT Cyberattacks have been increasing in volume and intensity, necessitating proactive measures. Cybersecurity risk management frameworks are deployed to provide actionable intelligence to mitigate potential threats by analyzing the available cybersecurity data. Existing frameworks, such as MITRE ATT&CK, provide timely mitigation strategies against attacker capabilities yet do not account for hacker data when developing cyber threat intelligence. Therefore, we developed a novel information technology artifact, ATT&CK-Link, which incorporates a novel transformer and multi-teacher knowledge distillation design, to link hacker threats to this broadly used framework. Here, we illustrated how hospital systems can use this framework to proactively protect their cyberinfrastructure against hacker threats. Our ATT&CK-Link framework has practical implications for cybersecurity professionals, who can implement our framework to generate strategic, operational, and tactical cyber threat intelligence. ATT&CK-Link also contributes to the information systems knowledge base by providing design principles to pursue targeted cybersecurity analytics, risk management, and broader text analytics research through simultaneous multi-modal (e.g., text and code) distillation and classification.

Neurosymbolic system profiling: A template-based approach

Two antagonistic categories can be identified within Artificial Intelligence models: symbolic and subsymbolic. Neurosymbolic artificial intelligence is the term used to describe a set of approaches whose goal is to combine both symbolic and subsymbolic models under a unified approach. Design methods for neurosymbolic systems have been presented throughout the years, focusing mostly on theoretical aspects, such as how knowledge is represented within the system or which learning paradigm should be used. However, existing design approaches do not consider contextual or practical aspects. This work presents a template-based approach for the design of neurosymbolic systems, focusing specifically on the interaction between knowledge-based systems (symbolic) and deep learning models (subsymbolic). The proposed method extends previously addressed aspects (limitations and benefits) with contextual and practical aspects (restrictions and considerations). From this general four-dimensional descriptive template, a specific template is provided describing the parameters of each potential integration between knowledge-based systems and deep learning. Instantiation examples are provided to demonstrate the versatility and applicability of the proposal. In addition, this paper presents an extensive review of the current state-of-the-art in the design of neurosymbolic systems. The benefits of the proposed approach are two-fold. First, it enables high-level description of existing neurosymbolic systems, thus facilitating the search process for research. Secondly, the proposed method simplifies the engineering process and reduces development time because: (i) previously existing instances that are already described by templates can be easily reused, or (ii) the corresponding integration template can serve as a baseline to develop new systems from scratch.

Harvesting the Future: AI and IoT in Agriculture

This review article, “Harvesting the Future: AI and IoT in Agriculture,” presents a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It synthesizes a range of studies to showcase how AI and Machine Learning (ML) algorithms are revolutionizing crop management, precision agriculture, and supply chain efficiency. Utilizing data from various sources like sensors, drones, and satellites, these technologies enable optimized resource use, enhanced crop yields, and better livestock health monitoring. The review highlights the role of IoT in agriculture, particularly its benefits in easy installation, reduced maintenance, and energy harvesting for device sustainability. It explores the integration of IoT with big data and knowledge-based systems, addressing key challenges in farm data management. Additionally, the paper delves into the growing adoption of smart agriculture techniques, driven by cost-effective IoT sensors and AI advancements. These technologies facilitate efficient resource management, predictive analytics, and autonomous farming, thereby modernizing traditional agricultural practices. The review also discusses the broader social implications and future trends in the adoption of smart agriculture systems, describing their significance in enhancing agricultural productivity, sustainability, and profitability.

A Process for Creating and Designing Automated CAD Models Through the SolidWorks API

This study proposes a novel method for speeding up CAD modelling procedures by combining parametric modelling strategies, Knowledge-Based Systems (KBS), and Visual Basic for Applications (VBA) inside the SolidWorks environment. The suggested system accepts client design requirements through a user-friendly graphical user interface (GUI), utilising the power of knowledge representation and automation to speed up the design process. This technique greatly decreases the amount of time needed to model CAD, improves design correctness, and equips engineers to quickly convert client demands into real components by utilising KBS and parametric modelling. This research shows the viability and efficacy of this unique technique, highlighting its potential to transform the processes of product design and development.

Investigating obstacles to the development of rural knowledge-based and technological systems in the agricultural sector of Sistan Region under the Decision Support System (DSS) approach

Investigating obstacles to the development of rural knowledge-based and technological systems in the agricultural sector of Sistan Region under the Decision Support System (DSS) approach

A Research-Based Ontology for Collaborative Innovation: A Methodology Leveraging AI and Domain Expert Knowledge

This paper introduces a method, for creating a research driven ontology to foster collaboration and innovation. The suggested approach combines intelligence (AI) and Expert knowledge to build a comprehensive model encompassing various aspects of research, development and innovation. To demonstrate the feasibility of this method the paper showcases its implementation, in the field of accounting science. First, AI-powered machine learning algorithms and text mining techniques are used to extract the main ontological elements from a large corpus of accounting literature. Subsequently, expert knowledge is utilized to refine and validate these identified elements. The resulting ontology can be used as the foundation of a knowledge-based system to promote collaboration and analyze the state of innovation.

Ontology Driven for Mapping a Relational Database to a Knowledge-based System

Ontology Driven for Mapping a Relational Database to a Knowledge-based System

XR4DRAMA a knowledge-based system for disaster management and media planning

Abstract In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).

Struktur Intelektual Penelitian Organisasi Pengetahuan dan Artificial Intelligence

Abstract The objective of this study is to identify the most contributing journal articles to the research on knowledge organization and artificial intelligence, as well as to comprehend the development of research in knowledge organization and artificial intelligence from 1977 to 2023 using bibliometric methods. The analysis employed citation analysis and co-word analysis applied to the Scopus database. The findings revealed 136 documents related to knowledge organization and artificial intelligence. Out of the 9 most influential documents, 3 studies stood out with significantly higher citation counts compared to others, surpassing 150 citations. The research identified three clusters as the focal points. The first cluster emphasized keywords such as knowledge representation, semantics, and ontology, while the second cluster centered on keywords like knowledge-based systems, expert systems, and decision making.

РОЗРОБКА ОНТОЛОГІЧНОЇ МОДЕЛІ БАЗИ ЗНАНЬ «БІБЛІОТЕКА» НА БАЗІ СЕРЕДОВИЩА PROTÉGÉ

Modern development of society, increasing the degree and pace of integration of information technology achievements into the sphere of human life, traditional approaches to the construction of information systems cease to be effective or become too cumbersome. The development and use of knowledge-based systems is one way to solve this problem. The material of the work is devoted to the ontological modeling of the new subject area «Library». Ontology is considered in the context of knowledge sharing. The created ontological model of the «Library» knowledge base is quite modern and relevant today. The developed ontological model of the knowledge base in this field can be implemented on thematic web resources and will significantly facilitate the semantic search of information within the subject area in comparison with the existing ones. The terminological dictionary of this subject area is defined using the term system. An analysis of the capabilities of the Protégé ontology development environment for modeling the specific subject area «Library» was carried out. The basic principle of ontology modeling in the form of a semantic network was chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area «Library». Using the Protégé environment for the implementation of the ontological model of the knowledge base made it possible to use the advantages and features of the created «Library» model, such as: structuredness, completeness, reflexivity, functionality, transitivity, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base was selected and used. Testing of the developed ontological knowledge base was conducted and a fairly high level of its correctness in the process of information search was confirmed. The average indicator (Average) according to the SUM metric, determined for all users, is equal to 83.25%, which is an acceptable indicator of the ontological knowledge base. When using the classic relational model of database organization to implement the «Library» database. the average SUM metric for 10 users is 75.26%. An example of the developed ontology in Protégé is considered, a graphical representation of the basic graph of the ontological model of the «Library» knowledge base is given, the model includes 10 classes and subclasses, 2 properties-relations and up to 10 properties-data are defined for each class and subclass, an example of the classes of the ontological model is given «Library», an example of the "property-relations" of the ontological model «Library» is given, an example of the "property-data" of the ontological model «Library» is given. Possible directions of further development of the ontological model «Library» have been determined.

Design of Intelligent Customer Service Knowledge Base for Medical Insurance Based on Foundation-scale Models

In this paper, we propose a method for constructing an intelligent customer service knowledge base system for medical insurance based on natural language processing (NLP) models. This method utilizes a local vector database to construct the knowledge base system and retrieves industry knowledge by calling the vector database through foundation-scale models. The use of this technology ensures the security of business data and reduces platform training costs. The intelligent knowledge base of medical insurance constructed using this method can automatically handle user’s inquiries, complaints, and general business transactions, effectively improving the efficiency and intelligence level of customer service.

Intelligent System in Education: Requirements and Design Method

Intelligent systems in education have proven to be highly beneficial in supporting self-learning among students, particularly in remote learning scenarios. This study proposes the necessary requirements for an intelligent educational system that serves two primary functions: querying course knowledge and evaluating learner proficiency through multiple-choice testing. Furthermore, this study builds a solution to design the knowledge base, inference engine, and tracing system based on a knowledge model that integrates ontology and knowledge graph.

The Method and the Model for Processing Textual Information on a Learned Transformer for Information-Retrieval System

To form a knowledge base is complicated problem traditionally. There are a lot kind of objects that are possibly used for forming a knowledge base. These objects may have different structures, formats, ways of data representation, languages. The simple conjunction is not effective and suitable. In general case the knowledge base has got as an unordered knowledge base. There are uncategorized documents in such unordered knowledge base with different formats that causes the special and particular approaches for recognition, systematization and next processing of some textual information. It’s why the complexes of automation for all stages of processing are complicated. Naturally it is a restriction for some kind of the decision support system, especially in military or other applications with key time factor (to get a quick and exact access to the knowledge base in decision support system). So, we analyzed the mentioned restrictions and conditions for forming a knowledge base in the paper. We depicted that the ontology of knowledge base both in general and specific cases includes such operations as data collection, data regularization, extraction of knowledge, data conversion for matrix representation, data language processing, tokenization, output generation for a request and machine learning for information-retrieval system optimization. There is a model of information-retrieval system for knowledge base with widely- used PDF-documents that is proposed in the paper. We made the model using open learned transformer and Llama Index framework to decrease the time demands in the information-retrieval system. Also, we included the language processing models for translation the specific textual information from Ukrainian into English and back. As a result, we got the method and the model for processing the textual information from PDF-document in Ukrainian that could be effective in any decision support system. The method ensures the reading, tokenization, translation, analysis and retrieve generation of the data in Ukrainian. The model showed its simple, stable and exact estimations, but there are also some disadvantages, high time installation/compilation and little language defaults are some of them. The results encourage us to continue the research and to get the statistics set to analyze the model estimation more properly.

Development and preliminary testing of a prior knowledge-based visual navigation system for cardiac ultrasound scanning.

Ultrasound is widely used to diagnose disease and guide surgery because it is versatile, inexpensive and radiation-free. However, image acquisition is dependent on the operation of a professional sonographer, which is a difficult skill to learn for a wider range of non-sonographers. We propose a prior knowledge-based visual navigation method to obtain three important standard ultrasound views of the heart, based on the sonographer's skill learning and augmented reality prompts. The key information about the probe movement was captured using vision-based tracking and normalisation methods on 14 volunteers, based on a professional sonographer's practice. An augmented reality-based navigation method was then proposed to guide operators with no ultrasound experience to find standard views of the heart in a second set of three volunteers. Through quantitative analysis and qualitative scoring, the results showed that the proposed method can effectively guide non-sonographers to obtain standard views with diagnostic value. It is believed that the method proposed in this paper has clear application value in primary care, and expansion of the data will allow the accuracy of the navigation to be further improved.

Research on the Development and Innovative Strategy of Knowledge Payment Platform in the Internet Era

The widespread adoption of knowledge-based payment systems and numerous online knowledge-based paid items is made possible by the quick development of knowledge information and networking technology. Paid columns were quickly introduced by platforms like Himalaya FM, Zhihu, and Qingting FM, while pay-for- knowledge businesses like Fenda, iGet, and Qianliao quickly went live online. Users rapidly increased, and it was assumed that the knowledge payment progressive trend had been met. This paper aims to examine the emergence of edge computing into the mobile information system into the presence and inevitability of the knowledge payment platform, evaluate the advantages, challenges, and pathways for knowledge payment platform optimization, and try to provide a conceptual suggestion for aiding in its advancement. During data analysis, the independence of the polynomial characteristics was evaluated using Pearson's chi- squared tests. A brand-new meta-heuristic optimizer dubbed Harris Hawks optimization is inspired by how Harris hawks seek food in the wild. According to the experimental results, 98 of the survey's respondents—or 19.1% of all respondents—were under the age of 18; 201 were between the ages of 18 and 29; 142 were between the ages of 30 and 39; and 73 were over 40, or 14.2% of all respondents. The findings indicate a younger age distribution for the sample, with the concentration being highest among those between the ages of 18 and 29, then 30 to 39. The mobile information system edge-based knowledge payment platform has successfully undergone continuous use behavior analysis.

Strategic concept paper for bioeconomy in Poland: executive summary.

The main objective of the proposed approach is to accelerate the transition of the bioeconomy towards a knowledge-based sustainable system, covering key biobased sectors strongly linked to agriculture in Poland, in line with the European Green Deal. The proposed model of a bioeconomy development strategy, with a special focus on agriculture, is based on two pillars: (1) strengthening traditional, relevant (in terms of economic indicators) sectors of the economy and improving their 'sustainability' by implementing the proposed transformation pathways; and (2) developing economic activities or 'niche or novel sectors' that are prospective accelerators of change in the face of climate challenge, and ensuring their upscaling. This approach forms the basis for policy planning at national and regional level in these European Union (EU) countries, where bioeconomy development strategies have been initiated through dialogue between science, administration and industry stakeholders. The strategic actions we propose are grouped in three areas; (1) Market intervention mainly by introducing sustainability criteria for the national production system; (2) Research, innovation and education that significantly strengthen the relationship between business and science and educational activities in the field of sustainability and climate change, and (3) Governance and policy actions to enforce the relationship between the main sub-sectors of bioeconomy and niche sectors in frame of bio-economy strategy or action plan that will profits by added value of products from bioeconomy sectors and increasing number of newly created jobs.