Intelligent Information Systems Research Articles

Synthesis of Concepts and Applications of Information Intelligent Systems and Knowledge Bases in Computer Science: A Systematic Literature Review

Synthesis of Concepts and Applications of Information Intelligent Systems and Knowledge Bases in Computer Science: A Systematic Literature Review

A novel quantum Dempster's rule of combination for pattern classification

Dempster's rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster's rule of combination increases exponentially. To address this issue, we propose a novel quantum Dempster's rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits. To validate the efficiency of the proposed QDRC, we conduct simulation experiments on IBM and IonQ quantum cloud platforms. Besides, we implement the QDRC algorithm to the real quantum computer of IonQ Harmony. The experiment results show that the result of QDRC is getting more and more accurate as the number of measurements increases. In addition, we propose a quantum multisource information fusion method on the basis of QDRC. Finally, we demonstrate its effectiveness to solve pattern classification problem under several diverse real UCI datasets.

Multi-Objective Construction of English Web-Based Independent Learning Based on Mobile Intelligent Information System

A Mobile Intelligent Information System (MIIS) represents a transformative tool in the digital age, empowering users with access to dynamic and personalized information anytime, anywhere. Through advanced technologies such as artificial intelligence, machine learning, and natural language processing, MIIS harnesses the power of data to deliver tailored insights and services to users via mobile devices. With its intuitive interface and seamless integration with mobile platforms, MIIS offers a user-friendly experience that adapts to the evolving needs and preferences of users. As mobile technology continues to evolve, MIIS remains at the forefront, revolutionizing the way information is accessed, processed, and utilized in our increasingly interconnected world. This paper presents a multi-objective approach to construct an English web-based independent learning platform leveraging a Mobile Intelligent Information System (MIIS), enhanced by Genetic Integrated Web Optimization (GIWO) with the integration of Ant Bee and Whale Optimization algorithms. The proposed framework aims to optimize multiple objectives, including content relevance, user engagement, and learning effectiveness, in the design and deployment of the learning platform. Through simulated experiments and empirical evaluations, the effectiveness of the GIWO-enhanced MIIS in constructing the web-based learning platform is assessed. Results demonstrate significant improvements in content recommendation accuracy, user satisfaction, and learning outcomes compared to traditional approaches. The integration of Ant Bee and Whale Optimization algorithms further enhances the optimization process, enabling the system to adapt dynamically to evolving user needs and preferences. This study highlights the potential of leveraging MIIS and GIWO algorithms for the multi-objective construction of web-based learning platforms, paving the way for more personalized, efficient, and effective independent learning experiences in English education. The GIWO-enhanced MIIS achieved an average increase of 35% in content relevance, as evidenced by precision and recall scores. Additionally, user satisfaction ratings increased by 40%, indicating higher levels of engagement and perceived usefulness of the platform. Furthermore, learning outcomes improved by 25%, as measured by pre- and post-assessment scores. These simulation results underscore the efficacy of the proposed approach in optimizing multiple objectives for the construction of web-based independent learning platforms in English education.

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry.

Healthcare Waste Management: An Overview

Healthcare waste (HCW) is a vital global issue that cannot be overlooked due to its threat to humans and the environment stemming from its infectious and hazardous nature. This study examines previous works undertaken on healthcare waste management (HCWM) practices around the world, notably the developing countries with a particular interest in segregation, collection, transportation, treatment, and disposal of HCW. This study draws attention to the environmental hazards arising from each stage of HCWM. Factors affecting HCWM practices have also been discussed. This study revealed evidence of poor HCWM practices in many developing countries. It also showed the impacts of human and non-human factors on HCWM practices. Proper documentation, sufficient budget, adequate supply of HCWM materials, frequent training of healthcare workers, and development of local manuals and guides are essential if a country is determined to achieve an efficient and sustainable HCWM system. Liquid HCW needs to be investigated as much as the solid HCW. Exploration of HCW minimization, reuse, and recycling opportunities is recommended for future research. The use of Modern-day technology such as Artificial Intelligence and geographic information system (GIS) has provided good results so far. However, they can be explored further for prediction, real-time monitoring, and reporting of HCW. The present study can be adopted as a guide in discussing issues about HCWM.

FRAMB: A man-machine cooperation risk assessment method for intelligent network information systems

The intelligent network information systems, such as smart grid systems, face many security problems in the aspects of sensing, communication and computing. Information security risk assessment is an important way to assess the threats faced by information systems before risk events occur and ensure the security of assets. However, a comprehensive risk assessment of the system is a very resource-consuming process. Many existing risk assessment methods rely on a large number of experts and computing resources. Their assessment results are vulnerable to the differences in experts’ subjective judgments. Therefore, we propose FRAMB, a novel man-machine collaborative risk assessment method based on fitting upper and lower bounds. Firstly, we present a risk assessment criterion including four categories and sixteen risk factors following the ISO/IEC 27005:2018 standard. On this basis, we present the DFAHP and CM-NN assessment models to obtain the upper and lower bounds of the risk assessment value, which provides a reference for expert assessment. FRAMB integrates the experts’ assessment value and the values of upper and lower bounds, and adjusts the weights of these values to give the final risk assessment value. We introduce the risk assessment process of FRAMB in detail through a case study of the smart grid system risk assessment. We evaluate the effectiveness and accuracy of FRAMB through experiments. The experimental results show that FRAMB can effectively and accurately assess the security risks of the intelligent network information systems.

Impact of digital transformation on accounting: trends and challenges

The rapid development of the information potential of the digital economic space in Ukraine, the issue of digitalization of the accounting system is becoming of paramount importance are given. The situation is stimulated by the rapid innovation and technological progress and digital transformation of all spheres of social life, as well as the trend of Ukraine's integration into the European economic area. The purpose of the paper is to analyze the current role of digital transformation tools on accounting and to predict the development of its impact in the future. The paper analyzes and assesses the risks caused by the active implementation of digitalization technologies in accounting. The author identifies a number of dangers that will accompany the further rooting of artificial intelligence in information systems, and proposes a number of preventive measures that should be applied within the framework of the general trend of digitalization of accounting. The study was conducted using scientific methods of cognition: abstract and logical and comparative analysis, abstraction, induction and deduction, as well as methods of specification and formalization. It is established that the digital transformation of accounting is positioned as a mandatory optimization stage in the development of the modern business environment. It is substantiated that modern information technologies make it possible to significantly simplify the collection, storage and transfer of accounting information through the introduction of special industry-specific software products. The main digital trends in the process of digitalization of accounting are analyzed, reflecting the vector of the overall development of a sustainable financial environment and the sharing economy. It is proved that the growing use of digital tools makes it possible to make accurate and effective decisions in complex analytical processes. The results of the study may be of practical value for the process of improving the modern accounting system in the context of globalization of digitalization and artificial intelligence technologies. The paper analyzes the feasibility and prospects of applying innovative AI capabilities in the accounting, as well as means and technologies to ensure an appropriate level of security and protection of personal information.

ВИКОРИСТАННЯ В ОСВІТНЬОМУ ПРОЦЕСІ ТЕХНОЛОГІЙДИСТАНЦІЙНОГО НАВЧАННЯ

The integration of Ukraine into the European educational space involves strengthening the requirements for the quality of professional training of future specialists. The main task is to create all the conditions for learning, personal and professional development of students, to ensure the effective formation of their professional competence, regardless of the modern challenges and dangers faced by society in the 21st century. Currently, higher education institutions have a dilemma: are they able to move away from the traditional academic style of teaching to innovations, to focus not on knowledge and skills, but to ensure the process of forming the competence of the future specialist. Considering the circumstances, digital technologies are becoming more and more widespread, and in scientific publications there is a debate about the effectiveness of their use in the educational process of higher education. The integration of digital technologies in higher education creates new opportunities for both students and teachers, making learning more interesting, dynamic, visual, etc. The advantages and features of the use of remote technologies to improve the quality of professional training of future specialists in the conditions of a mixed form of organization of the educational process in institutions of higher education, which creates new conditions of learning and cooperation for all participants of the educational process, are considered. The most promising technologies of distance learning are characterized, which have important advantages, taking into account the peculiarities of the professional training of future specialists in the conditions of mixed learning in institutions of higher education. Such technologies include: gamification of the process of professional training of future specialists, quest technologies, cloud-based learning technologies, intelligent information systems in education, mobile learning technologies, immersive technologies. Currently, in the conditions of modern existence and development of society, innovative technologies are offered to higher education seekers, which gradually replace traditional methods of learning and ensure flexibility and quality of the educational process. We see the prospects for further research in the improvement of the educational process by determining the optimal ratio of classroom and remote components in the conditions of a mixed form of education in the process of developing the professional competence of future specialists and developing appropriate educational and methodological support.

Interpretable Artificial Intelligence in Information Systems: Status Review and Future Research Directions

Efforts to develop black-box artificial intelligence (AI) systems have become a phenomenon of emerging global interest in academia, business, and society, and have led to the development of the XAI research field. With its pluralistic perspective, information systems (IS) research is destined to contribute to this emerging field; thus, it is not surprising that the number of research publications at XAI has increased significantly. This paper aims to provide a comprehensive overview of XAI research in public and electronic markets, specifically using a structured literature review. Based on a literature review of 180 research papers, this work examines the most receptive points, the development of academic debates, and the most important concepts and methodologies. In addition, eight research areas with different levels of maturity in e-markets are identified. Finally, guidelines for the XAI research agenda in IS are presented

Analysis of the structure of an intellectual information system based on the concept of functional stability

The development of modern society requires intensive development of information technologies with a high degree of autonomy. This issue is especially acute for production enterprises that operate under the influence of destabilizing factors. In this work, research on the property of functional stability continues. Functional stability means the property of an information system to maintain its functioning, possibly with a decrease in quality, during the specified time under the influence of external and internal destabilizing factors. External and internal destabilizing factors mean failures, failures of system modules, mechanical damage, thermal effects, errors of service personnel. The main stages of ensuring functional stability are the detection of the module that failed during control, the diagnosis of the module that failed, and the restoration of the functioning of the intelligent information system. A feature of intelligent information systems is that they must function autonomously. With their help, it is possible to ensure an increase in the labor productivity of all production centers while reducing the number of people employed in production. The purpose of the work is to build a mathematical apparatus for quantitative assessment of the functional stability of the data exchange system of the intelligent information system. In addition, this article shows the advantages of using quantitative methods of assessing functional stability based on such indicators as the limit and margin of functional stability. On the basis of these assessments, recommendations can be made for building an intelligent information system. In addition, the evaluation data will help to more clearly formulate the requirements for the structure of the intelligent information system, which will be designed for uninterrupted data transmission.

Formation of a knowledge base for selecting parameter values for the working parts of the TORUM rotary combine

The work examines the problem of adjusting the adjustable parameters of the axial-rotor threshing-separating device (ARTSD) of the TORUM grain harvester. A general block diagram of the solution has been constructed. To formalize the problem, a linguistic approach was used, which makes it possible to describe complex multi-level systems with ambiguous dependencies between input factors and controlled parameters. The feasibility of using an intelligent information system based on fuzzy logic to solve the problem of setting the adjustable parameters of a combine harvester is substantiated. The article presents the implementation of the first stage of solving the problem of designing an intelligent information system for setting the adjustable parameters of a combine harvester, constructing a formal logical scheme for solving the problem, and creating a fuzzy knowledge base that forms the basis of this information system. Options for pre-setting for harvesting various crops are considered. Models of the considered features in the form of membership functions are proposed. Basic and extended term sets are established. The consistency of the models was analyzed and the optimal models were selected. The results of the solutions obtained are illustrated. Based on expert information about the system of complex relationships between environmental factors and the adjustable parameters of the combine, a fuzzy knowledge base was created that establishes rules for the deductive conclusion of decisions about the values of parameters in various combinations of external conditions.

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

It is established that the urban traffic management system, as well as the aviation traffic management system, has a significant impact on the multimodal delivery of goods in the road-air transport, which requires the creation of tools for theoretical substantiation and practical implementation. The authors� method of determination the occurrence of vehicles queues in cities using mathematical modelling tools for three scenarios of crossing intersections was developed. The conceptual scheme for calculating the occurrence of the queue of vehicles was proposed. It was noted that vehicles in the queue often arrive randomly, the duration of service is constant, and the probability that before the queue disappears for the first time a certain number of cars cross the intersection has a Borel-Tanner distribution. The given mathematical modelling should become a part of tools for the implementation of the integrated intelligent information system of cities as a cluster of the city traffic management system.

Information System for the Educational Center

The goal of the research of the qualification thesis is the development of an intelligent information system of the educational centre using the Python programming language and the Django framework, the SQLite database system, and the chatbot with artificial intelligence ChatGPT. The developed system should facilitate the interaction of different types of users with learning/training centres in order to acquire new necessary skills. As you know, from now on every day more and more educational centres are opening, which provide their services for the study of this or that ability. Due to the onset of the pandemic period, all such educational centres are transferring their classes to the online mode, and the actual need to solve this type of issue arises more and more often. In this work, an analytical review of literary sources is carried out, a comparison of known means of implementation, i.e., so-called analogue systems, is carried out; the actual functioning of this type of system is checked. In addition, a goal tree is being built, which clearly outlines the general goal, namely, the optimization of all processes of the educational centre. In addition, an equally important step is the description of requirements, which is carried out in this work using the RUP methodology for the intelligent information system of the educational centre. The category of users is also determined and a generalized model of system functioning in the environment is built. To develop the system, an analysis of possible methods and software tools for creating and deploying the system is carried out. The most optimal methods for this case are chosen, namely the method of software synthesis and the method of using ready-made solutions, because their application will provide an opportunity to develop a system from scratch using the necessary components and provide an opportunity to integrate an already ready-made system with an intelligent component. Next, a recalculation of possible options among the integration of ready-made intelligent components is carried out, and a chat system with artificial intelligence is chosen, because it will give users the opportunity to search for answers to their questions without leaving the system and facilitate the learning process for each of the users.

Architecture of an intelligent information system for forecasting components of medical projects

The article concerns the development of an intelligent information system for forecasting components of medical projects. The purpose of the study is to propose an intelligent information system for forecasting component medical projects, which is based on the use of neural network models, as well as statistical and expert methods, which, unlike existing ones, ensures the accuracy of forecasting component medical projects, adaptability to changes in their project environment, as well as accessibility for users. The task of the research is to substantiate the architecture and develop an algorithm for the operation of an intelligent information system for forecasting component medical projects, as well as to develop the user interface of this system and to carry out forecasting of component medical projects. The object of research is decision support processes in medical project management. The subject of the study is the architecture of an intelligent information system for forecasting the components of medical projects, which defines the model, structure, functions, and relationships between its components. The scientific novelty is to substantiate the algorithm and architecture of an intelligent information system for forecasting the components of medical projects based on the use of dynamic data (modern medical records, medical information system server, and other external data), which ensures the formation of a large database underlying the training of neural network models and ensuring high accuracy of forecasting the components of medical projects. The proposed intelligent information system is an effective tool that can be used to increase the accuracy of management decisions and the effectiveness of medical projects. The system involves the use of a medical information system for the formation of a historical database, which ensures the formation of a knowledge base and the development of a set of systematically interconnected blocks. The developed algorithm of the proposed intelligent information system involves the implementation of 17 steps, which reflect the intellectual approach, which involves the use of neural networks for forecasting the components of medical projects. A user interface of an intelligent information system for evaluating component medical projects has been developed, which involves the use of 6 tabs. The use of dialog boxes for forecasting components of 5 groups of projects is foreseen. These include projects for the creation of hospital districts, projects of highly specialized medicine, projects of specialized medicine, projects of primary medicine, and advisory and diagnostic projects. A proposed neural network for predicting the duration of diabetes treatment projects in children. It is a deep neural network with two levels, which provides forecast accuracy at the level of 95.4 %, which indicates its sufficient efficiency and feasibility of use in an intelligent information system. Established trends in the duration of diabetes treatment in children due to changes in the main factors that cause them. The obtained results depend based on improving the quality and accuracy of decision support for assessing the duration of diabetes treatment in children with different states of their disease

Retracted: A Big Data Analysis Algorithm Designed for the Interactive Platform of the Intelligent Sensor Information System

Retracted: A Big Data Analysis Algorithm Designed for the Interactive Platform of the Intelligent Sensor Information System

A METHOD FOR EVALUATING EXPLANATIONS IN AN ARTIFICIAL INTELLIGENCE SYSTEM USING POSSIBILITY THEORY

The subject of the research is the process of generating explanations for the decision of an artificial intelligence system. Explanations are used to help the user understand the process of reaching the result and to be able to use an intelligent information system more effectively to make practical decisions for him or her. The purpose of this paper is to develop a method for evaluating explanations taking into account differences in input data and the corresponding decision of an artificial intelligence system. The solution of this problem makes it possible to evaluate the relevance of the explanation for the internal decision-making mechanism in an intelligent information system, regardless of the user's level of knowledge about the peculiarities of making and using such a decision. To achieve this goal, the following tasks are solved: structuring the evaluation of explanations depending on their level of detail, taking into account their compliance with the decision-making process in an intelligent system and the level of perception of the user of such a system; developing a method for evaluating explanations based on their compliance with the decision-making process in an intelligent system. Conclusions. The article structures the evaluation of explanations according to their level of detail. The levels of associative dependencies, precedents, causal dependencies and interactive dependencies are identified, which determine different levels of detail of explanations. It is shown that the associative and causal levels of detail of explanations can be assessed using numerical, probabilistic, or possibilistic indicators. The precedent and interactive levels require a subjective assessment based on a survey of users of the artificial intelligence system. The article develops a method for the possible assessment of the relevance of explanations for the decision-making process in an intelligent system, taking into account the dependencies between the input data and the decision of the intelligent system. The method includes the stages of assessing the sensitivity, correctness and complexity of the explanation based on a comparison of the values and quantity of the input data used in the explanation. The method makes it possible to comprehensively evaluate the explanation in terms of resistance to insignificant changes in the input data, relevance of the explanation to the result obtained, and complexity of the explanation calculation. In terms of practical application, the method makes it possible to minimize the number of input variables for the explanation while satisfying the sensitivity constraint of the explanation, which creates conditions for more efficient formation of the interpretation based on the use of a subset of key input variables that have a significant impact on the decision obtained by the intelligent system.

Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

This paper presents a natural language-driven dialogue system designed to support healthcare professionals and students in the field of physical medicine and rehabilitation. The system seamlessly integrates concepts from intelligent information systems, data mining, ontologies, and human-computer interaction, employing at its core a rule-based dialogue mechanism. The system harnesses the power of ontology-based graph knowledge, underscoring its domain-specific efficacy. This article delves into the automated knowledge base formation, utilising Python scripts to translate EBSCO’s dataset of articles on physical medicine and rehabilitation into an OWL ontology. This methodology ensures adaptability to the ever-evolving landscape of medical insights. The system’s approach to natural language processing encompasses text preprocessing, semantic category discernment, andSPARQL query creation, providing 26 predefined categories. As an innovation in performance optimisation, the system integrates a strategy to cache precomputed responses using a PostgreSQL database, which aids in resource conservation and reduction in query execution latency. The system’s user engagement avenues are further detailed, showcasing a Telegram bot and an API, enhancing accessibility and user experience. In essence, this article illuminates an advanced, efficient dialogue system for physical medicine and rehabilitation, synthesising multiple computational paradigms, and standing as a beacon for healthcare practitioners and students alike.

Construction of Intelligent Leadership Organization Information System Based on Intelligent CRM System Data Center Retrieval Algorithm

Construction of Intelligent Leadership Organization Information System Based on Intelligent CRM System Data Center Retrieval Algorithm

Application of intelligent systems for the development of inclusive education in educational institutions

The current level of development of information technology makes it possible to create software products that develop and promote inclusive education in higher education institutions. With the use of intelligent information systems, education becomes more developed, humane and effective not only for students with special educational needs, but also for healthy students. The paper proposes the creation of a mobile application - an assistant that will facilitate orientation in the university space. The application will help to create conditions that meet the requirements and needs of each individual, regardless of the peculiarities of their development

PROSPECTS FOR DIGITALIZATION OF JUDICIAL ACTIVITY ON THE APPOINTMENT OF CRIMINAL PUNISHMENT

The possibilities of improving the efficiency of judicial activity on the appointment of criminal punishment with the help of the latest information technologies are being investigated. The possibility of using artificial intelligence technologies to determine the category of severity of crime is analyzed. The problematic aspects of judicial activity in sentencing are highlighted and ways to solve them with the help of its programming and automation are proposed. The potential possibilities of improving the activity of courts for the appointment of criminal penalties with the help of intelligent information systems and methods have been identified.