Intelligent Information Systems Research Articles

Multi-agent modeling of traffic organization in urban agglomerations

The authors consider the features of multi-agent modeling for traffic optimization in the central areas of cities. While evaluating the unique challenges associated with the high concentration of vehicles, pedestrians and historical buildings, the potential of multi-agent systems to effectively solve the problem of congestion, safety and quality of life in urban areas is investigated. The potential of multi-agent modeling in the context of traffic management in the central areas of the city allows us to identify the key challenges and opportunities. Many scientists address the main aspects of such modeling and use them in the transport and road sectors. A review of current research and development has shown that multi-agent models aim to simulate and optimize the supervision and control of transportation in various traffic scenarios. Modeling traffic organization in the central areas of cities is one of the main elements of urban development planning and management. Due to the growing population of cities and the increasing number of vehicles, the problems of congestion, air pollution, and inefficient use of infrastructure are becoming increasingly relevant. Therefore, it can be noted that multi-agent traffic modeling opens up new prospects for developing effective traffic management strategies, providing a flexible and adaptive solution to these problems. The research analyzes the existing approaches, identifies the system`s key components, and develops a model that demonstrates the interaction between agents and the environment based on a mathematical description. A practical simulation of the model, carried out using the AnyLogic software on the example of Lesia Ukrainka Boulevard in Kyiv, confirms the effectiveness of the multi-agent approach. The results of the study indicate the possibility of applying the developed model to improve intelligent information systems for traffic flow management, which opens up new prospects for improving traffic in the central areas of cities.

Application and Exploration of Artificial Intelligence in Teaching and Learning in Private Colleges and Universities

The rapid advancement of artificial intelligence technology across various sectors has sparked profound transformation in the field of education, particularly in the realms of pedagogy and administration within private higher education institutions. The discourse delves into the specific applications of AI educational aids in both classroom instruction and post-class learning, encompassing intelligent tutoring systems, virtual laboratories, and personalized learning recommendation systems, among others. Furthermore, it addresses the utilization of AI-driven question-answering systems, automated homework grading systems, and speech recognition technology. In terms of educational administration, it investigates the integration of intelligent student information systems, learning trajectory tracking systems, as well as course resource recommendation systems and teaching evaluation systems. The aim of the article is to furnish educators with valuable insights to enhance teaching quality and administrative efficiency, thereby promoting the intelligent development of private higher education institutions.

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

The Application of Intelligent Information Systems Driven by 6G Big Data in Product Sales Traceability

Unveiling historical agroecological patterns through artificial intelligence (AI) and Geographic Information Systems (GIS)

Unveiling historical agroecological patterns through artificial intelligence (AI) and Geographic Information Systems (GIS)

МОДЕЛЮВАННЯ ВЗАЄМОДІЇ В РОЗПОДІЛЕНИХ ІНТЕЛЕКТУАЛЬНИХ ІНФОРМАЦІЙНИХ СИСТЕМАХ МЕТОДАМИ АВТОМАТИЧНОГО ПЛАНУВАННЯ

This paper delves into the intricacies of resource management and task scheduling within distributed information systems, aiming to propose a processing flow for effective coordination and information exchange among system components. The research focuses on the coordination of actions and information exchange within distributed intelligent systems, highlighting the issues stemming from asynchronous operations, resource management complexities, and the necessity to adapt to dynamic environmental conditions. By leveraging these challenges, the study presents results showcasing the impact on task execution delays, suboptimal resource utilization, and overall system performance degradation. Through experimentation, it underscores the complexity inherent in coordinating distributed intelligent systems and emphasizes the need for an efficient synchronization mechanism. The proposed approach aims to mitigate these challenges, offering insights into modeling, automatic planning, and optimization within information systems. Context. The paper focuses on addressing the challenges associated with resource management and task scheduling in distributed information systems. Objective. The purpose of the work is to propose the processing flow for coordination and information exchange between different components of distributed intelligent systems. Methods. The paper presents the research results about coordination of actions and information exchange between components of distributed intelligent systems. These problems can lead to delays in the execution of tasks, inefficient use of resources, and a general decrease in system performance. These difficulties are caused by a variety of factors, including the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions. In current research, we utilized the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions. Results. The paper presents the research results about coordination of actions and information exchange between components of distributed intelligent systems. Conclusions. The experiments confirmed the complexity of coordination and information exchange between these components, which can lead to delays in the execution of tasks, under-optimized use of resources and a general deterioration in system performance. This research was aimed at developing an effective way to sync and coordinate distributed intelligent information systems, including the asynchronous operation of components, the complexity of resource management, and the need to adapt to changing environmental conditions

An Analysis of the Development of Investment Banks' IPO Business under the Registered System

Under the reformed registration system, the role of investment banks in the Initial Public Offering (IPO) business has evolved significantly from the approval system. As crucial "gatekeepers" of the capital market, these banks now bear the responsibility of thoroughly examining issuance application materials for truthfulness, accuracy, and completeness. While the IPO business promises a prosperous future for investment banks, it also presents considerable challenges. Some banks face issues such as insufficient adherence to prudent examination requirements, lax establishment or implementation of internal control systems, and the need for improved information management. To enhance their sponsorship business, it is recommended that investment banks optimize talent training, internal management control, risk management, and intelligent information system construction. Cultivating professional sponsorship talents with comprehensive skills, reinforcing ethical standards, and conducting thorough reviews of IPO companies are crucial. Furthermore, optimizing the management system, establishing effective communication mechanisms, and enhancing risk management capabilities are essential steps. Leveraging advancements in computer technology, the construction of a secure and reliable intelligent information system is advised. As the reform progresses, investment banks should bolster international exchanges, cooperation, and professional competitiveness to evolve into reputable international brand banks.

Construction of explanations in intelligent systems based on the formation of causal dependencies

The article considers the process of building explanations in intelligent information systems. A causal approach to building explanations in such systems is developed, which creates conditions for automated refinement of explanations in order to make them understandable for users, taking into account their goals and needs. The explanation is built using the indicators of possibility and necessity, which makes it possible to take into account the uncertainty of the intermediate data of the intelligent system presented in the form of a "black box". Within the framework of the proposed approach, the tasks of building explanations in explanatory artificial intelligence systems are structured, a causal model of explanation in an intelligent information system is developed, and a method of building explanations in an intelligent information system using causal dependencies is developed. The following new scientific results were obtained. A generalized causal model of explanation is proposed, combining temporal, causal and target components. The model allows for the construction of a multidimensional explanation that can be used not only after the decision has been made, but also before its implementation. A generalized method for constructing an explanation based on causal dependencies is proposed, which includes stages of formation of temporal, causal and goal description of the explanation. The method offers the possibility of automated construction and iterative refinement of explanations.

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

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.

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.

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.

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.

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

ИНТЕЛЛЕКТУАЛЬНЫЕ СИСТЕМЫ УПРАВЛЕНИЯ КАК ИНСТРУМЕНТ ОБЕСПЕЧЕНИЯ УСТОЙЧИВОГО РАЗВИТИЯ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ

The article considers the issue of the introduction of intelligent management systems to ensure sustainable development of industrial enterprises. The concept of intelligence in information systems and its application to functions in production are analyzed. The classification of intelligent systems is discussed and the matching of the types of systems with the functions in the enterprise is carried out. The activity of the enterprise is considered from the point of view of management based on an extended cybernetic model. Within the framework of an enterprise managed at the level of three strata – strategic, tactical and operational – the need to add a function to the intelligent system to unite all levels of management is justified. It is proposed to use an intelligent system in the budgeting process to implement the methodology the ABC-ABB-ABM and to develop a ricasting system as a modern management accounting format. The list of key functions of an intelligent management system is substantiated, which includes intelligent controlling, coordination of expert opinions, as well as collection and analysis of information from open sources.

ИНТЕЛЛЕКТУАЛЬНАЯ АВТОМАТИЗИРОВАННАЯ СИСТЕМА УПРАВЛЕНИЯ СОРТИРОВКОЙ ФРУКТОВ

The development of effective technologies to reduce labor costs for the production, storage and processing of agricultural products is a difficult task. One of the promising directions of increasing the efficiency of technological processes in the agro-industrial complex is the full use of vision systems and artificial intelligence algorithms. Assessment of the quality of crop production is applied at various stages of production and allows detecting its damage, including during sorting. Due to the high productivity of production lines at the facilities of the agro-industrial complex, it seems effective to use intelligent information and control systems operating on the basis of machine learning algorithms. (Research purpose) The research purpose is proposing an approach to the creation of an intelligent automated control system for the fruit picking process using a vision system. (Materials and methods) Considered the structure of the information and control system of fruit sorting using a system of technical vision and machine learning. We implemented the test architecture of the neural network using the Keras library based on the Python language, the research was carried out in the google.collaborate service. A prepared data set based on images obtained on the production line was used to train the neural network. (Results and discussion) The structure of the convolutional neural network architecture was obtained, which allows us to adequately and with a sufficient accuracy solve the problem of classifying fruits into healthy and damaged ones in order to integrate it into an intelligent information and control sorting system. (Conclusions) Proposed an approach to improving the fruit sorting process, for example, for further processing into juice, using a technical vision system and machine learning. The structure of a convolutional neural network was developed to solve the problem of classifying apples by appearance, which became the basis for creating an intelligent information and control system for assessing the area of damage to the surface of fruits.

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.