Intelligent Information Systems Research Articles

The design of intelligent highway transportation system in smart city based on the internet of things.

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Design of a Universal Library of Components for the Development of Medical Systems in the Field of Thyroidology

Reducing the time and cost of developing application information systems by increasing the efficiency of the work performed is one of the main priorities of their modern production. With the development of AI technologies in all areas of activity, including medicine, there is a need for continuous improvement of both the client and server parts of information systems for many different nosologies. The purpose of this study is to form a ready-made library of components for the template design of user interfaces in the field of thyroidology (a field of medicine aimed at diagnosing and treating thyroid diseases) based on the experience gained in the development of several medical information systems for identifying thyroid diseases. Ensuring a conveyor mechanism for developing interfaces for medical information systems is possible only with specialized components of the tool based on a detailed understanding of the specifics of the actions of an endocrinologist. The components of the developed library cover up to 60 % of the component base of the interface of thyroidological systems, and are divided into several parts: for manipulating medical images and texts, annotating, creating input forms, working with tables of medical values, etc. The developed component library has been tested in the creation of intelligent information systems for the work of doctors of ultrasound and cytological diagnostics of thyroid diseases.

Falling behind again? Characterizing and assessing older adults' algorithm literacy in interactions with video recommendations

AbstractAlgorithms play a significant role in shaping our experiences of interacting with intelligent information systems but also inherit and amplify data biases, potentially leading to unfair decisions or discriminatory outcomes. This motivates us to investigate users' algorithm literacy, which covers the awareness and knowledge of algorithms and the skills to intervene in the operations of personalization algorithms when interacting with recommendation systems. Since vulnerable groups are more likely to suffer from the negative consequences of algorithmic decision‐making, investigating algorithm literacy among such groups is critical. This study aims to examine older adults' algorithm literacy, who are often considered a vulnerable group and labeled as digital laggards in contemporary information society. The empirical evidence collected from 21 participants in in‐depth interviews and cognitive mapping studies demonstrated that almost all participants are algorithm‐aware to some extent and identified (1) three types of information and sources collected by algorithms in user understanding, (2) two paradigms of how respondents understand personalized recommendations, and (3) two sets of strategies they develop to employ algorithms for improving user experience. The findings shed light on designing human‐centered intelligent information systems for unbiased personalization and developing a more inclusive AI‐assisted society that equally benefits people of all ages.

Development of an intelligent geographic information system for mountain roads monitoring: Ground data collection and analysis

This study examines the development of an intelligent geographic information system for mountain road monitoring. To make well-informed and timely management decisions in the planning, construction, and maintenance of mountain roads, and to anticipate potential critical situations and their effects on road conditions, it is imperative to employ modern methodologies. This study involves data collection from a specific section of a mountain road situated near Almaty, Kazakhstan. We gathered data on the road section through visual assessment, measurements of the coefficient of adhesion, and the acquisition of material samples from the structural layers of the roadbed. Based on the visual assessment and surface adhesion measurements, it became evident that the deformation of the studied road section, which includes a network of cracks and "non-standard" transverse cracks, is attributed to mountain mass shifts and water flows from slopes. Laboratory analysis of the collected samples revealed that excessive water saturation could result in cracks during freeze-thaw cycles, while a low resistance coefficient would lead to softening during humid conditions. An Intelligent Geographic Information System (IGIS) will incorporate the collected data alongside open remote sensing and weather data. The objective is to utilize this integrated system in the future for forecasting road conditions and maintenance requirements for similar roads in mountainous regions of the area. The measurements are carried out according to the state standards. That makes the system useful for government management purposes.

B-013 Improving the Workflow of Blood and Coagulation Tests in clinical laboratory through the Utilization of Laboratory Automation Track Systems

Abstract Background The Laboratory Automation Track System (TLA) is an automated system designed for executing highly repetitive tasks within a laboratory, commonly applied in biochemical and immunological testing systems. It aims to reduce human errors in specimen handling, enhance the overall process quality, and provide better Turnaround Time (TAT) from specimen collection to result reporting. For the first time in our laboratory, we have integrated the blood and coagulation systems with TLA. Consequently, we seek to evaluate the differences in the testing workflow and reporting efficiency of the blood and coagulation systems after being connected to TLA. Methods Abbott GLP systems Track was the TLA system for connecting with blood and coagulation systems. Two Sysmex XN-9100 fully automatic hematology systems and two Sysmex CS-5100 fully automatic coagulation analyzers were developed for blood and coagulation system, respectively. TUNYEN Laboratory Information System was established for Laboratory information system (LIS). Results Through the analysis of quality indicators and integration with intelligent information systems, improvements have been made, including the reduction of blood collection tube usage, efficient manpower utilization management, tracking of specimen progress and achievement of timely reporting rates.(1)Approximately 2,915 blood collection test tubes are saved every month, with a converted cost of 0.15 USD per tube, and a total saving of 437 USD. And reduce the carbon emissions of 32 kgCO2e, comply with the spirit of ESG sustainable development.(2)The TLA no longer requires staff to collect and deliver specimens as in the past,reducing staff and enhancing work efficiency.One of manpower was saved and new inspection items were developed.(3)Tracking of specimen progress:The laboratory has set up a specimen tracking system to monitor the specimen delivery location and provide timely response to the clinical status.The one-hour TAT for blood and coagulation increased from 82.0% and 76.9% before improvement to 91.8% and 93.3% after improvement respectively. Conclusions The implementation of Laboratory Automation Track Systems (TLA) leads to a reduction in medical errors and specimen sample volume, while also enhancing accuracy and precision, thereby improving the safety of laboratory staff. Although the introduction of TLA may require an initial investment, it brings the benefits of automation, offering ease and efficiency to laboratory processes.

Опыт внедрения информационной системы с технологиями искусственного интеллекта в работу поликлиники для оптимизации организации периодических медицинских осмотров.

Organizing medical examinations for patients with minimal time losses is the most difficult task for an outpatient clinic when creating a New model of a medical organization providing primary health care. Since during medical examinations it is necessary to perform a whole range of works associated with certain sets of actions, the authors developed an information system with technologies of artificial intelligence, which was tested during periodic medical examinations. The purpose of the study is to develop and test an information system with technologies of artificial intelligence that allows balancing the capabilities of a medical organization and the needs of the patient when undergoing periodic medical examinations. Materials and methods. The tool for solving this problem was an artificial intelligence platform based on the principle of decision making by controlling the positions of attractors in the state space of the Hopfield neural network, which was built into the medical information system using HL7 FHIR. Results. The result of the work was the “Smart Clinic” project, which provides for the creation of an information system with technologies of artificial intelligence for routing patients during periodic medical examinations. The information system provided dynamic routing of patients in real time with the construction of the shortest trajectory and minimal movement across the floors of the building, based on current information about the workload of specialists and the duration of each stage of the examination. Conclusions. The use of an artificial intelligence information system in the outpatient clinic made it possible to reduce the average duration of a patient’s trajectory during a periodic medical examination by 45%, the average waiting time for an appointment – from 7 minutes to 3 minutes, and the total time for undergoing a preventive examination – from 200 to 150 minutes. This made it possible to increase the number of periodic medical examinations per year by 1.5 times, and the level of patient satisfaction reached 95%.

Low-altitude intelligent transportation: System architecture, infrastructure, and key technologies

In the context of the burgeoning low-altitude economy, low-altitude intelligent transportation (LAIT) has emerged as the focal point of research. This study comprehensively explores the current state, challenges, and future development prospects of LAIT from three key aspects: system architecture, infrastructure, and critical technologies. First, we propose a future LAIT system framework based on a cyber-physical system (CPS) layered architecture to provide a potential solution for urban air transport. Second, we introduce a framework for the entire lifecycle and management chain of the LAIT system, offering an in-depth analysis of each stage from design, construction, and operation to management, with the aim of realizing intelligent management and operation of low-altitude transportation. Finally, this study discusses the technical, security, and social challenges that future LAIT will face in the context of Industry 4.0, while envisioning the pathways of technological innovation. It summarizes the need for advanced infrastructure and breakthroughs in key technologies such as the Internet of Things (IoT), 6G, artificial intelligence (AI), and low-altitude geographic information system. This study provides a systematic framework and technical guidelines for the future development of low-altitude intelligent transportation, supporting continuous innovation, and upgrading the low-altitude economy.

Principles of constructing mental models of a solution for external users in the task of generating explanations in an intelligent system

The subject of the research is the process of constructing user mental models in intelligent information systems. The aim is to improve the principles of building mental models of external users regarding the decisions of an intelligent information system to form explanations in accordance with both the needs of these users and the limitations on the use of solutions in a specific subject area. Objectives: improvement of existing principles of mental model construction based on structuring the description of the intelligent system's solution; development of a principle for constructing mental models of the intelligent system user, considering the limitations when using the obtained solution. The scientific novelty of the obtained results is as follows. The existing principles of human mental model construction have been adapted, considering the differences in constructing explanations for a solution in an intelligent system represented as a «black box». The adapted principles consider the correspondence between the structure of the mental model and the solution, the multiplicity of mental models regarding the solution, and the incompleteness of input data for decision-making. The principle of input data supplementation is proposed, which consists in using the values of solution properties that are irrelevant from the user's point of view to limit the set of relevant solutions. The use of this principle in constructing an explanation makes it possible to consider the negative aspects of using the obtained result and thereby simplify the user's choice and application of this solution.

Artificial intelligence, machine learning and GIS in environmental engineering: current trends

The use of computational tools, such as Artificial Intelligence, Machine Learning or Geographic Information Systems, has had a significant impact on the knowledge generated on environmental issues in recent years. The number of publications shown in this preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance. Search filters and keywords such as water, air, soil, climate change, energy and waste were used. The data obtained was processed to visualize the proportion of use in key topics of environmental interest, giving scientific guidance towards the sites of greatest applicability, as well as towards areas that deserve to be reinforced. In this way, this work aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.

Implementing Model Predictive Control (MPC) in Steer-by-Wire Systems for Future Automated Vehicles

Abstract The need for future green sustainable transportation represents a motivation for the researchers in order to create a better society. In this paper a proposed Steer-by-Wire system for future automated vehicles is analysed by implementing model predictive control (MPC) controller that would optimise the desired behaviour of the vehicle. By using MATLAB/Simulink, a bicycle vehicle model is simulated in standardised test manoeuvres according to standard ISO:7401. These results are used as referent data for the desired trajectory of the vehicle and as input data for the MPC controller. The idea of using these results for the controller is generated by the desire of creating a shared information intelligent systems. Shared information and control in the future intelligent systems would be crucial for creating the desired sustainable transport. By traveling, the vehicle would collect data of the desired measured variables and those data would be send to every other vehicle. Therefore, the next vehicle would possess the previous data and would try to improve the vehicle response and safety. Every optimised trajectory would be shared with the rest of the vehicles and this cycle would result in further optimization and in creation of a safer, more comfortable and more sustainable transport. The optimisation is done by the MPC controller which is responsible for defining the steering wheel angle and therefore creating the Steer-by-Wire system that is entirely defined by the MPC controller without driver commands. By comparing the trajectory of the automated vehicle and the desired trajectory gained from the previous simulations, the desired steering wheel angle is defined. This results in creation of a Steer-by-Wire system that improves the vehicle dynamics, safety and dynamic response of the vehicle. The mathematical modelling, results of the simulations and advantages of using the MPC controller for the Steer-by-Wire system in automated vehicle are presented.

A web intelligence information system to support the production of EuroGroups Register (EGR) statistics

There is a growing demand for statistics to better understand the globalisation that is accelerating due to the removal of barriers in international trade and to technological progress. Key players in globalisation are the multinational enterprise (MNE) groups that have increased in number and complexity and need to be properly represented by macroeconomic and business statistics. To deal with this need, the European Union Member States, the European Free Trade Association (EFTA) countries and Eurostat have collaborated to create the EuroGroups Register (EGR). This paper explores the use of web intelligence to improve the accuracy and completeness of the EGR, which makes use of tools for extracting and exploring information from the World Wide Web. Additionally, it presents a methodology to assess the quality of the information retrieved from the web, based on an ex-post comparison with the official information contained in the EGR. The results provide indications about the possibility of using web intelligence tools to support MNE group monitoring and to complete the EGR data in cases where this information is missing. Finally, it dives deeply into the approach to harness Wikipedia as a data source and into the techniques and methodology used to extract data from this source.

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Preface: 3rd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2024)

Preface: 3rd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2024)

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.

Information-analytical support to business processes for making investment decisions

The object of this study is the business processes of making an investment decision based on determining the state of the investment attractiveness of the enterprise. To support the adoption of investment decisions under the conditions of a fast-moving and dynamic environment, information-analytical support to the algorithm using intelligent information systems has been developed. The relevance of the study is justified by the continuous development of digitization processes, in particular in the financial realm. The traditional approach to the reproduction of management decision-making technology is complemented by the tools and methods of intelligent information systems. In particular, the modeling of the target subject area using UML made it possible to determine the main requirements for the projected information-analytical support (user roles, available options, types of connections and the logic of interaction between them). SQL queries to the information database speed up the process of processing and obtaining the necessary data samples. Business intelligence (BI) tools are used to create interactive reports that provide access to operational financial data. At the stage of making investment decisions, these tools make it possible to study a wide range of analytical data based on the results of the assessment of the investment attractiveness of the enterprise obtained at the previous stage of the developed algorithm. Monitoring of the main indicators of the enterprise's investment attractiveness is carried out on the basis of a dashboard, an information panel (display) with graphs, tables, and figures that clearly reflect the dynamics and rates of change of the investigated indicators. The results of the use of algorithmic information-analytical support make it possible to quickly prepare and make investment decisions. A visual description of the projected information-analytical support, visual content of the results of investment analysis, the validity of decisions due to the use of reliable retrospective information from an aggregated database

Construction of explanations at the local level of decision-making process representation for internal users of the intelligent system

The subject of study in the article is the processes of constructing explanations for the obtained results and the sequence of decision-making actions in an intelligent information system. The goal is to develop an approach to constructing explanations at the local level of representation of the decision-making process of an intelligent system based on building temporal and causal dependencies, which creates conditions for identifying "bottlenecks" of this process when representing the intelligent system as a "gray" box. Tasks: development of an explanation model at the local level of representation for internal users of an intelligent system; development of a method for constructing explanations at the local level of representation of the decision-making process of an intelligent system. The approaches used are: methods of constructing explanations, approaches to constructing temporal knowledge. Conclusions. The scientific novelty of the obtained results is as follows. An explanation model at the local level of representation of the decision-making process for internal users of an intelligent information system has been developed. The model uses a combination of temporal dependencies that determine the linear sequence of process actions and causal rules-constraints that specify mandatory actions performed for all variants of the decision-making process. The proposed model creates conditions for identifying actions that limit the normal execution of the decision-making process and lead to results that do not meet the requirements of external users. A method for constructing explanations at the local level of representation of the decision-making process of an intelligent system has been developed. The method involves identifying temporal dependencies between pairs of consecutive states of the current decision-making process, selecting temporal dependencies that are performed for all variants of the decision-making process, and forming causal dependencies based on them. Using the method makes it possible to present decision-making constraints as an explanation, which creates conditions for identifying bottlenecks in the sequence of actions for obtaining a result in an intelligent system.

Application of AI Technology for the Development of Destination Tourism towards an Intelligent Information System

"The pervasive influence of Artificial Intelligence (AI) technology is fundamentally transforming industries and daily life. AI, an emulation of human intelligence in machines, endows systems with the capacity to autonomously learn reason and make decisions. By processing extensive datasets at unprecedented speeds, AI is ushering in a revolution across diverse domains, including healthcare, finance, education and manufacturing. Its applications span from elevating customer experiences through personalized recommendations to propelling advancements in areas like autonomous vehicles and precision agriculture. With the tourism industry in transformative evolution, AI is pivotal in enhancing visitor experiences, streamlining operations, and fostering sustainable destination management. AI-powered solutions personalize and simplify the entire traveller journey, offering tailored recommendations for pre-trip planning, on-site experiences and post-travel engagement. This not only boosts customer satisfaction but also elevates the overall appeal and competitiveness of destinations. Moreover, AI optimizes operational processes by utilizing predictive analytics and machine learning for demand forecasting, resource allocation, and crowd management. This facilitates data-driven decision-making, promoting efficient resource utilization and minimizing environmental impact. In sustainable tourism, AI monitors and manages environmental factors, employing smart sensors and analytics to assess ecological impact. This enables the implementation of proactive measures to preserve natural resources and protect biodiversity, contributing to smart infrastructure development for destination resilience. The integration of AI in destination tourism drives economic growth and enhances safety for travellers. AI-powered systems analyse real-time data to detect and respond to potential threats, ensuring a secure environment. As the tourism industry evolves, embracing AI becomes imperative for destinations to remain competitive, sustainable, and resilient. This abstract provides insights into the diverse applications of AI in destination tourism, underscoring benefits for tourists and destination management stakeholders."

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.

Method for testing the stability of an autoregressive model of the vocal tract and adjusting its parameters

Within the framework of the traditional direction of research in the field of acoustic measurements, an autoregressive model of the vocal tract as a key link in the human speech apparatus is considered. The acute problem of ensuring the stability of the autoregressive model in systems with adaptation of its parameters to the observed speech signal of short duration is pointed out. To overcome this problem, the task was set of testing the stability of the autoregressive model and adjusting its parameters based on the results of this testing. The study is based on the author’s method of formant analysis of vowel sounds of speech through the synthesis of a recursive shaping filter in the free oscillation mode. To solve sated task, a method is proposed for testing the stability and adjusting the parameters of the autoregressive model of the vocal tract based on a two-stage algorithm for its transformation. At the first stage of transformation, the stability of the autoregressive model is tested using the impulse response of the shaping filter. At the second stage, if the stability of the autoregressive model is violated, its impulse response is modified by element-by-element multiplication by a variable exponential value that asymptotically converges to zero. A regular algorithm has been developed for recalculating the modified impulse response into an adjusted vector of autoregressive parameters at the second stage of transformation. Based on the results of experimental testing of the proposed method, it was concluded that guaranteed stability of the autoregressive model of the vocal tract has been achieved with minimal distortion in the frequency domain. The results obtained are useful in the development and modernization of automatic speech recognition systems, digital speech communications, artificial intelligence and other information systems that use data compression and speech coding based on an autoregressive model of the vocal tract in automatic speech signal processing.