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The object of this research is the processing of synthetic aperture radar (SAR) images using artificial intelligence. The subject of the study focuses on the utilization of artificial intelligence for the object detection on SAR images. The primary goal of this thesis is to investigate the principles of SAR operation, analyze various systems for detecting anomalous objects in soil, develop an intelligent system for processing SAR images, and evaluate the potential of the developed system for the classification of explosive objects. The research methods include the analysis of existing literature and programming in Python. The findings and materials from this thesis are recommended for use in the analysis of current underground anomaly detection systems, the potential application of artificial intelligence and machine learning in demining processes, and the examination of radar image processing methods.

The purpose of the study is to develop an approach to planning the trajectory of a robot manipulator using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of robot manipulators and the review of intelligent control systems provided a comprehensive picture of the current state of this issue. A system is proposed that can perceive the environment and controls the movement of the robot by generating correct control commands. For this purpose, 3 tasks were solved, namely, the analysis of the environment in order to determine its features, the determination of the trajectory in order to neutralize the collision, and the determination of controlled influences for the executive bodies in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks is proposed. The proposed approach is compared with existing approaches on key parameters, such as the execution time of the planned movement and the time of calculating the movement trajectory. The results confirmed that the use of neural network to optimize the trajectory and dynamic prediction to avoid obstacles significantly increased the adaptability of the system to the changing conditions of the production environment, which opens up new opportunities for improving automated processes and providing optimal conditions for the functioning of manipulator robots in real-time.

The purpose of the study is to develop an approach to planning the trajectory of the manipulator robot using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of manipulator robots and the review of intelligent control systems made it possible to obtain a complete picture of the current state of this issue. A system is proposed that can perceive the environment and control the movement of the robot by generating the correct control commands. For this, 3 tasks were solved, namely: analysis of the environment in order to determine its features, determination of the trajectory in order to neutralize the collision and determination of controlled influences for the executive authorities in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks are proposed.

The object of research is the process of automating systems for structuring data from several sources. The subject of the research is methods and algorithms for implementing a complete system for automated and parallel processing, validation and structuring of data. One of the most problematic areas is the merging of databases with different structures and several common fields into a generalized structure. The research was aimed at developing a system to increase the efficiency of automation of big data processing. As a result of the work, optimization methods were studied, the influence of their internal parameters on the operation of algorithms was analyzed, their main advantages and disadvantages were determined, and software was developed in which the corresponding methods were implemented. An algorithm for structuring data before processing has been obtained. Data structuring is achieved by performing the «mapping» operation. Mapping can take place by indexes of already cleaned data or using a defined dictionary with a given set of keys, which allows not to care about the sequence of storing values and their possible shift. The practical significance of the developed system lies in the improvement of methods of collecting and processing information for the purpose of its further validation, cleaning and accumulation in the following categories: geographic addresses and geo-coordinates, validation and automated addition of a mobile phone number to the international format, processing of car numbers (in modern and outdated format), VIN code of the engine and car brand, validation of urls of social networks, passport data and processing of personal data. Compared to similar methods for processing large volumes of data, the possibility of splitting the input file or stream into separate parts was used, the cleaned data from which is combined at the end of the system operation. Thanks to this, it is possible to process data whose size exceeds the available volume o

The subject of this research is the processes involved in generating explanations for decision-making in artificial intelligence systems. Explanations in such systems enable the decision-making process to be transparent and comprehensible for the user, thereby increasing user trust in the obtained results. The aim of this work is to develop an approach for constructing a probabilistic causal explanation model that takes into account the equivalence classes of input, intermediate, and resulting data. Solving this problem creates conditions for building explanations in the form of causal relationships based on the available information about the properties of input data as well as the properties of the results obtained in the artificial intelligence system.  To achieve this aim, the following tasks are addressed: developing a causal dependency model between the equivalence classes of input and output data; developing methods for constructing equivalence classes of data in the decision-making process and a method for constructing causal explanations. A probabilistic model of causal dependency is proposed, which includes a causal relationship between the equivalence classes of input or intermediate and resulting data obtained during the decision-making process in the artificial intelligence system. This relationship considers the estimates of the possibility and necessity of such a dependency. The model creates conditions for explaining the possible causes of the obtained decision. A set of methods for constructing equivalence classes of data in the decision-making process and for constructing causal explanations is proposed, establishing a causal relationship between the equivalence classes. When constructing equivalence classes, relations of mandatory and optional data refinement, requirements or exclusions of data, as well as data conjunctions, are established. When constructing causal explanations, the possibility and limitations of the necessity of such a depe

By analysing the results of ab initio simulations performed for Mn3Si2X6 (X = Se, Te), we first discuss the analogies and the differences in electronic and magnetic properties arising from the anion substitution, in terms of size, electronegativity, band widths of p electrons and spin-orbit coupling strengths. For example, through mean-field theory and simulations based on density functional theory, we demonstrate that magnetic frustration, known to be present in Mn3Si2Te6, also exists in Mn3Si2Se6 and leading to a ferrimagnetic ground state. Building on these results, we propose a strategy, electronic doping, to reduce the frustration and thus to increase the Curie temperature (TC). To this end, we first study the effect of electronic doping on the electronic structure and magnetic properties and discuss the differences in the two compounds, along with their causes. Secondly, we perform Monte-Carlo simulations, considering from the first to the fifth nearest-neighbor magnetic interactions and single-ion anisotropy, and show that electron doping efficiently raises the TC

This article analyzes in detail the actual problem of the spread of disinformation through various media resources and their impact on society. The main aspects of information consumption by Ukrainian society, in particular on the Internet, are highlighted, and the potential target audience is determined. Based on the analysis, a new intelligent system based on Natural Language Processing technology is proposed, which provides users with a comprehensive overview of the activity of each media resource. Available analogues with their advantages and limitations are examined in detail, emphasizing the significant advantages of the proposed intelligent system for increasing information literacy and countering disinformation. Figs.: 3. Refs.: 9 titles

Scientific and scientific-technical information is a valuable tool for the development of education, technology and society as a whole. The increase in the volume of information and the development of information networks of data exchange requires special means to ensure information protection of data. Methods, means and systems for information security of scientific, technical and scientific-educational resources in intellectual information systems are of particular importance. The purpose of the research is to develop methods and software tools to ensure information security of valuable scientific and technical information resources in intelligent information systems. The proposed solution for intrusion detection in intelligent system is a web application firewall, which is used for enhanced security, detecting and preventing attacks before they reach the web application. It will protect the system from a whole range of attacks while allowing HTTP traffic monitoring and analyzing small changes or persistent state online. The Web Application Firewall (WAF) has the following features: logging of all HTTP protocol transactions, including request termination permissions and logging of the response; HTTP traffic can be examined in real time to detect attacks; preventing attacks before they reach the web application. This work is performed within the framework of the project on creation of an integrated intelligent system “SMART TUIT”, which includes several subsystems (Information Retrieval, Voice Recognition, Pattern Recognition, Scientific Information Assessment, Geoinformation System).

Subject of the Research: The creation of an intelligent assistance system for people with visual impairments. Nowadays, the task of developing effective intelligent assistance systems that allow people with vision problems to achieve maximum independence is important and relevant, as existing systems have a number of drawbacks, such as limited autonomy, limited integration with other devices and systems, limited analysis of dynamic obstacles, and limited user feedback capabilities, which in most cases are restricted to voice guidance. Objective: The objective of this work is to create a generalized functional model of an intelligent assistance system for people with visual impairments, which has enhanced autonomy, integration with other devices and systems, the ability to analyze dynamic objects and predict their movement trajectory, and provide diverse feedback to the user. Tasks: To achieve the set objective, the following tasks were accomplished: a generalized functional model of the proposed intelligent assistance system for people with visual impairments was created; the functional dependencies of the components of the developed model were substantiated; a review of the basic modules of the proposed system model was conducted. Methods: The methods used include functional modeling methods and system analysis methods. Results: The following results were obtained: a functional model of an intelligent assistance system for people with visual impairments was proposed. This system surpasses existing analogs in a number of functional capabilities: detection of static and dynamic obstacles with prediction of dynamic obstacles’ movement trajectory, the ability to operate in various conditions (indoors, outdoors, in light or dark, in different weather conditions), support for integration with other systems and devices, and a high level of autonomy. Conclusions: The developed system model has enhanced autonomy, integration with other devices and systems, the abilit

Scientific and scientific-technical information is a valuable tool for the development of education, technology and society as a whole. The increase in the volume of information and the development of information networks of data exchange requires special means to ensure information protection of data. Methods, means and systems for information security of scientific, technical and scientific-educational resources in intellectual information systems are of particular importance. The purpose of the research is to develop methods and software tools to ensure information security of valuable scientific and technical information resources in intelligent information systems. The proposed solution for intrusion detection in intelligent system is a web application firewall, which is used for enhanced security, detecting and preventing attacks before they reach the web application. It will protect the system from a whole range of attacks while allowing HTTP traffic monitoring and analyzing small changes or persistent state online. The Web Application Firewall (WAF) has the following features: logging of all HTTP protocol transactions, including request termination permissions and logging of the response; HTTP traffic can be examined in real time to detect attacks; preventing attacks before they reach the web application. This work is performed within the framework of the project on creation of an integrated intelligent system “SMART TUIT”, which includes several subsystems (Information Retrieval, Voice Recognition, Pattern Recognition, Scientific Information Assessment, Geoinformation System).

This study used digital image processing and an artificial neural network (ANN) to determine the compressive strength of concrete in reinforced concrete buildings without coring. First, 32 concrete samples were produced in the laboratory, with different water-to-cement ratios, aggregate types, amounts of binder, compression values applied to fresh concrete, and amounts of additive. Next, the locations of 192 cores were visualized, and the compressive strengths of their corresponding core samples were matched with the surface images of the concrete, which were then digitized by image processing. The digitized images were the input layer, and the training and testing procedures were performed using the ANN as an output layer. After testing, the model was validated in existing reinforced concrete buildings. For the verification process, 20 cores taken from randomly selected concrete buildings were used. Although the results obtained from the samples produced in the laboratory were satisfactory, the success rate of the samples taken from the field was limited. Finally, the findings of this study are compared against the literature on this subject, especially from the last two decades., En este estudio se utilizó procesamiento de imágenes digitales y una red neuronal artificial (ANN) para determinar la resistencia a la compresión del hormigón en edificios de hormigón armado sin tomar núcleos. Primero, se generaron 32 muestras de concreto en el laboratorio con diferentes proporciones de agua a cemento, tipos de agregado, cantidades de aglutinante, valores de compresión aplicada al concreto fresco y cantidades de aditivo. A continuación, se visualizaron las ubicaciones de 192 núcleos, y las resistencias a la compresión de sus correspondientes muestras se compararon con las imágenes de la superficie del hormigón, que se digitalizaron mediante procesamiento de imágenes. Si las imágenes digitalizadas fueron la capa de entrada, y los procedimientos de entrenamiento y prueba se realizaron utilizando la ANN como capa de salida. Después de las pruebas, el modelo se validó en edificios reales de hormigón armado. Para el proceso de verificación, se utilizaron 20 núcleos tomados de edificios de hormigón seleccionados al azar. Si bien los resultados obtenidos de las muestras producidas en el laboratorio fueron satisfactorios, el porcentaje de éxito de las muestras tomadas en campo fue limitado. Por último, se comparan los hallazgos del estudio con la literatura sobre este tema, especialmente de las últimas dos décadas.

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 th

The object of the study is the automotive industry of the Republic of Kazakhstan. The subject of the study is the management of the decision-making process in assessing the consumer capabilities of potential customers of car dealerships, the process of forecasting car pricing. A method using a global search engine optimization algorithm, a forest conveyor line with a random forest model with Bayesian optimization (RFBO), is proposed. The algorithm of the method is as follows: – obtaining and processing initial data taking into account the degree of uncertainty; – formation of the optimization vector; – creation of descendant vectors; – ordering of vectors in descending order; – reducing the dimension of the feature space; – knowledge base training. In the presented work, data from websites www.m.Kolesa.kz, www.Cars.com and the average values of the median salary in the Republic of Kazakhstan were used to create a knowledge base, the program code of the platform was created using the Visual Studio Code in the Python language. The task to be solved was to predict car prices and assess the consumer capabilities of potential car dealership customers. We evaluate our solution based on a dataset that was created by analyzing several car classified sites and data on potential customers. Our results show that the accuracy of the model training was 92.1 %, and the accuracy of forecasting car prices and evaluating the consumer capabilities of potential customers was 87.3 % – this is primarily due to lower prediction errors than those of the estimated regressors using the same set of input data, high-quality object mapping and a more competitive RFBO algorithm, superior to simple linear models. The developed software solution should be used for making automated management decisions by car dealerships and credit organizations

The object of this study is the processes of automated management of maritime safety by analyzing the manifestations of the human factor of sea navigators. The task solved is justified by the need for formal and logical analysis and intelligent identification of mental motivational states (MMS) of marine navigators whose actions can cause dangerous situations during the control of the ship’s movement. High accident rates due to the fault of the navigators, in the absence of automated means of monitoring their condition, cause a contradiction between the existing means of safety control in controlling the movement of the vessel and the modern requirements of navigation, which needs to be resolved. A safety management approach was devised that takes into account the specificity of navigational tasks and the p-adic classification of dangerous MMS for navigators. This has made it possible to create three security modes that are activated depending on the detected state of the navigator’s MMS. Features of the results are the combination of analysis by means of p-adic systems and intelligent methods of data processing. As a result, sufficient identification accuracy was obtained for more than 75 % of MMS through neural network training. Experimental data collected during the navigation watch, as well as on the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland), became the basis for simulation by means of neural networks. In turn, the training of neural networks made it possible to obtain sufficient identification accuracy by performing up to 3000 iterations. Overall, the learning rate of the neural network was 0.98, which indicates a high level of identification. From a practical point of view, the results could be used for the automated management of shipping safety, as well as for evaluating the level of adaptation of the navigator to dynamically changing conditions. The proposed approach provides opportunities for the application of modern intellige

The object of research is the processes of energy conversion based on the use of alternative energy sources with an intelligent control system of actuators. This technological advancement is part of the equipment at the laboratory of dispatch control over power supply of Ternopil Ivan Puluj National Technical University. The research and the designed system could increase the level of training of future specialists for production activities during the educational process. Another task addressed was to provide technological production with practically trained specialists who could successfully work under conditions of its high automation and informatization. As a result of the research, an operating model of a hybrid solar mini-power plant and a controller with a Wi-Fi module were constructed. On the basis of the designed controller, an intelligent control system of actuators was built, which is powered by this power plant. Owing to the flexible configuration system, the controller is easily adjusted for various production tasks while the controller software provides the possibility of updating and expanding its functionality in the future. The controller has a web interface that allows monitoring and debugging from browsers without using specialized applications. A feature of the designed intelligent control system is that it can operate continuously owing to power from a hybrid solar power plant. The power plant built operates both from solar energy and from a centralized network and rechargeable batteries under an automatic mode. The results of research and technological advancements could be useful in forming practical skills of would-be specialists in the design and implementation of energy-efficient technologies, as well as intelligent control systems in the electric power industry

Data Science is increasingly applied for solving real-life problems, both in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market. Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this scenario, we present DSBot, an assistant that can analyze the user data and produce answers by mastering several Data Science techniques. DSBot understands the research question with the help of conversation interaction, produces a data science pipeline and automatically executes the pipeline in order to generate analysis. The strength of DSBot lies in the design of a rich domain specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology provided by a conversational agent. We benchmarked DSBot on two sets of 100 natural language questions and of 30 prediction tasks. We empirically evaluated the translation capabilities and the autoML performance of the system. In the translation task, it obtains a median BLEU score of 0.75. In prediction tasks, DSBot outperforms TPOT, an autoML tool, in 19 datasets out of 30.

This article presents a systematic literature review of studies and proposals developed where a total of 15 scientific studies describing systems and devices designed to improve mobilization, and that allow monitoring, communication and safety in bus transportation in cities were found. The PRISMA methodology was applied, for this research, articles from journals and congresses between the year 2017 to 2022 were considered, with the objective of investigating the devices and systems available to be applied in an urban transport unit, classifying the type of technology used, portability and its functionality. Inclusion and exclusion criteria were applied so that certain research that had very relevant information could not be included since they were oriented to other types of transportation. As a result, the implementation of intelligent systems in urban bus transportation significantly improves mobility, efficiency and safety in cities, which translates into benefits for public transportation users and for society as a whole, Durante este artículo se presenta una revisión bibliográfica sistemática de los estudios y las propuestas desarrolladas, se han encontrado un total de 15 estudios científicos que describen sistemas y dispositivos inteligentes diseñados para mejorar la movilización, y que permiten la monitorización, comunicación y seguridad de los autobuses en las ciudades. Se aplicó la metodología PRISMA, para esta investigación se consideraron artículos de revistas y congresos entre el año 2017 al 2022, con el objetivo de investigar los dispositivos y sistemas inteligentes disponibles para ser aplicados en una unidad de transporte público urbano, clasificando el tipo de tecnología utilizada, la portabilidad y su funcionalidad. Se aplicaron criterios de inclusión y exclusión que permitieron incluir ciertas investigaciones que tenían información relevante y se descartaron algunos que estaban orientados a otro tipo de transportes. El principal resultado de este estudio, es que la implementación de sistemas inteligentes en el transporte urbano (autobuses) mejora significativamente la movilidad, la eficiencia y la seguridad en las ciudades, lo que se traduce en beneficios para los usuarios del transporte público y para el conjunto de la sociedad.

This article presents a systematic literature review of studies and proposals developed where a total of 15 scientific studies describing systems and devices designed to improve mobilization, and that allow monitoring, communication and safety in bus transportation in cities were found. The PRISMA methodology was applied, for this research, articles from journals and congresses between the year 2017 to 2022 were considered, with the objective of investigating the devices and systems available to be applied in an urban transport unit, classifying the type of technology used, portability and its functionality. Inclusion and exclusion criteria were applied so that certain research that had very relevant information could not be included since they were oriented to other types of transportation. As a result, the implementation of intelligent systems in urban bus transportation significantly improves mobility, efficiency and safety in cities, which translates into benefits for public transportation users and for society as a whole, Durante este artículo se presenta una revisión bibliográfica sistemática de los estudios y las propuestas desarrolladas, se han encontrado un total de 15 estudios científicos que describen sistemas y dispositivos inteligentes diseñados para mejorar la movilización, y que permiten la monitorización, comunicación y seguridad de los autobuses en las ciudades. Se aplicó la metodología PRISMA, para esta investigación se consideraron artículos de revistas y congresos entre el año 2017 al 2022, con el objetivo de investigar los dispositivos y sistemas inteligentes disponibles para ser aplicados en una unidad de transporte público urbano, clasificando el tipo de tecnología utilizada, la portabilidad y su funcionalidad. Se aplicaron criterios de inclusión y exclusión que permitieron incluir ciertas investigaciones que tenían información relevante y se descartaron algunos que estaban orientados a otro tipo de transportes. El principal resultado de este estudio, es que la implementación de sistemas inteligentes en el transporte urbano (autobuses) mejora significativamente la movilidad, la eficiencia y la seguridad en las ciudades, lo que se traduce en beneficios para los usuarios del transporte público y para el conjunto de la sociedad.

In 2020, the World Health Organization (WHO) proclaimed the existence of a pandemic originating from the coronavirus (COVID-19), the initial outbreak of which occurred in Wuhan, China. This virus has had a devastating impact, claiming the lives of thousands and affecting millions worldwide. Its symptoms, which include cough, fever, fatigue and dyspnea, resemble those of a common flu. Spread of the virus occurs primarily through respiratory particles emitted by infected people, which can be deposited in the eyes, mouth or nose of others. Two types of tests are used to confirm infection: reverse transcription-polymerase chain reaction (RT-PCR) and antigen testing. However, due to their processing, these tests can take time to provide definitive results. It is in this context that artificial intelligence and Machine Learning (ML) techniques are presented as valuable tools to improve virus detection in lungs in an efficient way. In this work, the implementation of a Convolutional Neural Network (CNN) for the early detection of patients with COVID-19 is proposed. A dataset composed of 3616 chest X-ray images is used, employing a pre-trained neural network named VGG16. Through training, optimal accuracy in classifying images into COVID and Pneumonia categories is achieved., En el año 2020, la Organización Mundial de la Salud (OMS) proclamó la existencia de una pandemia originada por el coronavirus (COVID-19), cuyo brote inicial tuvo lugar en Wuhan, China. Este virus ha tenido un impacto devastador, cobrando la vida de miles y afectando a millones en todo el mundo. Sus síntomas, que incluyen tos, fiebre, fatiga y disnea, se asemejan a los de una gripe común. La propagación del virus ocurre principalmente a través de partículas respiratorias emitidas por personas infectadas, las cuales pueden depositarse en los ojos, boca o nariz de otras personas. Para confirmar la infección, se utilizan dos tipos de pruebas: la prueba de reacción en cadena de la polimerasa con transcripción inversa (RT-PCR) y las pruebas de antígenos. Sin embargo, debido a sus procesamientos, estas pruebas pueden demorar en proporcionar resultados definitivos. Es en este contexto que la inteligencia artificial y las técnicas de Machine Learning (ML) se presentan como herramientas valiosas para mejorar la detección del virus en los pulmones de manera eficiente. En este trabajo, se propone la implementación de una Red Neuronal Convolucional (CNN) para la detección temprana de pacientes con COVID-19. Se utiliza un conjunto de datos compuesto por 3616 imágenes de rayos X de tórax, empleando una red neuronal preentrenada denominada VGG16. A través del entrenamiento, se logra una precisión óptima en la clasificación de las imágenes en las categorías de COVID y Neumonía.

In 2020, the World Health Organization (WHO) proclaimed the existence of a pandemic originating from the coronavirus (COVID-19), the initial outbreak of which occurred in Wuhan, China. This virus has had a devastating impact, claiming the lives of thousands and affecting millions worldwide. Its symptoms, which include cough, fever, fatigue and dyspnea, resemble those of a common flu. Spread of the virus occurs primarily through respiratory particles emitted by infected people, which can be deposited in the eyes, mouth or nose of others. Two types of tests are used to confirm infection: reverse transcription-polymerase chain reaction (RT-PCR) and antigen testing. However, due to their processing, these tests can take time to provide definitive results. It is in this context that artificial intelligence and Machine Learning (ML) techniques are presented as valuable tools to improve virus detection in lungs in an efficient way. In this work, the implementation of a Convolutional Neural Network (CNN) for the early detection of patients with COVID-19 is proposed. A dataset composed of 3616 chest X-ray images is used, employing a pre-trained neural network named VGG16. Through training, optimal accuracy in classifying images into COVID and Pneumonia categories is achieved., En el año 2020, la Organización Mundial de la Salud (OMS) proclamó la existencia de una pandemia originada por el coronavirus (COVID-19), cuyo brote inicial tuvo lugar en Wuhan, China. Este virus ha tenido un impacto devastador, cobrando la vida de miles y afectando a millones en todo el mundo. Sus síntomas, que incluyen tos, fiebre, fatiga y disnea, se asemejan a los de una gripe común. La propagación del virus ocurre principalmente a través de partículas respiratorias emitidas por personas infectadas, las cuales pueden depositarse en los ojos, boca o nariz de otras personas. Para confirmar la infección, se utilizan dos tipos de pruebas: la prueba de reacción en cadena de la polimerasa con transcripción inversa (RT-PCR) y las pruebas de antígenos. Sin embargo, debido a sus procesamientos, estas pruebas pueden demorar en proporcionar resultados definitivos. Es en este contexto que la inteligencia artificial y las técnicas de Machine Learning (ML) se presentan como herramientas valiosas para mejorar la detección del virus en los pulmones de manera eficiente. En este trabajo, se propone la implementación de una Red Neuronal Convolucional (CNN) para la detección temprana de pacientes con COVID-19. Se utiliza un conjunto de datos compuesto por 3616 imágenes de rayos X de tórax, empleando una red neuronal preentrenada denominada VGG16. A través del entrenamiento, se logra una precisión óptima en la clasificación de las imágenes en las categorías de COVID y Neumonía.

The digital transformation of our health care system will require not only digitization of existing tools but also a redesign of our care delivery system and collaboration with digital partners. Traditional patient journeys are reactive to symptom presentation and delayed by health care system-centric scheduling, leading to poor experience and avoidable adverse outcomes. Patient journeys will be reimagined to a digital health pathway that seamlessly integrates various care experiences from telemedicine, remote monitoring, to in-person clinic visits. Through centering the care delivery around the patients, they can have more delightful experiences and enjoy the quality of standardized condition pathways and outcomes. To design and implement digital health pathways at scale, enterprise health care systems need to develop capabilities and partnerships in human-centered design, operational workflow, clinical content management, communication channels and mechanisms, reporting and analytics, standards-based integration, security and data management, and scalability. Using a human-centered design methodology, care pathways will be built upon an understanding of the unmet needs of the patients to have a more enjoyable experience of care with improved clinical outcomes. To power this digital care pathway, enterprises will choose to build or partner for clinical content management to operationalize up-to-date, best-in-class pathways. With this clinical engine, this digital solution will engage with patients through multimodal communication modalities, including written, audio, photo, or video, throughout the patient journey. Leadership teams will review reporting and analytics functions to track that the digital care pathways will be iterated to improve patient experience, clinical metrics, and operational efficiency. On the backend, standards-based integration will allow this system to be built in conjunction with the electronic medical record and other data systems to prov

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

Artificial neural networks can be trained on useful signals of the source data, but can not be taught on noisy data, so it is usually performed noise reduction or error compensation. This paper implements a noise reduction model based on artificial neural networks to suppress high-noise components, which is important for optimizing pre-filtering methods. The process of cleaning computers’ tomography scans in medical examinations of patients with tuberculosis is considered as an given problem in which the suppression of noise present in the image is required.. In order to reduce the level of radiation due to it is quite harmful to human. the power of the radiation is reduced. As a result, the ratio of the useful signal to noise is reduced, which causes noise, which contaminates the image and complicates its processing. Additional shadows appears on the image that no objects exist, which can provide false diagnosis. An algorithm for structural-parametric synthesis of convolutional neural networks used in image noise suppression has been developed. Computer tomograms of tuberculosis patients provided by the Research Institute of Pulmonology and Tuberculosis of the National Academy of Medical Sciences of Ukraine were used as a training sample.

The article proposes a method of intelligent management of the interaction of the elements of the control system in mobile radio networks to ensure a high-quality level of interaction between the elements of the mobile radio network and a better level of management decision-making by the MR control system. To ensure effective management of the interaction of the elements of the control system in mobile radio networks in the conditions of the characteristic features of the functioning of mobile radio networks, in particular: frequent changes in the situation are possible only in the presence of a control system capable of making management decisions in conditions of uncertainty. To solve this problem, it is proposed to use an intelligent problem solving system based on the mechanism of neural networks. This will make it possible to obtain the best quality of identification, training, which in general will ensure a better level of management decision-making in the organization and assessment of the state of interaction of the elements of the control system in mobile radio networks. In contrast to similar methods and techniques used in computer or wired networks, which provide interaction between system or network elements through hardware control of transmission power, in the presented method it is proposed to solve the task of ensuring interaction between elements of the control system of mobile radio networks through the use of neural systems with functions: building a suitable database and knowledge base, forming a decision base for events, obtaining and identifying the received data, analyzing and processing the received input data, making a decision to ensure the interaction of elements, choosing and making a decision to ensure interaction, checking the conditions for ensuring structural interaction connection of elements, implementation of management decisions. The application of this technique will allow choosing the optimal management solution for managing the

In recent years, water pollution has become one of the major problems that countries around the world face. The needs for drinking water in a tourist city like Vlora, Albania have changed in the last twenty years both in terms of the size of the aqueduct and consumption of the population. Therefore, it is important to constantly monitor the quality of the water, to be able to intervene and combat water pollution. The present work proposes a monitoring network based on most important water parameter such as pH, Temperature and Dissolved Oxygen, which will be measured through a network of wireless sensors on the entire water network of the city. This testbed will consist in building a monitoring system that can be later used to monitor other parameters or even in other situations. The system proposed in this approach is a combination of different sensors, whose sole purpose will be the monitoring of the parameter in the water over different time intervals, a database and an expert system. All the information gathered from the sensors related to the Dissolved Oxygen in the water, will be stored in a database. This database will then serve as a focal point for the expert system. The expert system will consist of a knowledge base and an interface engine, which together will serve as a monitoring, diagnostic, signaling, scheduling and planning for specialized events. The knowledge base will function based on data from human experts related to water pollution, for the interference engine to be able to make decisions based on different findings and scenarios in the cases of deviations from the norm parameter. The experimental results gathered during the implementation of the project will serve as a model that in the future can help in other systems that can be used for monitoring the environment in an intelligent and sustainable way.

Global digital transformation of the energy sector has led to the emergence of multiple digital platform solutions, the implementation of which have revealed new problems associated with continuous growth of data volumes requiring new approaches to their processing and analysis. This article is devoted to the improper application of machine learning approaches and flawed interpretation of their output at various stages of decision support systems development: data collection; model development, training and testing as well as industrial implementation. As a real industrial case study, the article examines the power generation forecasting problem of photovoltaic power plants. The authors supplement the revealed problems with the corresponding recommendation for industrial specialists and software developers. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

El objetivo de este trabajo consiste en visualizar la gestión del riesgo de desastres (GRD) como promotora de sistemas educativos inteligentes. Esto debido a que la manifestación de un desastre en el sistema educativo puede tener múltiples afectaciones en su funcionalidad e, incluso, puede contribuir en el aumento de las brechas sociales entre los grupos más vulnerables, así como comprometer la calidad y la continuidad de los procesos educativos. El sistema educativo debe cumplir con su misión emancipadora, por tanto, es necesario que este sea un sistema sostenible y resiliente y esto puede ser posible si se logra transformar el sistema educativo en una organización inteligente que permita el trabajo sistémico entre este y la GRD., The objetive of this research is to visualize disaster risk management as a prometer of intelligent educational systems. This is due to the fact that the manifestation of a disaster in the educational system can have multiple effects on its functionality and can even contribute to the increase of social gaps between the most vulnerable groups, as well as compromising the quality and continuity of the educational processes. The educational system must fulfill its emancipatory mission; therefore, it is necessary for this to be a sustainable and resilient system and this may be possible if the educational system is transformed into an intelligent organization that allows systemic work between this and DRM.

The object of research is the processes of pulse metalworking (hydroexplosive, magnetic pulse, electrohydraulic, gas detonation forming, etc.). Among these methods of forming for the production of aircrafts engines parts from cylindrical and conical blanks, the most efficient in terms of its energy capabilities and overall dimensions is explosive. The modern level of theory and practice of metal forming processes allows, on the basis of a systematic approach and control theory, to determine the optimal parameters of plastic forming processes, select the best technical solutions, and create a precondition for the transition to complex automation. The most difficult task of metals forming methods optimizing is to find the best solution among many potentially possible ones, considering the introduced restrictions and efficiency criteria, environmental, economic, technical, ergonomic, and other requirements. The most problematic is that it is impossible to optimize the process of forming post-factum (finishing works, elimination of defects in shape and size, welding of cracks, etc. are required), therefore, when solving optimization problems, the implementation of the feedback principle is required - comparison of the value of the controlled variable, determined by the control program, with the desired value. In general, the processes of metal forming by pressure are characterized by a variety of problems of the theory of optimal control, the solution of which is carried out by methods of mathematical programming. And, although the equipment for pulse processing can have a different design, it necessarily includes structural elements that make it possible to convert the energy of the source and with its help (through the action of a solid body, transmitting medium, or field) to deform the metal of the workpiece. Due to this, in this work, it is proposed to control the quality of the obtained parts by varying the degree of deformation of the workpiece in the process of f

In this study, the objective is to develop an intelligent system for making decisions on the choice of methods for cleaning exhaust gases from sulfur and nitrogen oxides using the Case-Based Reasoning- (CBR). The task of automating the selection of effective methods for cleaning waste gases is urgent and meets the paradigm of sustainable development. A database on methods for cleaning exhaust gases from nitrogen and sulfur oxides was created. The potential use of intelligent inference on precedents from the database to select the most appropriate cleaning method for new emission stream data is considered. The work of the CBR method is represented as a life cycle, which has four main stages: Retrieving, Reusing, Revising and Retaining. The following characteristics of precedents were considered: degree of purification, initial concentration, temperature, presence of impurities, obtained product, material consumption, and energy consumption. All of these characteristics (in CBR attributes), except for the fourth and fifth, are given by numerical values with respective units of measurement and can be easily normalized. The presence of impurities and the product are categorical attributes with a certain set of values (classes). One of the main problems in CBR was solved: the problem of choosing the type of indexes. A set of all input characteristics of the precedent as indices is suggested to be used for the proposed decision support system (DSS) for methods of cleaning gas emissions. The first two phases of the CBR lifecycle use the k-nearest neighbor method to Retrieving and Reusing. The Euclidean metric is used to estimate the distances between precedents in the developed system. During the third and fourth phases of CBR, the intervention of the decision maker is provided. The process finishes with the adoption of the found solution and the possible storage of this solution in the base of use cases. An intelligent decision-making system has been developed for the sel

The approach, algorithm, and intelligent system of support of decision-making of management for forecasting of time fund for the performance of the mechanized chemical protection of plants are offered. They are based on the formation of a database and knowledge of the weather from the Open Weather Map service for individual countries and their regions. They provide the formation of databases and knowledge for a given country or its region, taking into account the characteristics of natural, climatic, and industrial conditions based on computer modelling. Also, the proposed intelligent management decision support system provides a systematically accountable set of variable agrometeorological components of the mechanized chemical plant protection system and their impact on the projected time fund of the relevant work. Based on the use of the developed intelligent system of support of acceptance of administrative decisions forecasting of time fund for the performance of the mechanized chemical protection of plants and the set natural-climatic and industrial conditions is executed. The climatically admissible time fund model for mechanized chemical protection of plants during the day for May, which is described by Weibull distribution, is substantiated. The obtained research results can be used by managers of agricultural enterprises during the management processes of forecasting the time fund for the implementation of mechanized chemical plant protection. The developed intelligent decision support system provides further research on forecasting the time fund for the implementation of mechanized chemical plant protection and substantiation of its models for different countries and their regions.

Subject: the use of the apparatus of temporal logic and probabilistic approaches to construct an explanation of the results of the work of an intelligent system in order to increase the efficiency of using the solutions and recommendations obtained. Purpose: development of a method for constructing explanations in intelligent systems with the ability to form and evaluate several alternative interpretations of the results of the operation of such a system. Tasks: justification for the use of the black box principle for interactive construction of explanations; development of a pattern explanation model that provides for probabilistic estimation; development of a method of interactive construction of explanations on the basis of the probabilistic approach. Methods: methods of data analysis, methods of system analysis, methods of constructing explanations, models of knowledge representation. Results: A model of the explanation pattern is proposed, which contains temporal regulations reflecting the sequence of user interaction with an intelligent system, which allows the formation of explanations based on a comparison of the actions of the current user and other well-known users. An interactive method for constructing explanations based on a probabilistic approach has been developed; the method uses patterns of user interaction with an intelligent system and contains phases of constructing patterns of explanations and forming explanations using the obtained patterns. The method organizes the received explanations according to the likelihood of use, which makes it possible to form target and alternative explanations for the user. Conclusions: The use of the black box principle for the development of a probabilistic approach to the construction of explanations in intelligent systems has been substantiated. A model of a pattern of explanations based on temporal regulations is proposed. The model reflects the sequence of user interaction with the intelligent system when rec

The approach, algorithm, and intelligent system of support of decision-making of management for forecasting of time fund for the performance of the mechanized chemical protection of plants are offered. They are based on the formation of a database and knowledge of the weather from the Open Weather Map service for individual countries and their regions. They provide the formation of databases and knowledge for a given country or its region, taking into account the characteristics of natural, climatic, and industrial conditions based on computer modelling. Also, the proposed intelligent management decision support system provides a systematically accountable set of variable agrometeorological components of the mechanized chemical plant protection system and their impact on the projected time fund of the relevant work. Based on the use of the developed intelligent system of support of acceptance of administrative decisions forecasting of time fund for the performance of the mechanized chemical protection of plants and the set natural-climatic and industrial conditions is executed. The climatically admissible time fund model for mechanized chemical protection of plants during the day for May, which is described by Weibull distribution, is substantiated. The obtained research results can be used by managers of agricultural enterprises during the management processes of forecasting the time fund for the implementation of mechanized chemical plant protection. The developed intelligent decision support system provides further research on forecasting the time fund for the implementation of mechanized chemical plant protection and substantiation of its models for different countries and their regions.

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

There is a growing use of intelligent systems to support human decision-making across several domains. Trust in intelligent systems, however, is pivotal in shaping their widespread adoption. Little is currently understood about how trust in an intelligent system evolves over time and how it is mediated by the accuracy of the system. We aim to address this knowledge gap by exploring trust formation over time and its relation to system accuracy. To that end, we built an intelligent house recommendation system and carried out a longitudinal study consisting of 201 participants across 3 sessions in a week. In each session, participants were tasked with finding housing that fit a given set of constraints using a conventional web interface that reflected a typical housing search website. Participants could choose to use an intelligent decision support system to help them find the right house. Depending on the group, participants received a variation of accurate or inaccurate advice from the intelligent system throughout each session. We measured trust using a trust in automation scale at the end of each session. We found evidence suggesting that trust development is a slow process that evolves over multiple sessions, and that first impressions of the intelligent system are highly influential. Our results echo earlier research on trust formation in single session interactions, corroborating that reliability, validity, predictability, and dependability all influence trust formation. We also found that the age of the participants and their affinity with technology had an effect on their trust in the intelligent system. Our findings highlight the importance of first impressions and improvement of system accuracy for trust development. Hence, our study is an important first step in understanding trust development, breakdown of trust, and trust repair over multiple system interactions, informing improved system design., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Web Information Systems

We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.

The article discusses the latest developments in the field of automation, as well as the use of intelligent control systems and modern technologies in the field oftechnology

The possibility to improve depth of interaction (DOI) resolution and coincidence time resolution (CTR) of a pixellated PET module by enabling light re-circulation inside it with a light guide is well known. Typically the light guide consists of a non-scintillating material of about 1 mm thickness. In this work, we propose to further extend the concept by replacing the passive light guide with a fast scintillating material, in order to combine the benefits of light re-circulation with a fraction of very fast events, where the DOI is precisely known.Several configurations with such an active layer are proposed and studied in this work by means of Monte Carlo simulations with experimental verification. First, the possibility of replacing the glass light guide with a layer of LYSO is investigated. This configuration allows to reach DOI resolutions beyond the possibilities of a simple glass guide, while retaining comparable performances in terms of energy and timing resolutions. Then, the performance of two fast scintillators (BaF2 and BC422) used as light guides, in combinations with crystal arrays made of both LYSO and BGO, is investigated. The fraction of shared events (i.e. those events where the 511 keV gamma ray scatters in the light guide and deposits the rest of its energy in the crystal array) in a 3 mm light guide is found to be around 1% for BC422, and 12.1% for BaF2. Therefore, the configuration using the latter material is investigated in depth, and two alternative readout schemes are proposed, to maximize the collection of light produced by BaF2. The results show that ∼ 100 ps FWHM CTR can be reached for shared events using a BaF2 light guide. Finally, the possibility to use such a detector design as a Compton camera is discussed.

The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfacto

The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfacto

There is a growing use of intelligent systems to support human decision-making across several domains. Trust in intelligent systems, however, is pivotal in shaping their widespread adoption. Little is currently understood about how trust in an intelligent system evolves over time and how it is mediated by the accuracy of the system. We aim to address this knowledge gap by exploring trust formation over time and its relation to system accuracy. To that end, we built an intelligent house recommendation system and carried out a longitudinal study consisting of 201 participants across 3 sessions in a week. In each session, participants were tasked with finding housing that fit a given set of constraints using a conventional web interface that reflected a typical housing search website. Participants could choose to use an intelligent decision support system to help them find the right house. Depending on the group, participants received a variation of accurate or inaccurate advice from the intelligent system throughout each session. We measured trust using a trust in automation scale at the end of each session. We found evidence suggesting that trust development is a slow process that evolves over multiple sessions, and that first impressions of the intelligent system are highly influential. Our results echo earlier research on trust formation in single session interactions, corroborating that reliability, validity, predictability, and dependability all influence trust formation. We also found that the age of the participants and their affinity with technology had an effect on their trust in the intelligent system. Our findings highlight the importance of first impressions and improvement of system accuracy for trust development. Hence, our study is an important first step in understanding trust development, breakdown of trust, and trust repair over multiple system interactions, informing improved system design., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Web Information Systems

We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.

[Abstract] (1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems

The subject matter of the article is the processes of automated construction of explanations on the operation of an intelligent system for use in the human-computer interface. The goal is to develop a dynamic model of explanation for the human-computer interface using temporal knowledge about the process of functioning of the intelligent system. Temporal knowledge makes it possible to set possible sequences of decision-making actions in an intelligent system based on the known temporal order for pairs of such actions. Tasks: to develop an approach to constructing explanations for the operation of an intelligent system based on the use of temporal knowledge; development of a three-aspect model of explanations using temporal knowledge. The approaches used are: approaches to the construction of knowledge representation based on temporal dependencies, approaches to the construction of chatbot answers using rules, as well as with their automatic generation. The following results are obtained. The structuring of aspects of explanation taking into account the possibilities of their description with the help of temporal knowledge is performed; a temporal approach to constructing an explanation is proposed; a dynamic explanation model using temporal rules has been developed. Conclusions. The scientific novelty of the results is as follows. A temporal approach to constructing explanations for the operation of an intelligent system is proposed. The approach describes explanation as a process consisting of a temporally ordered sequence of facts. The order of time for pairs of facts is determined by temporal rules. Such rules may define the explanation process with varying degrees of detail over time, depending on the request for clarification. Detailed explanations reflect the subject area model and include the basic and alternative sequences of actions performed by the intelligent system. The explanation of the basic patterns of the intelligent system makes it possible to inte