2023

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https://ela.kpi.ua/handle/123456789/57991

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Зараз показуємо 1 - 20 з 42

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A comprehensive survey on load balancing techniques for virtual machines
(КПІ ім. Ігоря Сікорського, 2023) Suman Sansanwal; Nitin Jain
Cloud computing is an emerging technique with remarkable features such as scalability, high flexibility, and reliability. Since this field is growing exponentially, more users are attracted to fast and better service. Virtual Machine (VM) allocation plays a crucial role in cloud computing optimization; hence, resource distribution is not impacted by machine failure and is migrated with no downtime. Therefore, effective management of virtual machines is necessary for increasing profit, energy-saving, etc. However, it could utilize the virtual machine resources more efficiently because of the increased load, so load balancing is more concentrated. The predominant purpose of load balancing is to balance the available load equally among the nodes to avoid overloading or underloading problems. The present study conducted an extensive survey on virtual machine placement to describe the application of prediction algorithms and to provide more efficient, reliable, high response, and low overhead VM placement. Furthermore, the survey attempted to overview the challenges in load balancing in VM placement and various ideas of state-of-the-art techniques to resolve the issues.
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A concatenation approach-based disease prediction model for sustainable health care system
(КПІ ім. Ігоря Сікорського, 2023) Tharageswari, K.; Sundaram, N. Mohana; Santhosh, R.
Abstract. In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF).
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A genetic algorithm improvement by tour constraint violation penalty discount for maritime cargo delivery
(КПІ ім. Ігоря Сікорського, 2023) Romanuke, V.V.; Romanov, A.Y.; Malaksiano, M.O.
Abstract. The problem of minimizing the cost of maritime cargo delivery is considered. The cost is equivalent to the sum of the tour lengths of feeders used for the delivery. The problem is formulated as a multiple traveling salesman problem. In order to find its solution as the shortest route of the tours of feeders, a genetic algorithm is used where we present two inequalities constraining the tour length of every feeder to lie between the shortest and longest lengths. Apart from the constant tour constraint violation penalty in the genetic algorithm, we suggest a changeable penalty as an exponential function of the algorithm iteration, where we maintain the possibility of the penalty rate to be either increasing or decreasing, whose steepness is controlled by a positive parameter. Our tests show that the changeable penalty algorithm may return shorter routes, although the constant penalty algorithms cannot be neglected. As the longest possible tour of the feeder is shortened, the changeable penalty becomes more useful owing to a penalty discount required either at the beginning or at the end of the algorithm run to improve the selectivity of the best feeder tours. In optimizing maritime cargo delivery, we propose to run the genetic algorithm by the low and constant penalties along with the increasing and decreasing penalties. The solution is the minimal value of the four route lengths. In addition, we recommend that four algorithm versions be initialized by four different pseudorandom number generator states. The expected gain is a few percent, by which the route length is shortened, but it substantially reduces expenses for maritime cargo delivery.
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A multi-level decision-making framework for heart-related disease prediction and recommendation
(КПІ ім. Ігоря Сікорського, 2023) Vedna Sharma; Surender Singh Samant
The precise prediction of health-related issues is a significant challenge in healthcare, with heart-related diseases posing a particularly threatening global health problem. Accurate prediction and recommendation for heart-related diseases are crucial for timely and effective treatment solutions. The primary objective of this study is to develop a classification model capable of accurately identifying heart diseases and providing appropriate recommendations for patients. The proposed system utilizes a multilevel-based classification mechanism employing Support Vector Machines. It aims to categorize heart diseases by analyzing patient’s vital parameters. The performance of the proposed model was evaluated by testing it on a dataset containing patient records. The generated recommendations are based on a comprehensive assessment of the severity of clinical features exhibited by patients, including estimating the associated risk of both clinical features and the disease itself. The predictions were evaluated using three metrics: accuracy, specificity, and the receiver operating characteristic curve. The proposed Multilevel Support Vector Machine (MSVM) classification model achieved an accuracy rate of 94.09% in detecting the severity of heart disease. This makes it a valuable tool in the medical field for providing timely diagnosis and treatment recommendations. The proposed model presents a promising approach for accurately predicting heart-related diseases and highlights the potential of soft computing techniques in healthcare. Future research could focus on further enhancing the proposed model’s accuracy and applicability.
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Academician Glushkov’s legacy: Human capital in the field of cybernetics, computing, and informatics at Igor Sikorsky Kyiv Polytechnic Institute
(КПІ ім. Ігоря Сікорського, 2023) Zgurovsky, M. Z.
Abstract. The role of academician Viktor Glushkov in the creation of scientific schools in the field of cybernetics, computing, and informatics at the Igor Sikorsky Kyiv Polytechnic Institute, which became a powerful national center for training specialists in this field, is considered. The significant influence of academician Hlushkov’s ideas on the formation of generations of scientists, who to this day continue to build a digital society in Ukraine and far beyond its borders, is shown.
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Algorithm for simulating melting polar ice, Earth internal movement and volcano eruption with 3-dimensional inertia tensor
(КПІ ім. Ігоря Сікорського, 2023) Matsuki, Yoshio; Bidyuk, Petro
Abstract. This paper reports the result of an investigation of a hypothesis that the melting polar ice of Earth flowing down to the equatorial region causes volcano eruptions. We assumed a cube inside the spherical body of Earth, formulated a 3-dimensional inertia tensor of the cube, and then simulated the redistribution of the mass that is to be caused by the movement of melted ice on the Earth’s surface. Such mass distribution changes the inertia tensor of the cube. Then, the cube’s rotation inside Earth was simulated by multiplying the Euler angle matrix by the inertia tensor. Then, changes in the energy intensity and the angular momentum of the cube were calculated as coefficients of Hamiltonian equations of motion, which are made of the inertia tensor and sine and cosine curves of the rotation angles. The calculations show that the melted ice increases Earth’s internal energy intensity and angular momentum, possibly increasing volcano eruptions.
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Algorithms of statistical anomalies clearing for data science applications
(КПІ ім. Ігоря Сікорського, 2023) Pysarchuk, O.; Baran, D.; Mironov, Yu.; Pysarchuk, I.
The paper considers the nature of input data used by Data Science algorithms of modern-day application domains. It then proposes three algorithms designed to remove statistical anomalies from datasets as a part of the Data Science pipeline. The main advantages of given algorithms are their relative simplicity and a small number of configurable parameters. Parameters are determined by machine learning with respect to the properties of input data. These algorithms are flexible and have no strict dependency on the nature and origin of data. The efficiency of the proposed approaches is verified with a modeling experiment conducted using algorithms implemented in Python. The results are illustrated with plots built using raw and processed datasets. The algorithms application is analyzed, and results are compared.
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Approach to positional logic algebra
(КПІ ім. Ігоря Сікорського, 2023) Kovalov, M.
The method of Boolean function representation in terms of positional logic algebra in compact operator form is offered. Compared with the known method, it uses position operators with a complexity of no more than two and only one type of equivalent transformations. The method is less labor intensive. It allows parallelizing logic calculations. The corresponding way of Boolean function implementation is developed. It competes with some known ways in terms of hardware complexity, resource intensity, and speed when implemented on an FPGA basis. Possibilities open up for creating effective automating means of representing Boolean functions from a large number of variables, synthesizing the corresponding LCs, and improving modern element bases.
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Assessment of the economical dimension of sustainable development of the ukraine’s regions based on the brightness of night lights
(КПІ ім. Ігоря Сікорського, 2023) Zgurovsky, M.; Yefremov, K.; Gapon, S.; Pyshnograiev, I.
Abstract. When assessing the level of development of territories, the problem of finding objective qualitative data that will characterize it arises. One of the possible sources of such data is the remote sensing of the Earth (RSE). The article is devoted to the analysis of the possibility of using the product of RSE – the map of night lights, for modeling the economical dimension of the sustainable development of the regions of Ukraine. Using the regression and correlation analysis and neural networks, appropriate models for assessing the level of economic development of the Kherson region, Donetsk region, and the AR of Crimea were obtained. The study was carried out by the team of the World Data Center for Geoinformatics and Sustainable Development of the Igor Sikorsky Kyiv Polytechnic Institute. It was part of research on the analysis of the behavior of complex socio-economic systems and processes of sustainable development in the context of the quality and safety of people’s lives.
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Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography
(КПІ ім. Ігоря Сікорського, 2023) Dharmadhikari, Dipa D.; Tamane, Sharvari C.
Abstract. Technology for Cloud Computing (CC) has advanced, so Cloud Computing creates a variety of cloud services. Users may receive storage space from the provider as Cloud storage services are quite practical; many users and businesses save their data in cloud storage. Data confidentiality becomes a larger risk for service providers when more information is outsourced to Cloud storage. Hence in this work, a Ciphertext and Elliptic Curve Cryptography (ECC) with Identity-based encryption (CP-IBE) approaches are used in the cloud environment to ensure data security for a healthcare environment. The revocation problem becomes complicated since characteristics are used to create cipher texts and secret keys; therefore, a User revocation algorithm is introduced for which a secret token key is uniquely produced for each level ensuring security. The initial operation, including signature, public audits, and dynamic data, are sensible to Sybil attacks; hence, to overcome that, a Sybil Attack Check Algorithm is introduced, effectively securing the system. Moreover, the conditions for public auditing using shared data and providing typical strategies, including the analytical function, security, and performance conditions, are analyzed in terms of accuracy, sensitivity, and similarity.
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Basic algorithm for approximation of the boundary trajectory of short-focus electron beam using the root-polynomial functions of the fourth and fifth order
(КПІ ім. Ігоря Сікорського, 2023) Melnyk, I.; Pochynok, A.
Abstract. The new iterative method of approximating the boundary trajectory of a short-focus electron beam propagating in a free drift mode in a low-pressure ionized gas under the condition of compensation of the space charge of electrons is considered and discussed in the article. To solve the given approximation task, the rootpolynomial functions of the fourth and fifth order were applied, the main features of which are the ravine character and the presence of one global minimum. As an initial approach to solving the approximation problem, the values of the polynomial coefficients are calculated by solving the interpolation problem. After this, the approximation task is solved iteratively. All necessary polynomial coefficients are calculated multiple times, taking into account the values of the function and its derivative at the reference points. The final values of polynomial coefficients of high-order root-polynomial functions are calculated using the dichotomy method. The article also provides examples of the applying fourth-order and fifth-order root-polynomial functions to approximate sets of numerical data that correspond to the description of ravine functions. The obtained theoretical results are interesting and important for the experts who study the physics of electron beams and design modern industrial electron beam technological equipment.
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Blockchain transaction analysis: a comprehensive review of applications, tasks and methods
(КПІ ім. Ігоря Сікорського, 2023) Dorogyy, Yaroslaw Yu.; Kolisnichenko, Vadym Yu.
Blockchain transaction analysis is a powerful tool to gain insights into the actions and conduct of participants within blockchain networks. This article aims to extensively examine the applications, tasks, and methods associated with blockchain transaction analysis. We look at various uses of transaction analysis, ranging from its instrumental role in blockchain development to its pivotal significance in the field of criminal investigations. By leveraging common techniques and technologies employed in conducting such an analysis, we unlock hidden insights and uncover information that is not visible at first look. This article offers a wide-ranging perspective on the profound significance of blockchain transaction analysis while shedding light on its key role within the cryptocurrency industry and its wide-ranging applications beyond.
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Decision-tree and ensemble-based mortality risk models for hospitalized patients with СOVID-19
(КПІ ім. Ігоря Сікорського, 2023) Vyklyuk, Ya.; Levytska, S.; Nevinskyi, D.; Hazdiuk, K.; Škoda, M.; Andrushko, S.
The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of the main indicators that lead to the patients’ mortality. Using the good-known parameters that are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible and understandable decision tree and ML ensemble of classifiers models that would allow the physician to determine the prognosis in a few minutes and, accordingly, to understand the need for treatment adjustment, transfer of the patient to the emergency department. The accuracy of the resulting ensemble of models fitted on actual hospital patient data was in the range of 0.88–0.91 for different metrics. Creating a data collection system with further training of classifiers will dynamically increase the forecast’s accuracy and automate the doctor’s decision-making process/
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Digital twins: stages of concept development, areas of use, prospects
(КПІ ім. Ігоря Сікорського, 2023) Pankratova, N.D.; Grishyn, K.D.; Barilko, V.E.
Abstract. The results of a review of the digital twin concept development, the areas of their use, and the prospects are highlighted. The history of the emergence and de velopment of the digital twin concept, its definition, and its classification are given. The relevance of the technology under consideration is reflected. The purpose of this review is to provide the most complete, up-to-date information on the current state of the digital twin technology, its application in various fields of human activity, and further prospects for the development of the industry. An extensive bibliography on the topic is provided, which may be helpful for researchers and representatives of various industries.
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Estimation of the parameters of generalized linear models in the analysis of actuarial risks
(КПІ ім. Ігоря Сікорського, 2023) Panibratov, R. S.; Bidyuk, P. I.
Abstract. Methods of estimating the parameters of generalized linear models for the case of paying insurance premiums to clients are considered. The iterative-recursive weighted least squares method, the Adam optimization algorithm, and the Monte Carlo method for Markov chains were implemented. Insurance indicators and the target variable were randomly generated due to the problem of public access to insurance data. For the latter, the normal and exponential law of distribution and the Pareto distribution with the corresponding link functions were used. Based on the quality metrics of model learning, conclusions were made regarding their construction quality.
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Generalization of the thermodynamic approach to multi-dimensional quasistatic processes
(КПІ ім. Ігоря Сікорського, 2023) Kutsenko, A.S.; Kovalenko, S.V.; Kovalenko, S.M.
A method of mathematical modeling of multidimensional quasi-static processes, a generalization of quasi-static processes of equilibrium thermodynamics, is proposed and substantiated. The authors obtain a generalization of the first and the second law of thermodynamics in the form of Carathéodory to multidimensional quasi-static processes. The idea of generalization is to construct an orthogonal system of functionals similar to the work and heat functionals of classical thermodynamics along families of phase trajectories corresponding to different types of influences on a multidimensional quasi-static system. The representation of quasi-static processes by systems of ordinary differential equations containing control variables is substantiated. The obtained results make it possible to use a wide arsenal of methods of the theory of control of dynamical systems to solve problems of control of quasi-static processes.
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Guaranteed root-mean-square estimates of the forecast of matrix observations under conditions of statistical uncertainty
(КПІ ім. Ігоря Сікорського, 2023) Nakonechnyi, O.G.; Kudin, G.I.; Zinko, P.M.; Zinko, T.P.
Abstract. We investigate the problem of linear estimation of unknown mathematical expectations based on observations of realizations of random matrix sequences. Constructive mathematical methods have been developed for finding linear guaranteed RMS estimates of unknown non-stationary parameters of average values based on observations of realizations of random matrix sequences. It is shown that such guaranteed estimates are obtained either as solutions to boundary value problems for systems of linear differential equations or as solutions to the corresponding Cauchy problems. We establish the form and look for errors for the guaranteed RMS quasiminimax estimates of the special forecast vector and parameters of unknown average values. In the presence of small perturbations of known matrices in the model of matrix observations, quasi-minimax RMS estimates are found, and their guaranteed RMS errors are obtained in the first approximation of the small parameter method. Two test examples for calculating the guaranteed root mean square estimates and their errors are given.
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Identification of lung disease types using convolutional neural network and VGG-16 architecture
(КПІ ім. Ігоря Сікорського, 2023) Bukhori, S.; Verdy, B. Y. N.; Eka, Y. R. Windi; Januar, A. P.
Abstract. Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%.
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Information system for assessing the informativeness of an epidemic process features
(КПІ ім. Ігоря Сікорського, 2023) Bazilevych, Kseniia O.; Kyrylenko, Olena Yu.; Parfenyuk, Yurii L.; Yakovlev, Sergiy V.; Krivtsov, Serhii O.; Meniailov, Ievgen S.; Kuznietcova, Victoriya O.; Chumachenko, Dmytro I.
The primary objective of this study is to assess the informativeness of various parameters influencing epidemic processes utilizing the Shannon and Kullback–Leibler methods. These methods were selected based on their foundation in the principles of information theory and their extensive application in machine learning, statistics, and other relevant domains. A comparative analysis was performed between the results acquired from both methods, and an information system was designed to facilitate the uploading of data samples and the calculation of factor informativeness impacting the epidemic processes. The findings revealed that certain features, such as “Chronic lung disease,” “Chronic kidney disease,” and “Weakened immunity,” did not carry significant information for further analysis and hindered the forecasting process, as per the data set examined. The developed information system efficiently supports the assessment of feature informativeness, thereby aiding in the comprehensive analysis of epidemic processes and enabling the visualization of the results. This study contributes to the current body of knowledge by providing specific examples of applying the described algorithmic models, comparing various methods and their outcomes, and developing a supportive tool for analyzing epidemic processes.
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Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: research and implementation
(КПІ ім. Ігоря Сікорського, 2023) Kulik, A.S.; Chukhray, A.G.; Havrylenko, O.V.
Abstract. Information technologies, particularly artificial intelligence methods, involve more and more deeply into all spheres of human activity: science, technology, art, and education. Ukraine also has sufficient potential and needs to develop educational support, which is the subject of this paper. The work aims to demonstrate the components of information technology for the creation of Intelligent Tutoring Systems (ITS), which are involved in studying various engineering disciplines. The work includes methods of system analysis, mathematical and simulation modeling, technical diagnostics, and artificial intelligence. The proposed models and methods are implemented in ITS prototypes for teaching mathematics, programming, and the automatic control theory. The Intelligent Tutoring Systems were implemented in the educational process of KhAI University and other institutions in Ukraine, Great Britain, Austria, and China. Experimental studies have shown increased student learning success rates using ITS compared to traditional methods. Improved and adapted for computer training methods of technical diagnostics, Bayesian networks, and developed models of algorithmic tasks, the learning process and the learner are valuable from a scientific point of view. In a practical sense, the obtained results can be used to create new specialized ITSs and build an expandable common learning platform integrating the basic disciplines of a specific educational field.

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