Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал
Постійне посилання на фонд
ISSN 2308-8893 (Online), ISSN 1681-6048 (Print)
Періодичність: 4 рази на рік
Рік заснування: 2001
Тематика: теоретичні та прикладні проблеми і методи системного аналізу; теоретичні та прикладні проблеми інформатики; автоматизовані системи управління; прогресивні інформаційні технології; високопродуктивні комп'ютерні системи; проблеми прийняття рішень і управління в економічних, технічних, екологічних і соціальних системах; теоретичні та прикладні проблеми інтелектуальних систем підтримки прийняття рішень; проблемно і функціонально орієнтовані комп'ютерні системи та мережі; методи оптимізації, оптимальне управління і теорія ігор; математичні методи, моделі, проблеми і технології дослідження складних систем; методи аналізу та управління системами в умовах ризику і невизначеності; евристичні методи та алгоритми в системному аналізі та управлінні; нові методи в системному аналізі, інформатиці та теорії прийняття рішень; науково-методичні проблеми в освіті.
Офіційний сайт: http://journal.iasa.kpi.ua/
Рік заснування: 2001
Тематика: теоретичні та прикладні проблеми і методи системного аналізу; теоретичні та прикладні проблеми інформатики; автоматизовані системи управління; прогресивні інформаційні технології; високопродуктивні комп'ютерні системи; проблеми прийняття рішень і управління в економічних, технічних, екологічних і соціальних системах; теоретичні та прикладні проблеми інтелектуальних систем підтримки прийняття рішень; проблемно і функціонально орієнтовані комп'ютерні системи та мережі; методи оптимізації, оптимальне управління і теорія ігор; математичні методи, моделі, проблеми і технології дослідження складних систем; методи аналізу та управління системами в умовах ризику і невизначеності; евристичні методи та алгоритми в системному аналізі та управлінні; нові методи в системному аналізі, інформатиці та теорії прийняття рішень; науково-методичні проблеми в освіті.
Офіційний сайт: http://journal.iasa.kpi.ua/
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Перегляд Системні дослідження та інформаційні технології: міжнародний науково-технічний журнал за Назва
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Документ Відкритий доступ 1D CNN model for ECG diagnosis based on several classifiers(КПІ ім. Ігоря Сікорського, 2022) Mahmoud M. Bassiouni; Islam Hegazy; Nouhad Rizk; El-Sayed A. El-Dahshan; Abdelbadeeh M. SalemOne of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.Документ Відкритий доступ 3D frame models switching elements by berezovsky for software-configurable switching structures(КПІ ім. Ігоря Сікорського, 2018) Berezovsky, S. A.The frame 2D and 3D models of patented by Berezovsky switching elements are proposed in relation to the construction of topologies of switching structures admissible for reconfiguration. It has been revealed that the use of frame models by Berezovsky switching elements allows to visualize the information about the state of the structure of switching elements, to vary the number of independent inputs and outputs, and provides additional possibilities in the simulation of topologies of modern structures with separated by planes data and control. The method of formation of states of the switching structure topology elements has been proposed.Документ Відкритий доступ 3D-model reconstruction with use of monocular rgb camera(КПІ ім. Ігоря Сікорського, 2017) Vedmedenko, O. V.; Nikolaiev, S. S.; Tymoshenko, Y. A.Every year the edge between the real and digital worlds is becoming more and more blurred. Augmented and virtual reality rapid development creates new opportunities for more productive work and entertainment, revolution in 3D printing technologies begets boost in multiple DIY communities appearance and sharing economy growth. All these factors require new technologies that allow making 3D models from real world objects, but most of these solutions are either very expensive or require complex technical knowledge that most ordinary people do not have. This paper provides a review and comparison of modern methods for 3D models of physical objects real time reconstruction that can be used in present-day mobile solutions.Документ Відкритий доступ A comprehensive survey on load balancing techniques for virtual machines(КПІ ім. Ігоря Сікорського, 2023) Suman Sansanwal; Nitin JainCloud 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.Документ Відкритий доступ 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).Документ Відкритий доступ 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.Документ Відкритий доступ A literature review of abstractive summarization methods(КПІ ім. Ігоря Сікорського, 2019) Shypik, D. V.; Bidyuk, P. I.Документ Відкритий доступ A middle- time recognition of epileptic seizures from geometrical patterns of eeg data(КПІ ім. Ігоря Сікорського, 2002) Makarenko, A.; Oleksandruk, B.; Schindler, K.; Donatti, F.; Villa, A.; Tetko, I.An approach for middle- time recognition of epileptic seizures from EEG data is proposed. The method considers sharp changes in the recorded data using geometrical patterns of the signal in phase-space. The approach was developed using experimental clinical EEG data recorded from ten patients and reliably predicted epileptic seizures in the ten-minute interval before the seizure onsets. An estimation of sensitivity and specificity of the proposed method is also provided.Документ Відкритий доступ A multi-level decision-making framework for heart-related disease prediction and recommendation(КПІ ім. Ігоря Сікорського, 2023) Vedna Sharma; Surender Singh SamantThe 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.Документ Невідомий A multi-objective mixed integer programming model for multi echelon supply chain network design and optimization(Політехніка, 2010) Paksoy, T.; Özceylan, E.; Weber, G.-W.; Паксой, Туран; Оцейлан, Ерен; Вебер, Герхард-Вільгельм; Паксой, Т.; Оцейлан, Е.; Вебер, Г.-В.Документ Невідомий A novel approach to remote sensing of vegetation(КПІ ім. Ігоря Сікорського, 2005) Bidyuk, P. I.; Litvinenko, V. I.; Ponomarenko, S. O.The problem of remote estimation of chlorophyll content in vegetation is considered. Some reflectance spectra have been recorded for winter wheat leaves with various level of chlorophyll concentration. To reduce the level of noise in the measurement data produced by measuring system and possible influence of soil surface smoothing procedure proposed by Savitzky and Golay was applied. The 1st derivative of reflectance spectra curves had been computed and analyzed with respect to correlation with pigment content. To compute an estimate of chlorophyll content multiple regression as well as neural net approach have been applied and both proved to be successful.Документ Відкритий доступ A parallel search algorithm for formal grammar data types(КПІ ім. Ігоря Сікорського, 2018) Prodan, AnastasiiaIn this paper, we developed a concurrent generic heuristic algorithm for parallel parsing and searching in structured text datasets. The main objective of the algorithm was to increase an efficiency of central processing unit dependent operations when parsing large-scale datasets by using a parallel approach. The developed algorithm uses heuristics to find requested data without needing to process the whole file and without syntax tree building. It can be applied to any data formats. An increase in efficiency was discovered when input-output operations take significantly less time than the process of searching, the file is loaded into random access memory or when an efficient non-sequential access to file is possible. We also developed a prototype implementation of the algorithm for use in performance comparisons. The prototype supports searching in large-scale XML datasets using a subset of XPath expressions to specify search request. Our experimental results show that the developed algorithm is faster than classical algorithms, when all the requirements are met and the desired data is located closer to the beginning of the dataset. In worst cases, our algorithm gives nearly the same results as the others, but consumes more memory.Документ Відкритий доступ Academic segment of Ukrainian Grid infrastructure(Політехніка, 2009) Martynov, E.; Svistunov, S.; Zinovjev, G.; Мартинов, Євген Сергійович; Свістунов, Сергій Якович; Зінов'єв, Геннадій Михайлович; Мартынов, Е.; Свистунов, С.; Зиновьев, Г.Документ Відкритий доступ 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.Документ Відкритий доступ Adaptation of oscillatory systems in networks – a learning signal approach(Політехніка, 2014) Rodriguez, Julio; Родрігеc, Хуліо; Родригес, ХулиоДокумент Відкритий доступ Adaptive forecasting and financial risk estimation(КПІ ім. Ігоря Сікорського, 2020) Danilov, V.; Gozhyj, O.; Kalinina, I.; Belas, A.; Bidyuk, P.; Jirov, O.Документ Відкритий доступ Adaptive hybrid activation function for deep neural networks(КПІ ім. Ігоря Сікорського, 2022) Bodyanskiy, Yevgeniy V.; Kostiuk, Serhii O.Документ Відкритий доступ Aggressive and peaceful behavior in multiagent systems on cellular space(НТУУ "КПІ", 2016) Zavertanyy, Valentin Viktorovych; Makarenko, Alexander Sergijovych; Завертаний, Валентин Вікторович; Макаренко, Олександр Сергійович; Завертаный, Валентин Викторович; Макаренко, Александр СергеевичДокумент Відкритий доступ «Alert»-технології, що ґрунтуються на теорії дінамічних систем в економічних задачах(Політехніка, 2011) Лопатін, Олексій Константинович; Lopatin, A. K.; Лопатин, А. К.Документ Відкритий доступ Algorithm FLARS and recognition of time series anomalies(КПІ ім. Ігоря Сікорського, 2004) Gvishiani, A. D.; Agayan, S. M.; Bogoutdinov, Sh. R.; Tikhotsky, S. A.; Hinderer, J.; Bonnin, J.; Diament, M.As a rule, algorithms of recognition of time series anomalies are based on time frequency or statistical analysis . This article is devoted to detailed formal description of new fuzzy set based algorithm FLARS (Fuzzy Logic Algorithm for Recognition of Signals). It recognizes time series anomalies by means «smooth» modelling (in fuzzy mathematics sense) of interpreter’s logic, which searches for anomalies at the record.