Sistemni Doslidzena ta Informacijni Tehnologii最新文献

Identification of lung disease types using convolutional neural network and VGG-16 architecture 基于卷积神经网络和VGG-16架构的肺部疾病类型识别

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.07

Saiful Bukhori, Bangkit Yudho Negoro Verdy, Yulia Retnani Windi Eka, Adi Putra Januar

引用次数: 0

Basic algorithm for approximation of the boundary trajectory of short-focus electron beam using the root-polynomial functions of the fourth and fifth order 用四阶和五阶根多项式函数逼近短焦电子束边界轨迹的基本算法

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.10

Igor Melnyk, Alina Pochynok

{"title":"Basic algorithm for approximation of the boundary trajectory of short-focus electron beam using the root-polynomial functions of the fourth and fifth order","authors":"Igor Melnyk, Alina Pochynok","doi":"10.20535/srit.2308-8893.2023.3.10","DOIUrl":"https://doi.org/10.20535/srit.2308-8893.2023.3.10","url":null,"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 root-polynomial 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.","PeriodicalId":30502,"journal":{"name":"Sistemni Doslidzena ta Informacijni Tehnologii","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A concatenation approach-based disease prediction model for sustainable health care system 基于串联方法的可持续卫生保健系统疾病预测模型

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.06

Kamaraj Tharageswari, Natarajan Mohana Sundaram, Rajendran Santhosh

{"title":"A concatenation approach-based disease prediction model for sustainable health care system","authors":"Kamaraj Tharageswari, Natarajan Mohana Sundaram, Rajendran Santhosh","doi":"10.20535/srit.2308-8893.2023.3.06","DOIUrl":"https://doi.org/10.20535/srit.2308-8893.2023.3.06","url":null,"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).","PeriodicalId":30502,"journal":{"name":"Sistemni Doslidzena ta Informacijni Tehnologii","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainties in data processing, forecasting and decision making 数据处理、预测和决策中的不确定性

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.05

Liudmyla Levenchuk, Oxana Tymoshchuk, Vira Huskova, Petro Bidyuk

引用次数: 0

Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography 基于高效轻量级椭圆曲线密码的共享动态数据增强安全方案

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.02

Dipa Dharmadhikari, Sharvari Chandrashekhar Tamane

{"title":"Augmented security scheme for shared dynamic data with efficient lightweight elliptic curve cryptography","authors":"Dipa Dharmadhikari, Sharvari Chandrashekhar Tamane","doi":"10.20535/srit.2308-8893.2023.3.02","DOIUrl":"https://doi.org/10.20535/srit.2308-8893.2023.3.02","url":null,"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.","PeriodicalId":30502,"journal":{"name":"Sistemni Doslidzena ta Informacijni Tehnologii","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithm for simulating melting polar ice, Earth internal movement and volcano eruption with 3-dimensional inertia tensor 三维惯性张量模拟极地冰融化、地球内部运动和火山喷发的算法

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.09

Yoshio Matsuki, Petro Bidyuk

引用次数: 0

Academician Glushkov’s legacy: Human capital in the field of cybernetics, computing, and informatics at Igor Sikorsky Kyiv Polytechnic Institute 格卢什科夫院士的遗产:伊戈尔·西科尔斯基基辅理工学院控制论、计算和信息学领域的人力资本

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.01

Michael Zgurovsky

引用次数: 0

Research on hybrid transformer-based autoencoders for user biometric verification 基于混合变压器的用户生物特征验证自编码器研究

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.03

Mariia Havrylovych, Valeriy Danylov

{"title":"Research on hybrid transformer-based autoencoders for user biometric verification","authors":"Mariia Havrylovych, Valeriy Danylov","doi":"10.20535/srit.2308-8893.2023.3.03","DOIUrl":"https://doi.org/10.20535/srit.2308-8893.2023.3.03","url":null,"abstract":"Our current study extends previous work on motion-based biometric verification using sensory data by exploring new architectures and more complex input from various sensors. Biometric verification offers advantages like uniqueness and protection against fraud. The state-of-the-art transformer architecture in AI is known for its attention block and applications in various fields, including NLP and CV. We investigated its potential value for applications involving sensory data. The research proposes a hybrid architecture, integrating transformer attention blocks with different autoencoders, to evaluate its efficacy for biometric verification and user authentication. Various configurations were compared, including LSTM autoencoder, transformer autoencoder, LSTM VAE, and transformer VAE. Results showed that combining transformer blocks with an undercomplete deterministic autoencoder yields the best performance, but model performance is significantly influenced by data preprocessing and configuration parameters. The application of transformers for biometric verification and sensory data appears promising, performing on par with or surpassing LSTM-based models but with lower inference and training time.","PeriodicalId":30502,"journal":{"name":"Sistemni Doslidzena ta Informacijni Tehnologii","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135343375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent information system of the city's socio-economic infrastructure 智能信息系统是城市的社会经济基础设施

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.08

Khrystyna Lipianina-Honcharenko, Yevgeniy Bodyanskiy, Anatoliy Sachenko

引用次数: 0

Investigation of computational intelligence methods in forecasting at financial markets 计算智能方法在金融市场预测中的研究

Sistemni Doslidzena ta Informacijni Tehnologii Pub Date : 2023-09-29 DOI: 10.20535/srit.2308-8893.2023.3.04

Yuriy Zaychenko, Helen Zaichenko, Oleksii Kuzmenko

引用次数: 0