Python Programming Language Research Articles

Онтологічний підхід в системі забезпечення безпеки використання IP-телефонії

In today's information world, the use of VoIP has become an attractive option for user communication. With the downward trend in paying for basic broadband services and the rapid increase in internet speeds, the use of VoIP should only continue to grow in popularity. However, as the use of VoIP increases, so do the potential threats to ordinary users. This article examines the peculiarities of organizing corporate VoIP telephony systems, highlights the main problems in information protection systems in VoIP telephony, and outlines ways to solve them. The development of methods for speech analysis and corresponding processing of natural language, which allows for creating more accurate and effective systems for detecting anomalous traffic and potentially dangerous communications, is especially relevant. With the continuous development of artificial intelligence technologies, the direction of using intelligent means for content analysis in the VoIP system is becoming interesting. A method for detecting anomalies in IP-telephony traffic, based on grouping VoIP messages through context-frequency analysis, is proposed. Additionally, a method for automated filling of the ontology of thematic messages in corporate IP-telephony systems is proposed, based on the formalized presentation of messages using tree-like structures and the description of interaction operations through tuple algebra. Furthermore, a software implementation for converting voice messages into text representations using the SpeechRecognition library for voice-to-text conversion in the Python programming language was created. Experimental studies of the proposed approaches were conducted, and a software subsystem for detecting anomalous messages based on the ontological approach was implemented in the current corporate IP-telephony system.

Comparative Analysis of Sorting Algorithms: TimSort Python and Classical Sorting Methods

The sorted() function within the Python programming language has emerged as the primary choice among developers for sorting operations. Consequently, this study offers a comparative analysis of various classical sorting algorithms and Python's built-in sorting mechanisms, with the objective of identifying the most time-efficient sorting algorithm. The analysis involves assessing the time complexity of each algorithm while handling data arrays ranging from 10 to 1,000,000 elements using Python. These arrays are populated with randomly generated numeric values falling within the range of 1 to 1000. The benchmark algorithms utilized encompass Heap Sort, Shell Sort, Quick Sort, and Merge Sort. A looping mechanism is applied to each algorithm, and their execution speeds are gauged utilizing the Python 'time.perf_counter()' library. The findings of this study collectively indicate that Python's standard algorithm, surpasses classic sorting algorithms, including Heapsort, Shellsort, Quicksort, and Mergesort, in terms of execution.

Klasifikasi Sel Tumor Payudara Menggunakan Algoritma Support Vector Machine (SVM)

Cancer is a large group of diseases that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably, beyond their normal limits to invade adjacent parts of the body and/or spread to other organs. There is a lot of information about breast cancer that can be accessed easily. Information about breast cancer can be processed with machine learning. Machine learning can discover new meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technology and statistical and mathematical techniques. The purpose of this research is to determine the value of the accuracy of the SVM model on training data and testing data; and to determine the precision value of the SVM model on training data and testing data. Wisconsin Breast Cancer (WBC) data available in the UCI Machine Learning Repository. The data have been processed using the Python programming language with a support vector machine (SVM) modeling algorithm. The results of this research indicate that the value of accuracy in training data was equal to , the value of accuracy in testing data was equal to , and the value of precision in the SVM model algorithm was obtained as large as for training data and as large as for data testing.

RESEARCH ON SOFTWARE FOR ERROR PROBABILITY PREDICTION IN BUSINESS PROCESS MODELS USING LOGISTIC REGRESSION

Business process modeling allows to graphically represent organizational activities and related events. It allows to identify areas for improving the organizational performance, define requirements for software solutions, and, in general, to facilitate communication between IT and business parties within or between different organizations. Therefore, at the stage of representing the activity in the form of a model, it is necessary to understand how likely it is that errors will occur during the implementation of the depicted business process. Thus, this study aims to improve the quality of business process models by solving the problem of predicting the error probability of business process execution. In order to assign error probabilities to each business process model from the training dataset, it is proposed to use one of the complexity metrics – the coefficient of network connectivity. To predict the error probability in business process execution, it is proposed to use the simplest and most intuitive machine learning model – logistic regression. As independent variables, it is proposed to choose the basic metrics of business process modeling – the number of nodes and arcs. Thus, the algorithm for solving the task includes steps related to calculating probabilities for the training data set, preparing training and test sets, determining regression parameters, visualization, and evaluation of training results. For the software that implements the proposed approach, a client-server architecture was chosen due to its flexibility and scalability. When developing software components, the Scikit-Learn machine learning library and the Python programming language were used to build a logistic regression mathematical model. The software tool is implemented as a web application based on MySQL, the Node JS platform, and the Express JS web framework. The quality assessment results of the developed prediction model indicate the suitability of the software tool for solving the problem of predicting the error probability of business process execution.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

О РАЗРАБОТКЕ ЭКСПЕРТНЫХ СИСТЕМ

The development of expert systems using examples of a master's thesis and laboratory work is considered. Expert systems are created in the C # and Python programming languages

Дифференциальная мера эмиссии солнечных нановспышек, определенная с помощью алгоритма SITES

The paper presents the results of a study of capabilities of the SITES algorithm for reconstructing the differential emission measure (DEM) of a source from its radiation in several parts of the electromagnetic spectrum in the context of observing solar nanoflares with the AIA/SDO instrument. The SITES method was implemented in the Python programming language and was first used to construct the DEM of nanoflares. For this purpose, we tested the efficiency of the algorithm on model single- and double-peak DEM at characteristic temperatures of solar nanoflares. The test results indicate that the SITES algorithm can be of limited applicability for studying the DEM of nanoflares in the single-peak approximation. The algorithm has a combination of high accuracy and high counting rate in the studied temperature range from 1 to 3 MK. The features of DEM nanoflares reconstructed by the SITES method were examined using our previously found sample of 58855 events observed in 2019 with the AIA/SDO instrument. The results confirm that the characteristic plasma temperature in nanoflares is 1–2 MK. The reconstructed DEM of nanoflares generally have one maximum within this range, but the temperature distribution we obtained for all flares forms two clusters with maxima at 1.2 and 1.7 MK. We interpret this as possible evidence for the existence of two types of solar nanoflares, but this result requires further confirmation.

Differential emission measure of solar nanoflares measured with the SITES algorithm

The paper presents the results of a study of capabilities of the SITES algorithm for reconstructing the differential emission measure (DEM) of a source from its radiation in several parts of the electromagnetic spectrum in the context of observing solar nanoflares with the AIA/SDO instrument. The SITES method was implemented in the Python programming language and was first used to construct the DEM of nanoflares. For this purpose, we tested the efficiency of the algorithm on model single- and double-peak DEM at characteristic temperatures of solar nanoflares. The test results indicate that the SITES algorithm can be of limited applicability for studying the DEM of nanoflares in the single-peak approximation. The algorithm has a combination of high accuracy and high counting rate in the studied temperature range from 1 to 3 MK. The features of DEM nanoflares reconstructed by the SITES method were examined using our previously found sample of 58855 events observed in 2019 with the AIA/SDO instrument. The results confirm that the characteristic plasma temperature in nanoflares is 1–2 MK. The reconstructed DEM of nanoflares generally have one maximum within this range, but the temperature distribution we obtained for all flares forms two clusters with maxima at 1.2 and 1.7 MK. We interpret this as possible evidence for the existence of two types of solar nanoflares, but this result requires further confirmation.

РАЗРАБОТКА ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ДЛЯ АВТОМАТИЗАЦИИ РАСЧЕТА РАДИАТОРОВ ОХЛАЖДЕНИЯ РЭА

The development and implementation of software that automates the calculation of cooling radiators for electronic equipment in the Python programming language using the PyQt library for creating windows and widgets is considered. In the developed software, two approaches to calculating radiators were implemented: through determining the required area and through determining the required specific thermal resistance

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ПОИСКА РЕШЕНИЙ ДИФФЕРЕНЦИАЛЬНЫХ УРАВНЕНИЙ

An approach to modeling chemical technological processes for calculating solutions of differential methods using artificial neural networks is considered. The capabilities of the Keras module of the Python programming language within these problems are described

Application of virtual reality technologies in the rehabilitation of patients with left and right hemisphere lesions in the acute period of ischemic stroke

BACKGROUND: Currently, neurocognitive rehabilitation with the integration of innovative virtual reality (VR) technologies is the standard component of early rehabilitation treatment programs for patients with acute focal brain damage. AIM: Based on the statistical methodology, this study aimed to evaluate the efficiency of neurocognitive rehabilitation with the application VR technologies in patients with left and right hemisphere lesions in the acute period of ischemic stroke. MATERIALS AND METHODS: The study examined 160 patients diagnosed with ischemic stroke exhibiting cognitive impairments. Patients were divided into two groups based on stroke localization: group 1 (n=80) had left hemisphere lesions, whereas group 2 (n=80) had left hemisphere lesions. Since day 5 of hospitalization, in addition to basic therapy, rehabilitation was performed using a hardware–software multimedia complex based on VR for remote-controlled rehabilitation «Devirta-Delphi». Rehabilitation efficiency (RE) was assessed by testing objectively quantitative measures of accuracy and coordination of movement performance before and after treatment using a Kinect Delphi system. Cognitive and psychoemotional measures were assessed by neuropsychological tests on days 2–4 of hospitalization and at the end of the VR therapy. Mathematical statistics were calculated using Python programming language and its libraries Pandas and SciPy. RESULTS: Higher RE values were obtained in patients with right-sided stroke. Irrespective of lesion lateralization, significant improvement was noted with respect to mnestic, executive function, and ideomotor praxis, regression of anxiety, depression, and general aggression. Group 1 also showed an improvement in speech function and a decrease in general asthenia, whereas group 2 showed an increase in attention and emotion recognition ability. Factors that negatively affected RE were identified, namely, apathy, anxiety, prestroke cognitive decline, semantic aphasia, and decreased attention in group 1 and semantic and visual–spatial impairments, prestroke decline, and recurrent stroke in group 2. CONCLUSION: The application of an immersive environment in the early rehabilitation of patients with cognitive impairment improves the performance of verbal and visual tests reflecting mnestic, regulatory, and neurodynamic executive functions in lesions with different lateralization.

Research on image recognition of tomato leaf diseases based on improved AlexNet model

Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Using an online platform in a didactic learning model that stimulates critical thinking skills of future informatics teachers

Critical thinking is a key competency for future computer science teachers, who must be prepared not only for the rapid technological and informational development of the economy and society but also for the actively evolving process of digitalization in education. The aim of the research presented in the article is to develop methods and tools for enhancing critical thinking skills among future informatics teachers. To this end, the author has created an online platform that provides students with access to a variety of educational resources, including lectures, laboratory work, and interactive tasks. By integrating constructivist, cognitive, and sociocultural approaches to learning, this model promotes deep understanding and analysis of information by students. The platform is physically hosted on the server of Termez State University, built using the Python programming language with the Django framework, and is already being used by second-year students majoring in “Mathematics and Informatics”, studying under the discipline “Informatics and Digital Technologies”.The online platform offers the necessary functionality for organizing independent study sessions and self-monitoring, allowing students to track their progress and develop the ability to critically evaluate information. The educational platform is a key component of the didactic model for developing critical thinking skills in future informatics teachers, the architecture of which is detailed in the article.The results of the study presented in the article can be utilized in the educational practices of higher educational institutions (and even schools) seeking to update and optimize their training processes in the context of digitalization.

Determination of the In-Plane Behavior of Masonry Walls Using the Truss Model

ABSTRACT The diverse characteristics of masonry structures make it difficult to determine their behavior. The development of numerical methodologies using various approaches and assumptions reflects the need for adaptable methods to accommodate these diverse structures. This study focuses on a novel simplified approach for analyzing masonry walls. The proposed method uses truss elements with a well-known theoretical background to elucidate the load-bearing capacity, damage formation and its causative factors, and failure mechanics of masonry piers within a plane. Five masonry wall samples from three different studies were used to validate the accuracy of the proposed approach. Pushover analyses were performed using software with nonlinear analysis capabilities, which was developed in the Python programming language. Consequently, the obtained results were compared with the literature data. This method provides initial expectations such as simplification and consistency, making it a potential alternative for the preliminary assessment of masonry structures.

Automated Extraction of Structured Data from the Social Network Instagram

The paper explores the extraction of structured information from the social network Instagram through a suitable application programming interface, namely the unofficial Instagram Private API. It focuses on creating a computer program that identifies which posts a user has tagged as "Likes" and then stores this information for profiling specific user profiles. The introduction of the paper highlights the general use of social media in modern society and the importance of personal data for these platforms. It specifies the aim of the study, which is to extract information from Instagram and then analyse it for user profiling. It then describes the evolution of the social network Instagram and key features such as different types of posts. This paper further focuses on the solution and implementation by using Python programming language to minimize the load on Instagram servers and reduce the risk of detection of automated processes. It describes the process of setting up new Instagram accounts, the obstacles in obtaining login credentials, and the need to simulate human behaviour to bypass the network's defence mechanisms. It then focuses on the actual retrieval of information such as the users followed, their posts and information about which posts the user has marked as favourites. It mentions that extracting data from closed profiles is difficult and elaborates on the technical challenges associated with this task. A significant part of this paper is a discussion of Instagram's defence mechanisms that respond to automated computer programs. It describes access denial, account blocking, and identity verification prompts such as CAPTCHA tests. Finally, the conclusion summarizes the results obtained, which indicate the acquisition of approximately 90,000 records for user profiling. It discusses the shortcomings of a fully automated solution due to Instagram's account creation conditions and defence mechanisms. It mentions the need for further research and highlights key gaps and challenges in this area. Overall, the study highlights the technical and security challenges in extracting information from Instagram and emphasises the need for further research and improvements in the technical procedures for extracting data from the platform.

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

Normalised similarity assessment to inform grouping of advanced multi-component nanomaterials by means of an Asymmetric Sigmoid function

This manuscript presents a procedure for similarity assessment as a basis for grouping of multi component nanomaterials (MCNMs). This methodology is an adaptation of the approach by Zabeo et al. (2022), which includes an impactful change: the calculated similarities are normalised in the [0,1] domain by means of asymmetric Logistic scaling to simplify comparisons among properties' distances. This novel approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. It can be applied to assess groups of MCNMs as well as mixed groups of multi and single component nanomaterials as well as chemicals. To facilitate the application of the proposed methodology, a software script was developed by using the Python programming language, which is currently undergoing migration to a user-friendly web-based tool. The presented approach was tested against a real industrial case study provided by the Andalusian Innovation Centre for Sustainable Solution (CIAC): SiO2-ZnO hybrid nanocomposite used in building coatings, which is designed to facilitate photocatalytic removal of NOx gases from the atmosphere. The results of applying the methodology in the case study demonstrated that ZnO is dissimilar from the other candidates mainly due to its different dissolution profiles.

Calculation of heat protective parameters of clothing packages by method simulation modeling (Monte Carlo method)

The heat-protective properties of clothing have been studied taking into account the influence of deterministic factors, and the influence of random factors on the heat-protective properties of clothing has not been studied. In this regard, to obtain objective values of indicators of the heat-protective properties of clothing, taking into account the nature of the distribution of factors for a batch of products that are random. Knowing the laws of distribution of random factors effectively, it is possible to carry out calculations of the thermal insulation properties of clothing material packages using the simulation method. The work has developed a method for calculating the thermophysical parameters of clothing packages based on simulations of random parameters distributed according to a normal law. For the calculation, an algorithm and calculation program were compiled in the Python programming language. The thermophysical parameters of the clothing package were calculated, and the influence of wind flow speed on them was analyzed. When calculating the wind flow speed, which have a significant impact on the values of the effective thermal conductivity coefficient of the packages and the values of the total coefficient of the packages, they are taken taking into account the Beaufort table and the recommendations of the Ministry of Emergency Situations of Kazakhstan. The developed program for calculating the thermophysical characteristics of a package of clothing materials allows to change the model parameters during the calculation process: introduce new layers of materials into the package, change the thermophysical and linear characteristics of individual layers of the package, change external conditions.