Python Programming Language Research Articles

COMPARISON OF CLASSICAL AND QUANTUM COMPUTING FOR PARTICLE SWARM OPTIMIZATION

The article explored and delved into the advanced computational strategies of Particle Swarm Optimization (PSO) by contrasting classical and quantum computing paradigms. The advantages of quantum computing lie in its potential to solve computationally complex problems exponentially faster than classical computers. One of the advantages of Particle Swarm Optimization is its ability to find optimal solutions in complex search spaces. The research centers around the performance of PSO algorithms, as a part of the biological swarm optimization algorithms, when applied to a set of single-objective optimization functions, namely the Sphere, Rosenbrock, Booth, and Himmelblau functions. Utilizing a controlled setup of 100 particles, iterating 100 times across various dimensions tailored to each function, our study reveals that quantum Particle Swarm Optimization, implemented via Q# programming language and tested in Azure Quantum Workspace, consistently surpasses classical PSO in precision and convergence to global minima, despite the increased computational demands and error sensitivity inherent to quantum computations. The classical approach facilitated through Python programming language and leveraging deterministic pseudorandom number generators demonstrates robustness and lower computational costs but does not achieve the quantum's level of accuracy. The paper highlights the potential of quantum PSO to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces, paving the way for future applications where quantum advantages can be fully realized. The analysis goes further to discuss the implications of these findings for the future of optimization in various industries, including logistics, engineering, and finance, where optimization plays a critical role. The potential of quantum Particle Swarm Optimization to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces is particularly notable. It suggests that quantum computing could soon transform the landscape of computational optimization, providing solutions that are not only quicker but also more accurate.

Advancing the automated foraminifera fossil identification through scanning electron microscopy image classification: A convolutional neural network approach

Abstract Handling more than thousand fossil foraminifera data is very challenging, especially for old-way identification. Determining morpho-taxonomy by conventional microscopic observation is very time-consuming and can lead to innacuracy identification. We are certain that the process could be advanced through big data analysis using a machine learning approach. Foraminifera fossils have already become a common standard for biostratigraphic proxies and paleoenvironmental interpretation. Therefore, the objective of this study was to develop an automated identification method using Convolutional Neural Networks (CNN). We used standardized Scanning Electron Microscopy (SEM) images of foraminifera acquired from various open-source databases for image classification. The analysis was conducted using Python programming language in Google Colaboratory. The image dataset is categorized based on its genus (n: 138) and divided into training and test/validation data for accuracy simulation (total image: 1833; data training: 1387; test/validation: 237/237). The best-fit accuracy values of the training-data and testing-data were between 97-86%:73-77%, with parameters including epoch number ranging up to 40, learning rates of 0.05, and a batch size of 64. These values indicate good prospects for foraminifera SEM Image taxonomic classification, demonstrating a noteworthy level of identification accuracy (63%). The outcomes of this study offer a new method for further effective automated morpho-taxonomic identification of foraminifera fossils using other conventional optic microscopy.

Modeling damped spring vibration using python to train students' critical thinking and scientific reasoning

Programming has been carried out to model the vibration of a mass spring system with no damping and with damping variations using Python programming language coding. This research aims to simulate the simple harmonic vibration of a mass spring (without damping) and the vibration of a damped mass spring. The programming is designed after formulating the equations of motion of damped mass spring vibrations that behave as second-order differential equations and analyzing the numerical formulation of the Feynman-Newton algorithm arrangement. The method used is experimentation using python to simulate the vibration of the spring. Simulation by varying the damping constant (c = 0, c < √4mk, c = √4mk, and c > √4mk). The simulation results show various vibration graphs, namely simple harmonic vibration, under damped vibration, critically damped vibration, and over damped vibration. The shape of the vibration graph is influenced by the mass, spring constant, and damping constant. The greater the damping constant, the less the maximum speed of vibration. This research has succeeded in visualizing the simple harmonic vibration of a massless spring (without damping) and the vibration of a damped massless spring. This modeling can help the physics learning process in high school in understanding the concept of spring vibration to obtain critical reasoning in accordance with physical phenomena. Modeling of damped spring vibrations using Python can be used as a physics learning media on vibration material to train scientific and critical reasoning skills, and as a learning innovation because it is a new thing where the curriculum and high school learning in Indonesia are not used to being delivered.

Соціотопографія Вільнюса в XVII–XVIII ст.: основні особливості та напрямки розвитку

The article summarises the characteristics of the development of the Vilnius community in the Grand Duchy of Lithuania, regarding the context of historical events between the 17th and 18th centuries. The summary is part of a scientific project carried out between 2018 and 2021, based on a research of real estate registers and lists of taxes collected by the magistrate. The research used a statistical method, a spatial relational database and the structured query language (SQL) as well as Python programming language libraries NumPy and Pandas for managing and analysing big data. A detailed description of the study and its results can be found in the collective monograph published in 2021. Based on these sources, the value of real estate in different districts of Vilnius, spatial distribution, size and function of the buildings as well as the distribution of their owners in the city between first half of the 17th and the end of the 18th century were analysed. It was determined that frequent large fires were causing the biggest changes to the spatial structure of Vilnius. The greatest changes in demography and sociotopography were recorded after the occupation of Vilnius by the Moscow army between 1655 and 1661 and plague outbreaks in 1657 and between 1709 and 1710. During these times, the city has lost between 30 and 50 percent of its population. As a result, the mediaeval spatial structure of the old city gradually disappeared and its current appearance was formed with large, almost completely built-up plots of land, elite residences and dominant brick architecture of the Baroque style. The social structure has been partially changed. Poor townspeople and some artisans moved from the city centre to the suburbs. In the fortified centre, most real estate passed from the hands of ordinary citizens to wealthy individuals and organisations, who often acquired entire groups of empty land areas. However, some characteristics of the sociotopography of the 16th and first half of the 17th centuries remained unchanged, including the locations of some areas where social groups were living.

Hopsy - a methods marketplace for convex polytope sampling in Python.

Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.

ChatGPT’s crystal ring: simulating auditors’ use of machine learning in stock price prediction

ABSTRACT This study investigates the influence of technological factors on the intent to use Machine Learning (ML) tools such as Python for the purpose of predicting stock prices. Further, it investigates the moderate impact of Artificial Intelligence (AI) models usage, in particular ChatGPT, on these associations. The outcomes of a simulation involving 400 auditors, accounting for the heterogeneity of their competencies, were obtained through code utilisation based on the Python programming language. The technological factors drawn from diffusion of innovation theory (DOI), including relative advantages, Complexity, compatibility, observability, and triability, all showed positive associations with behavioural intent. The use of ChatGPT significantly fortified these connections. These results suggest a fruitful symbiotic outcome may be achieved by combining AI capabilities with these variables. The findings underscore the significance of planning for the adoption of AI in financial decision-making and auditing and also illustrate the potential of AI in these areas.

APPLICATION OF SMART CONTRACTS IN ELECTRONIC SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGIES

In the era of digitization, where information technology and business processes are closely intertwined, the development and implementation of blockchain-based smart contracts become key to achieving a new level of automation, security, and efficiency. This article deeply analyzed how blockchain smart contracts can enhance the execution of contractual obligations, making processes more transparent and efficient. The main aspect of study is the technical details of smart contracts and exploration of their practical application for optimizing business procedures, significantly reducing risks associated with fraud and the need for intermediaries. A practical demonstration of deploying a smart contract, executed in the Python programming language, is proposed as a method used in the article, highlighting the possibilities and challenges related to scalability and regulation. Results underscore a notable boost in operational efficiency and security, while also identifying barriers to broader technological adoption. Concluding, the significant role of smart contracts in evolving information systems is underlined, advocating for novel approaches to secure, autonomous contract fulfillment and emphasizing the importance of ongoing research to exploit their full capabilities in fortifying information security and operational efficacy.

APPLICATION OF ALGORITHMS FOR ANOMALY DETECTION IN HEALTH-ENABLED SENSOR-CLOUD INFRASTRUCTURE

Real-time patient monitoring and early disease diagnosis are two ways that the healthcare industry is benefiting from the integration of sensors and cloud technology. In order to detect changes in patient's health, a variety of non-invasive sensors are applied to the skin to monitor various physiological parameters. The collected data are then wirelessly communicated to the cloud data center. However, the transmitted data are susceptible to several sources of interference called anomalies. Anomalies is when a sudden change occurs from the expected sensor data generated. This may be as a result of sensor faults, measurement faults, injection and alteration by malicious attackers. Therefore, this research tends to conduct a survey on existing algorithms or techniques used for the detection of anomalies in health-enabled sensor-cloud infrastructure. The processes adopted by the algorithms were identified and discussed exhaustively. In addition, the simulation setup and programming languages adopted to implement and evaluate the existing algorithms, followed by the limitations of the algorithms, which may lead to future research directions are captured in this paper. The outcome of the research shows that machine learning algorithms were predominantly adopted for detecting anomalies with the support of clustering and classification processes. Furthermore, Visual Basic.Net simulation tool and Python programming language was mostly adopted for experimentation and evaluation of the existing techniques. Limitations such as overfitting, under-fitting, computation complexity (time and memory space), and missing data are hindering the optimal performance of existing algorithm, which needs to be addressed in future researches.

Adoption of Bloom Filter and Firebase Framework to Enhance Authentication Time for Healthcare Systems Based on Blockchain Technology

Healthcare offers several advantages for actual-time smart healthcare. security concerns are growing due to its constrained computing power, storage capacity, and self-defense capabilities. The tamper-resistant decentralized architecture of more recent blockchain-based authentication solutions gives them significant security features, but they come with a high resource cost because they need a lot of processing power, additional storage, and lengthy authentication processes. Therefore, these challenges offer impediments to achieving the optimal degrees of temporal efficiency and scalability, which are critical for the effective operation of large-scale, time-sensitive IoHT systems. Our work provides an authentication solution specifically created for healthcare systems to address these issues. We work in three phases: initializing, registering, logging in, and authenticating. The suggested system combines blockchain technology, Firebase Framework, Bloom Filter, Multi-Factor authentication, and other elements to improve security and efficiency at the same time. We use the Python programming language to simulate the work, and our findings indicate that the Bloom filter decreases the amount of time it takes to determine whether a person is in the system compared to the previous way. Moreover, using Firebase may reduce transaction numbers by up to 73%. Using the Scyther tool, a security analysis of the proposed scheme proved that the suggested plan is safe from possible threats and maintains the IoHT system's scalability

НЕЛИНЕЙНАЯ ДЕФОРМАЦИОННАЯ МОДЕЛЬ СП 63.13330 В СТАДИЙНОЙ ПОСТАНОВКЕ

Normative documents in the field of reinforcement of reinforced concrete structures and the design of prefabricated monolithic structures fairly prescribe consideration of the initial stressstrain state of structures before reinforcement. The calculation can be performed using a normalized nonlinear deformed model according to SP 63.13330. At the same time, it does not explicitly provide for a calculation in several stages. The aim of the study is to develop a calculation method based on a normalized nonlinear deformation model in a staged formulation with minimal differences from the provisions of SP 63.13330. To achieve the goal, the following tasks are set: development of a calculation method, implementation of the method in the form of a universal program and verification of the developed program. The article presents a universal nonlinear deformation model in a staged formulation, which allows, within the framework of certain ratios, to calculate the strength and crack resistance of structural sections at an unlimited number of work stages, taking into account reinforcement, prestressing, initial stresses and cyclic loading (unloading and reloading). The method is implemented as a program in the Python programming language and verified on full-scale and numerical (finite element method) experiments. If we assume the initial stresses and deformations to be zero and calculate in one stage, then the proposed model is reduced to the SP 63.13330 model. For this reason, changing the norms according to the proposed method will not affect the current algorithms for calculating structures in one stage, however, it will significantly increase the scope of the model for calculating prefabricated monolithic, steel-reinforced concrete and reinforced structures. In addition, the accuracy of calculations of prestressed structures will increase, and it will be possible to calculate the cyclic loading of the section.

A model for optimizing an investment portfolio in modern conditions

Subject. This article discusses the issues of improving the methodology for optimizing an investment portfolio in modern conditions. Objectives. The article aims to develop a model for optimizing the investment portfolio, taking into account the investor's risk profile and the limited budget. Methods. For the study, I used a comparative analysis, mathematical modeling in economics, machine learning methods, genetic algorithms, and the Python programming language. Results. The article presents the author-developed model of optimizing the investment portfolio of an enterprise, based on the symbiosis of individual elements and principles of the most effective methods of optimizing the investment portfolio, algorithms for their implementation, mathematical model and practical recommendations. Conclusions and Relevance. The combination of classical methods of investment portfolio optimization and machine learning in the model helps improve the accuracy of assessing the future return of assets and determine their weights in the investor's portfolio. The theoretical significance of the work lies in the fact that the scientific provisions that actualize the task of improving existing or developing new approaches to optimizing the investment portfolio are proved. The practical significance of the study lies in the fact that the presented model is of an applied nature, taking into account the risk profile of the investor, tactical and strategic goals of investment, and budget constraints. The results of the study may be of interest to novice stock market participants, experienced investors, IT specialists engaged in the creation of programs and robots for trading on the stock market using machine learning tools.

Misuse of large language models: Exploiting weaknesses for target-specific outputs

Prompt engineering in large language models (LLMs) in combination with external context can be misused for jailbreaks in order to generate malicious outputs. In the process, jailbreak prompts are apparently amplified in such a way that LLMs can generate malicious outputs on a large scale despite their initial training. As social bots, these can contribute to the dissemination of misinformation, hate speech, and discriminatory content. Using GPT4-x-Vicuna-13b-4bit from NousResearch, we demonstrate in this article the effectiveness of jailbreak prompts and external contexts via Jupyter Notebook based on the Python programming language. In addition, we highlight the methodological foundations of prompt engineering and its potential to create malicious content in order to sensitize researchers, practitioners, and policymakers to the importance of responsible development and deployment of LLMs.

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.