Python Programming Language Research Articles

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Design of Tools for Visualizing Thermodynamic Concepts in Steam Power Plant Trainer Processes with Web-Based Exploratory Data Analysis (EDA)

Thermodynamics is considered one of the most complex and challenging subjects for many students. This is primarily due to comprehending abstract concepts such as entropy, enthalpy, and energy flow, which involve complex mathematical equations and are rarely accompanied by tangible visualizations. This research aims to design, develop, and test a data-based visualization tool for thermodynamics testing results. This study collected and processed data from thermodynamics testing and simulations, such as the mini-steam power plant trainer used as a teaching aid in thermodynamics education, as the foundation for designing a data-based visualization tool for thermodynamics concepts. The visualization tool was created using the Python programming language integrated with the web-based Streamlit framework. The designed visualization tool encompasses various features, including automated data reporting, visualization of variable correlations using correlation heatmaps, Sankey diagrams for visualizing energy flow, and the capability to predict electrical output using machine learning integrated with three different machine learning algorithms. The visualization tool was evaluated by thermodynamics experts using a Likert scale. Based on the results obtained, the experts gave an average score of 4 in the information accuracy aspect in the good category. This shows that the information displayed in this visualization tool is by thermodynamics learning at Padang State University. In the visualization aspect, experts gave an average score of 4.25, which is in the Good and Very Good range. In alignment with the education aspect, experts gave an average score of 3.75, which is close to the good category. This shows that this aspect is considered suitable for studying thermodynamics, although shortcomings still need to be corrected. Experts gave a relatively high assessment of the Ease-of-Use aspect, with an average score of 4.5, with a range of Good and Very Good. This enables students to better understand complex patterns, cause-and-effect relationships, and parameter changes within thermodynamics concepts.

Reseña del uso del lenguaje de programación Python en el Desarrollo de Software

Introduction: This article provides a concise and clear definition of the Python programming language, highlighting its key features and its significance in the current landscape of software development. Python, renowned for its simple and readable syntax, has gained popularity due to its versatility and ease of use. Objective: The primary objective of this study is to answer the question: What role does Python play in current software development and what are its future prospects? This analysis aims to understand the impact of Python on the software industry and assess how its evolution may influence future technological trends. Methods: To address this objective, a literature review was conducted to gather relevant information about Python. Rigorous selection criteria were established to ensure the quality and timeliness of the consulted sources, and a data analysis was carried out that combined qualitative and quantitative approaches. Results: The article summarizes the main advantages and disadvantages of Python, as well as exploring its various areas of application. Python offers advantages such as ease of learning and a robust ecosystem of libraries, but it also presents disadvantages such as slower execution speed compared to other languages. Conclusions: In conclusion, the study synthesizes the results obtained, evaluates the current use of Python in software development, and provides practical recommendations for developers, highlighting the importance of staying updated with the latest versions and tools available.

Analysis of methods and algorithms for image filtering and quality enhancement

This paper addresses the prevalent issue of image noise and presents methods for its mitigation. The paper describes, analyses and tests a variety of image filtering techniques, with specific reference to their use in different contexts. The filtering methods can be classified into two principal categories: linear filters, which include the Gaussian and mean filters, and non-linear filters, which comprise the median filter, the Fast Fourier Transform (FFT), the Non-Local Means (NLM) filter, and the anisotropic diffusion filter. The efficacy of each filter is mathematically described and evaluated on RGB images using the Python programming language. The study delineates the evaluation metrics and their respective advantages and disadvantages. The Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) are employed as criteria for the analysis of algorithm efficiency. Furthermore, the mean execution time for each algorithm is also monitored. The experimental data suggests that linear filters are relatively fast but produce inferior results and are best employed as preparatory measures. Non-linear filters have been demonstrated to be more robust and applicable to a variety of noise types, although it has been established that they require parameter fine-tuning. The study demonstrates that anisotropic diffusion is suitable for both manual image processing and real-time applications, offering an optimal balance between processing speed and denoised image quality. NLM is optimal for high-quality single image processing due to its superior results, despite a slower processing speed. FFT is noted for its efficiency in eliminating periodic noise. Further research will be conducted on advancing filtering techniques for different real-world scenarios and autonomous systems.

Сравнительный анализ методов построения систем обработки зашифрованных данных и их сравнение для решения задач машинного обучения

The article discusses two methods for constructing encrypted data processing systems based on homomorphic encryption and separation calculations. Each method differs from each other in the presented algorithm and data processing model. Basic operations on encrypted data are considered, namely, multiplication and addition. The following libraries were used for the study: tenSEAL, tensorflow and PyTorch. The tenSEAL library is a fork of the full homomorphic encryption tool created for the C++ programming language, but adapted for the Python programming language used in the study. This library allows you to use the method of full homomorphic encryption in constructing a computational model, the task of which will be to process encrypted data. To implement the method of separation calculations, better known as multi-party computation, the tensorflow library will be used, which allows you to create several tensors and train them simultaneously, which in turn makes it possible to implement the principle of separation calculations.

The Effect of Cooperative Learning on Academic Performances and Computational Thinking Skills in the Computational Problem-Solving Approach

In this study, we aimed to investigate the impact of cooperative learning on the computational thinking skills and academic performances of middle school students in the computational problem-solving approach. We used the pretest-posttest control group design of the quasi-experimental method. In the research, computational problem-solving activities regarding 6th graders' goals of the "heat and matter" unit, were applied individually by Group 1 and cooperative learning by Group 2. These activities required students to use computational thinking skills and code using the Python programming language. The study involved 34 students from the 6th grade of a private middle school located in the capital city of Turkey. The Computational Thinking Test (CTt) and an academic achievement test were used as pre-tests and post-tests to monitor students' computational thinking skills and academic performances. Additionally, computational problem-solving activities were scored to track the progress of students' computational thinking abilities. Non-parametric Mann Whitney U and Wilcoxon T-tests were utilized to analyze the progression of pupils' computational thinking abilities and academic success, and ANCOVA was used to analyze CTt scores. Qualitative data were collected through semi-structured interviews at the end of the process to determine students' views on the computational problem-solving process. Results revealed a significant increase in students' academic achievement and computational thinking skills in both groups. A comparison of post-test scores showed no significant difference between groups. It is anticipated that the research results will make meaningful contributions to the literature concerning the progress of computational thinking skills in secondary school students.

Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations

RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the problems of short-term forecasting. To test the applicability of the multi-agent approach to determine the features used to build a machine learning model of short-term forecasting of power consumption. To build machine learning models. To study the influence of technological factors on the accuracy of forecasting of power consumption. To apply the SHapley Additive exPlanations and analyze its interpretation of the forecasting results.METHODS. Pre-processing of the dataset, construction and testing of machine learning models were made in the programming language Python 3 using opensource libraries Scikit-Learn, XGBoost, LightGBM, Shap.RESULTS. The article describes the relevance of the topic of short-term forecasting of power consumption by the enterprise of the oil and gas industry within the ESG-approach. The method of selecting the features used using a multi-agent approach to build a machine learning model was developed. Machine learning models were built. Experimentations with the consideration of different features were made. Interpretation of results using SHapley Additive exPlanations was made.CONCLUSION. The use of technological factors of power consumption of compressor yards and natural gas air coolers allowed to increase the accuracy of forecast of power consumption from 8.82 % to 3.65 %. The application of the SHapley Additive exPlanations allows to interpret the results of machine learning models and confirms the need to consider technological factors in the task of short-term forecasting of power consumption of oil and gas industry.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.

Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis

The work program promised by the 2024 Presidential Candidate and Vice Presidential Candidate pair, namely, Prabowo Subianto and Gibran Rakabuming Raka, one of which is a free lunch program, this program is an effort to improve community welfare, but this has attracted public attention on social media, one the other is platform X. The public response to the free lunch program is the main focus. In this research, researchers analyzed sentiment related to the free lunch program using the Naive Bayes method using the Python programming language on Google Colab to analyze sentiment towards social media users. X. the results obtained from 920 tweets data, there are 167positive value tweets, 744tweets are negative. The evaluation results with the confusion matrix showed an accuracy of 86.95%, with precision 93%, recall 61%, and F1-Score 65%.From the results of this research it can be concluded that the majority of the public who commented on social media X gave a negative response to the free lunch program. These results can be used as material for government evaluation in determining effective and efficient policies for society.

Social Media Sentiment and Gender on Peer-To-Peer Lending Performance: Machine Learning Approaches

One innovation brought about by fintech is peer-to-peer lending. However, in recent years with rapid growth, unfortunately, there are still many negative sentiments about the peer-to-peer lending industry. Especially with the advancement of technology and the internet, all information about peer-to-peer lending is easily accessible through social media. The amount of negative information about peer-to-peer lending can influence opinions or sentiments towards the performance of peer-to-peer lending platforms and invite the public to comment on social media. Additionally, the gender factor of the information provider also affects the performance of the peer-to-peer market. Based on the explanation above, there has never been any previous research conducted in Indonesia, so this study will examine how the influence of social media information sentiment and the gender factor of the information provider affects peer-to-peer lending performance. The analysis method used is logistic regression, and the data used is obtained through machine learning using the Python programming language on Google Collab from social media opinions and the gender that provides opinions on social media. Meanwhile, the financial performance of peer-to-peer lending is taken from the annual performance report of the Financial Services Authority (OJK) by default. From the results of this study, it was found that the gender of the information provider affects the financial performance of peer-to-peer lending. However, simultaneously, social media sentiment and the gender of the information provider affect the financial performance of peer-to-peer lending.

Exploring the Utilisation of Generative AI Tools by Undergraduate First-Year Mechanical Engineering Students in Programming Assessments.

Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.

What Drives Social Accounting Research? Insights From a Bibliometric Analysis

ABSTRACTWith the increasing importance of sustainability and corporate social responsibility across the globe, social accounting practices are being accepted worldwide; however, the rate of acceptance varies. By analyzing trends, patterns, and future areas of social accounting research, this study aims to address the gap in systematic analysis in this area of research. In the present study, social accounting literature is reviewed through bibliometric techniques, and the vast body of research associated with social accounting is systematized to identify major trends, the most successful authors, methodological variety, and topic relevance. Bibliographic data are retrieved from Scopus and Web of Science databases. Data analysis is performed using Biblioshiny R and Python programming languages. The corpus includes publications from 1946 to March 2024, and 2286 publications remain in the final sample. The results indicate a 6.2% growth rate per year, and the leading contributing country is the UK, followed by Australia, Italy, and the USA. Methodological diversity analysis shows a commitment to empirical research, in which surveys and quantitative research dominate. The topic analysis identified 10 major topics. The studie's innovation lies in the comprehensive nature of bibliometric analysis, which provides insights into the growth, development, and diversity of social accounting research. The implications are related to the increasing importance of sustainability and environmental, social, and governance performance areas, while future research opportunities include the use of digital technologies to achieve improved transparency and stakeholder engagement.

INCREASING THE STABILITY OF ORGANISATIONAL AND TECHNICAL SYSTEMS THROUGH THE USE OF MACHINE LEARNING

The stability of technical and organisational-technical systems is an integral factor in the functioning of a wide range of complex technical and organisational-technical systems, such as energy systems, industrial facilities, transport systems, and others. While it depends on various factors, there are currently no means to support decision-making for automated dispatch control systems users in case of failures. The article analyses the effects of failures and breakdowns in complex technical and organisational-technical systems. It provides examples of major technogenic disasters. The study examines the human factor influence on the emergence of technogenic disasters. Currently, automated dispatch control systems do not have any means to facilitate decision-making by providing the dispatcher with a clear plan of action to troubleshoot and eliminate a possible disaster. The authors propose a concept of support means for decision-making in response to crises using automated dispatch systems for managing complex organisational-technical systems based on machine learning and artificial intelligence. The means is based on machine learning methods and has a two-component structure: an artificial neural network that defines the type of failure and a programme component that selects the algorithm. The neural network is implemented in the Python programming language, and the user interface is in the C# language. The neural network fulfils the classification task defining the failure based on an array of logical variables that demonstrate the change of state of system elements. The programme component uses the database created with SQL language to define the algorithm of actions for the dispatcher. The authors describe the dataset for the model in case of its use in energy systems. There is also a description of the model components. The model’s distinguishing features are adaptivity due to machine learning methods and the possibility of further model training to increase the classification accuracy based on its use cases. Keywords: machine learning, automated dispatch control systems, decision-making support, system stability.

Sentiment Analysis Techniques: A Comparative Study of Logistic Regression, Random Forest, and Naive Bayes on General English and Nigerian Texts

This research investigates sentiment analysis on two distinct datasets: a general English dataset and a Nigerian dataset (Gangs of Lagos movie review), using three machine learning algorithms: Logistic Regression, Random Forest, and Naive Bayes with python programming language and its libraries. The study aims to evaluate and compare the performance of these models across different linguistic and cultural contexts. Results indicate that Logistic Regression consistently outperforms the other models, achieving the highest accuracy and balanced performance across sentiment classes. Random Forest provides comparable results but struggles with positive sentiment detection in the Nigerian dataset. Naive Bayes shows the lowest overall accuracy, with significant challenges in recall for certain sentiment classes. These findings highlight the importance of model selection and tuning tailored to specific datasets for effective sentiment analysis.

Differential Equations and Applications to COVID-19

This paper focuses on the application of the Verhulst logistic equation to model in retrospect the total COVID-19 cases in Senegal during the period from April 2022 to April 2023. Our predictions for April 2023 are compared with the real COVID-19 data for April 2023 to assess the accuracy of the model. The data analysis is conducted using Python programming language, which allows for efficient data processing and prediction generation.

CO2 emission heat map of Gulf Cooperation Council region using Python for Geographic Information Systems

Abstract As part of the efforts to ensure a place in the global competitive landscape, Oman is aligning itself with sustainable development through diversified economic measures. To address the United Nations thirteenth Sustainable Development Goal (SDG) which is climate action, the stakeholders of Oman need to understand the carbon dioxide (CO2) emissions and compare the amount of CO2 with the emissions in neighbouring Gulf Cooperation Council (GCC) countries such as United Arab Emirates (UAE), Bahrain, Qatar, Kuwait, and Kingdom of Saudi Arabia (KSA). In this research, an attempt is being made to collect the CO2 emission for all these countries and show them on a web map using python programming language. The data ranging from year 1964 to 2021 is collected from CO2 and Greenhouse Gas Emissions in Comma Separated Values (csv) format. For each country, an approximate location for Capital in terms of Latitude and Longitude is taken from google maps. Using folium library of python, these locations and CO2 emission for a particular year is plotted on google map Application Programming Interface (API). The findings reveal that Oman’s CO2 emissions from fossil fuels and industry in 2022 year is 72 million tonnes which is relatively lower when compared to those of smaller GCC countries such as Qatar (101 million tonnes) and the UAE (244 million tonnes). Nonetheless, a gradual upward trend of 12 million tonnes of CO2 emissions has been observed in Oman between the years 2019 to 2022. The python program provides an easy way to visualise the CO2 emission heatmap without the need of any proprietary software or program.

Investigation of Fractional Order Tumor Cell Concentration Equation Using Finite Difference Method

المعادلة التفاضلية الكسرية توفر إطارًا رياضيًا قويًا لنمذجة وفهم مجموعة واسعة من الظواهر المعقدة وغير المحلية والمعتمدة على الذاكرة في مختلف التطبيقات العلمية والهندسية والعالمية. تعد الاعتلالات الكبدية أورامًا سرطانية ثانوية تتطور في الكبد نتيجة انتشار خلايا السرطان منورام سرطاني أصلي ينشأ في جزء آخر من جسم الإنسان. التركيز الأساسي لهذه الدراسة هو فهم نمو الأورام في الكبد البشري، سواء بوجود أو بدون علاج دوائي. ولتحقيق ذلك، يتم استخدام معادلة تفاضلية جزئية معرفة بالترتيب الزمني والكسري، ويتم تحليلها باستخدام أساليب عددية. يتم استخدام المشتقة كابوتو لاستكشاف تأثير العلاج الدوائي على نمو الورم. لحل النموذج الرياضي عدديًا، تم تطوير طريقة الفروق المحدودة كرانك-نيكولسون. يتم اختيار هذه الطريقة بسبب خصائصها المميزة، بما في ذلك الاستقرار الغير مشروط والدقة من الدرجة الثانية في الأبعاد الزمنية والمكانية. الاستقرار والدقة التي توفرهما هاتان الخصائص أمران حاسمان لضمان موثوقية النتائج المحصلة في هذه البحث. يتم تقديم النتائج والتحققات المستمدة من هذه الدراسة بفعالية من خلال تمثيلات بصرية متنوعة. تعتبر هذه الوسائل البصرية أدوات لا غنى عنها لفهم التأثير العميق للعلاج الدوائي على نمو الأورام. من خلال هذا التمثيل البصري، يمكن للشخص الحصول على فهم أوضح وأكثر تفصيلًا للديناميات المعقدة التي تحدث. تم الوصول إلى الحل العددي لهذه المشكلة المعقدة من خلال تنفيذ خوارزميات تم تطويرها بدقة باستخدام لغة البرمجة بايثون المتعددة الاستخدامات والقوية. مرونة بايثون والمكتبات الشاملة وإمكانيات الحساب العددي القوية تجعلها الخيار المثالي للتعامل مع تعقيدات هذه الدراسة.

Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.

Efficient‐Residual Net—A Hybrid Neural Network for Automated Brain Tumor Detection

ABSTRACTA multiscale feature fusion of Efficient‐Residual Net is proposed for classifying tumors on brain Magnetic resonance images with solid or cystic masses, inadequate borders, unpredictable cystic and necrotic regions, and variable heterogeneity. Therefore, in this research, Efficient‐Residual Net is proposed by efficaciously amalgamating features of two Deep Convolution Neural Networks—ResNet50 and EffficientNetB0. The skip connections in ResNet50 have reduced the chances of vanishing gradient and overfitting problems considerably thus learning of a higher number of features from input MR images. In addition, EffficientNetB0 uses a compound scaling coefficient for uniformly scaling the dimensions of the network such as depth, width, and resolution. The hybrid model has improved classification results on brain tumors with similar morphology and is tested on three internet repository datasets, namely, Kaggle, BraTS 2018, BraTS 2021, and real‐time dataset from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. It is observed that the proposed system delivers an overall accuracy of 96.40%, 97.59%, 97.75%, and 97.99% on the four datasets, respectively. The proposed hybrid methodology has given assuring results of 98%–99% of other statistical such parameters as precision, negatively predicted values, and F1 score. The cloud‐based web page is also created using the Django framework in Python programming language for accurate prediction and classification of different types of brain tumors.

Contributions of computing in geosciences: a case study of geometallurgical forecasting

Geometallurgical modeling has been improved over the years with the incorporation of mineralogical, chemical, and metallurgical information. However, the diversity of the database brings challenges since the information is not unified in procedures, sample support, or quantities. Characteristics such as nonlinearity and nonadditivity of the geometallurgical database impose even more difficulties. The objective of this article is to demonstrate the feasibility of geometallurgical models from multivariate techniques combined with computational tools. The algorithms used in this study were developed in Python programming language. The multivariate analysis and machine learning techniques allowed the grouping of transitional typological domains and the quantification of uncertainty; the selection and reduction of the dimensionality of the heterotopic database; and the prediction of metallurgical behavior from exhaustive primary variables using a transfer function. The results were verified and proved to be satisfactory. Other benefits of the computational structure were: reduction of subjectivity; automation; and dynamic updates of models using new data.