Python Programming Research Articles

The spectrum of synthetic data generation: A comprehensive review

Synthetic data, which is the data produced to mimic the characteristics of actual data without revealing any confidential information, is a much safer option than original data, especially when it comes to extreme instances such as personal data, financial data, or military intelligence. There are substantial dangers connected with the use of real-life data such as assault with the intent to commit identity theft, fraud, and hacking, but because synthetic data (SD) reproduces some of the elements of real data, without infringing on anyone’s privacy, suffers from these risks. The project concentrates on the cutting-edge fields of Language Learning Models (LLM) and Deep Learning (DL) to generate synthetic data that mimics real-world data in its intricacy. Advances in LSTM networks and Generative Adversarial Networks (GAN) produce plausible and useful data in sequence forms for natural language processing and machine learning (ML) augmentation respectively. Applications of this technology include, but are not limited to, the use of augmented datasets to improve medical diagnosis, advanced finance fraud detection systems, and designing fictitious consumers in order to enhance AI-based system recommendations. The project which is implemented with Python programming language and also takes advantage of some open source packages such as SymPy, Pydbgen, Synthetic Data Vault (SDV), and Scikit-learn offers a solution to data scarcity and quality problems in order to improve the performance of the AI models in various sectors.

Soil fertility management on sesame (Sesamum indicum L.) production in Northern Ethiopia: A review.

Sesame (Sesamum indicum L.) is an important cash crop and plays a vital role in many people's livelihoods in Ethiopia. However, its production is low due to many constraints, and low soil fertility is among the major. The previous fertilizer recommendation was a blanket recommendation regardless of the soil fertility status. Hence, the objective of this review is to synthesize the different recommendations and forward to the sesame growing areas. R statistical software and Python programming language used to analyze the chemical and physical soil properties, association among the chemical and physical soil properties and association with soil depth. The organic carbon, total N (Nitrogen), available P (Phosphorus), S (Sulfur) and Zn (Zinc) are below optimum value while K is sufficient in the soil and moderately alkaline soil. The soil particle size is dominantly clay (58.5%). The clay particle size and pH increased while the sand and silt particle sizes, organic carbon, total N, available P and S decreased as the soil depth increased. 204,558.8 ton of sesame stack residue are burned every year, and from this, 7360.03 ton of NPK is lost every year in Western Tigray. The recommended N, P and S for sesame are: (i) 64 and 41kgha -1N for low N content areas and for medium N content areas respectively; 46 and 23kgha -1 P2O5 for low and medium P content areas; (iii) 60 and 30kgha -1S for low medium S content areas may be recommended to boost sesame production.

Investigating students’ awareness of online privacy and cybersecurity: an empirical study with effective cybersecurity training framework

Purpose The main purpose of this study is to assess the level of cybersecurity and social media privacy awareness among college students in Bangladesh and propose a scalable cybersecurity training framework to promote safe online practices. Design/methodology/approach This study used a structured, printed questionnaire survey to collect data from the students of five renowned colleges from the southern part of Bangladesh. Stratified sampling technique was followed and a total of 353 data were collected. Collected data were analyzed using SPSS and Python programming languages. Findings The findings uncovered that most of the students possessed limited knowledge regarding fundamental cybersecurity concepts, though they were using different social media platforms and posting personal information frequently. The study found a significant positive relationship between students’ sharing of personal information on social media and becoming a victim of cybersecurity attacks. Contrarily, becoming a victim of a cybersecurity attack was negatively correlated with their ability to respond in case of cyberattacks. However, most of the students were interested in acquiring knowledge of cybersecurity. Based on these findings, the study proposed a game-based cybersecurity training framework for students named “CyberSAFE”. Originality/value Though several studies have proposed different cybersecurity frameworks in different contexts, this study, to the best of the authors’ knowledge, is the first attempt to explore college students’ cybersecurity knowledge and propose a game-based framework in a developing country setting.

Analysis of the relationship between visual complexity and SEO rank of library websites affiliated with top universities

Purpose Computational aesthetics involves the application of computer-based methods to analyse and understand various phenomena. Given the significance of university library websites in the advancement of science and knowledge, it is essential to consider their aesthetic qualities in conjunction with their web visibility, particularly regarding search engine optimisation (SEO). This study aims to investigate the relationship between visual complexity as an important aspect of the aesthetics of a website and its SEO rankings of top university library websites. Design/methodology/approach This study employed an analytical survey research design, analysing 35 library websites affiliated with top universities as ranked by Times Higher Education in 2023. Visual complexity was assessed using Python Athec programming, a tool specifically developed for computational aesthetic analysis in social science research. Additionally, the AIOSEO online smart tool was utilised to extract SEO scores. Data analysis was conducted using SPSS and Excel. Findings The study found no significant correlation between visual complexity and SEO rankings, with a significance level of 0.125 indicated by linear regression and correlation analyses. This suggests that top university library websites may not require extensive SEO optimisation due to their established credibility and branding. Notably, even those with lower SEO rankings continue to attract international users. Originality/value This research is distinguished by its innovative use of Python programming to measure user experience in the context of aesthetics. It offers new insights into the field of computational aesthetics for the managers of libraries.

Analysis of The Energy Impact of Electric Vehicle Integration in Uruguay

Objective: The objective of this study is to investigate the integration of EVs in Uruguay between 2025 and 2040, based on data from the National Energy Balance (BEN) and the 2005–2030 Energy Policies (PE0530) document, aiming to analyze the impacts of the increasing adoption of EVs on the country's electrical system.. Theoretical Framework: The methodology of this research is based on the development of scenarios derived from indicators of the electric sector, transport sector, population, and vehicle fleet. It is structured around new indicators that allow the comparison of energy demand increases under three EV adoption rates in Uruguay: natural (1%), moderate (3%), and accelerated (10%). Python programming was used to evaluate the extrapolations of these indicators through Simple Linear Regression (RLS) and Simple Exponential Smoothing (SES), determining the most appropriate methodology using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) metrics. Data were collected from governmental websites in Uruguay and other publicly accessible domains. Method: The methodology adopted for this research comprises [concisely describe the study design, including approach, participants, instruments, procedures, etc.]. Data collection was carried out through [explain the specific methods used, such as interviews, questionnaires, observations, among others]. Results and Discussion: The results revealed that EV adoption generates increasing impacts on electrical demand, with 1% and 3% scenarios showing controlled impacts, while the 10% scenario demonstrates a significant increase, requiring strategic adaptations to avoid overloading the electrical system. Despite the usefulness of RLS and SES projection methodologies, their limitations lie in their sensitivity to extreme variations and reliance on historical data, which may hinder accuracy in contexts of rapid changes or unforeseen factors, such as future policies or technological advancements. Research Implications: This study presents practical and theoretical implications, highlighting Uruguay's need to invest in the expansion of the SIN and renewable sources to meet potential future EV demand, as well as to implement integrated policies for electric mobility. Theoretically, it reinforces the use of methodologies like RLS and SES for robust projections, influencing sectors such as the automotive industry, adapting to electrification, and the environmental sector, through emissions reductions, showcasing the impact of interdisciplinary approaches. Originality/Value: This study adds value by highlighting how different scenarios affect the SIN infrastructure of a country, especially one powered by 97% renewable energy sources. The approach can guide the formulation of future energy policies, assisting in infrastructure planning, setting renewable generation targets, and promoting electric mobility. By quantifying demands and benefits, this study provides input for strategic decisions, supporting sustainable energy transition and aligning public policies.

A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks

This study employed some machine learning (ML) techniques with Python programming to forecast the adsorption capacity of MOF adsorbents for thiophenic compounds namely benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyl dibenzothiophene (4,6-DMDBT). Five ML models were developed with the help of a dataset containing 676 rows to correlate the adsorbent features, adsorption conditions, and adsorbate characteristics to the MOF sample’s sulfur adsorption capability. Among the ML approaches, MLP model achieved the best performance with a low mean squared error (MSE) of 0.0032 on the test set and 0.0021 on the training set and mean relative error (MRE) of 15.26% on the test set. Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. Feature importance analysis was performed by utilizing MLP model and shapely additive plan (SHAP) method, and the findings revealed that “initial concentration of sulfur” (SHAP value 0.51) and “contact time” (SHAP value 0.37) were the crucial factors influenced desulfurization process efficiency. Additionally, a comparative analysis of the features utilizing the MLP network classified the factors into three primary categories: process conditions, adsorbent characteristics, and adsorbate characteristics. Consequently, the process condition was identified as the most significant group compared to others. Finally, the desulfurization process optimization indicated the maximum DBT adsorption of 161.6 mg/g for Zr-based MOF could be achieved when the features including BET, TPV, pore size, oil/adsorbent ration, and temperature were tuned around 756 m2/g, 0.955 cm3/g, 5.96 nm, 449.85 g/g, 20.1 °C, respectively.

Метод разработки интеллектуальных тренажёров на основе онтологии предметной области

In academic disciplines, students are required to learn many new concepts, which necessitates extensive training with feedback. An intelligent simulator enables students to practice solving simple problems while receiving explanations for their mistakes, allowing teachers to focus on addressing more complex issues during lessons. This paper presents a method for developing intelligent simulators based on the ontology of the subject domain, implemented as web appli-cations suitable for both classroom and extracurricular use. Representing the problem and the subject area model in RDF format enables logical inference using the Apache Jena Reasoner inference engine. An example is provided of an intelligent simulator designed for learning the order of operations in expressions, supporting the C++, C#, and Python programming languages. The simulator can explain errors, generate explanatory hints, and engage in educational dia-logue through guiding questions. The simulator was tested with undergraduate and graduate students of the Faculty of Electronics and Computer Engineering at Volgograd State Technical University. Most students found the simulator to be more useful than traditional training tests. It can be employed both for independent study and as part of the educa-tional process in classroom settings.

ClearFinder: a Python GUI for annotating cells in cleared mouse brain

BackgroundTissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community.ResultsWe developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we present a use case of ClearFinder GUI for cross-analyzing the same samples with two cell counting tools, highlighting the discrepancies in cell detection efficiency between them.ConclusionsOur easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains.

Optimized Internet of Things Model for Smart Irrigation Using the Drip Method

Irrigation is the regular application of water to the crops so that the water supply to the crop will assist it to grow well. Irrigation is mainly useful in areas where there is a shortage of rainfall. The aim of this study is to optimize an IoT-based model for smart irrigation using the drop method. However, the objectives of the study are to; design an IoT-based model for smart irrigation and implement the optimized IoT-based model for smart irrigation. This study achieved its target; an optimized IoT model for smart irrigation using the drip method. The program has made the following functionality: login page wireframe, dashboard wireframe, system user case diagram, actuators page wireframe, sensor page wireframe and application interface design. The model was tested and approved to fulfill its mission and objectives such as designing an IoT-based model for smart irrigation, implementing the optimized IoT-based model for smart irrigation using the drip method with Python programming language, testing and evaluating the performance of the optimized IoT-based model for smart irrigation water using drip method was then deployed to the farm. Also, helps to solve the problem of scarcity of empirical evidences in the area of the study.

Three-Position Vehicle Control Based on Neural Interface Using Machine Learning

The brain-computer interface is a complex system that allows you to control external electronic devices using brain activity. This system includes several elements – a device for reading brain activity signals, a hardware and software complex that processes and analyzes these signals, and a control object. The main challenge here is the development of methods and algorithms that can correctly recognize and predict the intentions of the person who uses this interface to provide solutions to control problems. This paper describes the mathematical formulation of the equipment control problem. Methods for preprocessing EEG signals, analyzing them, and making decisions about issuing a control signal are described; the structure of the software implementation of these methods is described, as well as a plan for experimental testing of the performance of the entire system that forms the brain-computer interface. For classification of EEG signals the methods of machine learning are used. A modification of the k-nearest neighbors method is proposed – the so-called fuzzy almost nearest neighbors method. An algorithm for the adaptive classification of EEG taking into account the drift of the parameters of the subject's model based on the method of recurrent objective inequalities (ROI) has also been developed. The control algorithm was implemented in the Python programming language. A remote-controlled wheelchair is considered as a control object, and turning the chair to the right or left is considered as a control task. To experimentally test the performance of the developed model and algorithms, more than 15 tests were carried out with five subjects in total. The approach developed and described in this article and its software implementation during testing demonstrated its effectiveness in the tasks of controlling the rotation of a wheelchair. Special attention was also paid to the resource intensity of the software implementation. Methods and algorithms were implemented taking into account the requirements that arise when performing calculations on low-performance devices with a limited amount of memory.

Exploring the synergy of python programming in single point incremental forming

Exploring the synergy of python programming in single point incremental forming

Python Development of Algorithm to find Solution in Pure Strategies of Antagonistic Matrix Game

The article provides an algorithm for finding the optimal solution to the matrix antagonistic game in pure strategies. The algorithm implements the search for the upper and lower prices of the matrix game by implementing the maximal and minimax strategies of the players. The algorithm is encoded using the functionality of the Python programming language. The algorithm is tested using a numerical example.

Connecting Discrete Structures and Python: Transforming Discrete Structures Education

This paper presents a Python-integrated framework for teaching Discrete Structures, focusing on bridging the gap between theoretical concepts and practical applications. The primary goals of this framework are to enhance student engagement, improve conceptual clarity, develop computational thinking, and provide real-world relevance to abstract topics. By leveraging Python’s intuitive syntax and versatile libraries, such as itertools for combinatorics and networkx for graph theory, the framework transforms traditional teaching methods into a hands-on, interactive learning experience. The framework aims to help students connect core topics like propositional logic, set theory, and graph traversal with their computational implementations. For instance, students can write Python programs to generate truth tables, explore set operations, or visualize graph algorithms. These activities foster active learning by providing immediate feedback and enabling experimentation, which promotes critical thinking and problem-solving skills. Another key goal is to demonstrate the real-world relevance of Discrete Structures. Students learn to apply modular arithmetic in cryptography, graph algorithms in network analysis, and recursion in algorithmic problem-solving. This interdisciplinary approach prepares students for modern technological challenges while aligning theoretical knowledge with practical skills. Initial evaluations show significant improvements in student engagement, comprehension, and performance. By integrating Python into the teaching of Discrete Structures, this framework not only bridges the gap between theory and practice but also equips students with the tools and mindset needed to excel in computational fields.

DynPy—Python Library for Mechanical and Electrical Engineering: An Assessment with Coupled Electro-Mechanical Direct Current Motor Model

DynPy is an open-source library implemented in Python (version 3.10.12) programming language which aims to provide a versatile set of functionalities for mechanical and electrical engineers. It enables the user to model, solve, simulate, and report analysis of dynamic systems with the use of a single environment. The DynPy library comes with a predefined collection of ready-to-use mechanical and electrical systems. A proprietary approach to creating new systems by combining independent elements defined as classes, such as masses, springs, dampers, resistors, capacitors, inductors, and more, allows for the quick creation of new, or the modification of existing systems. In the paper examples for obtaining analytical and numerical solutions of the systems described with ordinary differential equations were presented. The assessment of solver accuracy was conducted utilising a coupled electro-mechanical model of a direct current motor, with MATLAB/Simulink (R2022b) used as a reference tool. The model was solved in DynPy with the hybrid analytical–numerical method and fully analytically, while in MATLAB/Simulink strictly numerical simulations were run. The comparison of the results obtained from both tools not only proved the credibility of the developed library but also showed its superiority in specific conditions.

SCORBOT arm control using pseudoinverse jacobian control for Bézier path

The kinematics of manipulators represents a fundamental challenge in the field of robotic automatic control. This challenge involves transitioning between Cartesian space and combinatorial space. The kinematic equations governing motion are formulated using the Denavit-Hartenberg (D-H) notation. This paper introduces a method for the inversion of a SCORBOT ER-V plus manipulator, aimed at meticulously analyzing the arm's movement from one spatial point to another. To implement this solution, it is necessary to identify only the starting and ending points in space, along with the geometric constraints of the manipulator. The SCORBOT-ER V plus is a straightforward top-bottom robot featuring five revolute joints, recognized for its reliability and safety, particularly in laboratory and training environments. This experiment provides students with practical experience in robotics, automation, and control systems, highlighting the principles related to the operation and causation of various phenomena. The Python programming language is utilized to develop a mathematical model that complies with a defined set of constraints and guidelines. The analysis of object motion demonstrated that the output from the Python script closely aligned with the actual measurements obtained from the robotic arm. Differential movement is employed as a technique to analyze and characterize motion at different points of the robot, thereby facilitating the examination of robotic movement over short durations. This paper discusses both forward modeling and inverse kinematics within the realm of robotics. The traditional D-H model is applied to create the mathematical framework essential for determining and assessing the position of the end-effector in a 5-Degree-Of-Freedom (5DOF) robot, which will then be used to calculate the necessary joint configurations. Finally, we present the results derived from this experiment.

License Plate Detection Methods Based on OpenCV

---Since the technology was discovered with the automobile, this advancement in computer vision detection technology has slowly become part of the intelligent traffic management system. This technology can be used to divide vehicle images into different parts and outline specific regions for further identification across many systems. It is also highly applied in intelligent traffic management and video surveillance for automobiles among other applications. This paper introduces a new state-of-the-art License Plate Detection system using the CV2 Algorithm. Existing approaches have been highly prone to substantial sensitivity of the changes in illumination, not well defined and rather complex backgrounds themselves together with the plates which deterd recognition. Generally, the proposed future system is expected to exhibit high accuracy improvement and the cost of recognition for the present system may offset the problems that the current system has. All these considerations are taken into account when designing the proposed system. The proposed system based on CV2 Algorithm is supposed to exhibit considerable efficiency and robustness in handling noisy data. In this context, we gave our working model the analytical results to prove that our proposed model is much more beyond the current system since it uses the Python programming language. KEYWORDS--- OpenCV2 Algorithm, OCR (Optical Character Recognition system), Automatic license plate recognition (ALPR), BINARIZATION, Gray threshold (GT), EDGE DETECTION, NUMBER PLATE LOCALIZATION, DESKEWING, SPYDER3.

การพัฒนาระบบสนทนาอัตโนมัติด้วยวิธีการจำแนกแรนดอมฟอเรส สำหรับงานทะเบียนและสถิติการศึกษา สำนักส่งเสริมวิชาการและงานทะเบียนของมหาวิทยาลัยเทคโนโลยีราชมงคลตะวันออก

The paper presents the establishing chatbots utilizing the random forest classification for division of registration and education statistics for the Office of Academic Promotion and Registration for Rajamangala University of Technology Tawan-ok. The aim is to research best practices to implement an automated response system for education statistics and registration through online platforms in university departments, assessing the effectiveness of the system as well as the user’s acceptance and extent to satisfaction regarding the information system's use. Using the Random Forest Classification and the Python programming language, it constructed a model using the learning data set, which it then applied to the development of chatbots on the Dialogflow platform. The model evaluation resulted in an Accuracy of 97.47, Precision of 92.19, Recall of 93.67, and F-Measure of 92.92, the expert's assessment of efficiency had an average of 4.26, and the user group's satisfaction evaluation had an average of 4.28. To efficiently operate the organization's informative and news release services for educators, students, and related staff.

Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation.

To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (n = 69) and a validation set (n = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561-0.960). The model's accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation.

A Review Of Precision And Innovative Farming For Nigerian Farmers

This study successfully developed a logistic regression model (LRM), a machine learning algorithm to assess farmers’ perceptions of precision agriculture (PA) based on key factors such as gender, educational level, farming experience, household size, farm income, access to credit, farm size, and awareness of precision farming. Python programming language was the primary language, utilizing libraries such as numpy, scikit-learn, matplotlib, and seaborn for data processing, model building, and visualization. The dataset comprised 350 samples and was split into training (70%) and testing (30%) sets. The model achieved an accuracy of 81.9%, with a recall of 97.7%, F1 score of 0.899, and precision of 83.3%, demonstrating its effectiveness in identifying positive perceptions of PA. The confusion matrix showed a true positive rate of 84 and a false positive rate of 17, suggesting a need for model improvement in handling false positives. The ROC curve showed an AUC of 0.58 indicating that the model has no discriminatory ability. Overall, the findings suggest that the highlighted factors influence farmers’ perceptions of PA. Python proved highly efficient for implementing this machine learning-based study

Entropy Analysis of Carreau Nanofluid Flow in the Presence of Joule Heating and Viscous Dissipation Past Unsteady Stretching Cylinder

ABSTRACTThe study aims to investigate the irreversibilities of a Carreau nanofluid flow over, unsteady stretching cylindrical sheet exposed to radiation, non‐Darcy porous medium, viscous dissipation, joule heating, etc. It provides how energy produced in the nanofluid flow is used efficiently by minimizing the irreversibilities. The governing partial differential equations are transformed into first‐order initial value problems by similarity transformation and linearization. The shooting technique and an open‐source Python programming package are used to solve the initial value problems using the Runge–Kutta sixth‐order, and the numerical approach is validated using published articles. Basic flow profiles and, most importantly, entropy generation are examined using graphs in relation to relevant parameters. Skin friction and the behavior of heat and mass fluxes in response to various parameters are also examined. The results of the study demonstrated that the entropy creation is initiated by an increase in the magnetic and curvature parameters, as well as the Eckert, Brinkman, and porosity parameters. However, when the Forchheimer number increases, entropy generation decreases. An increase in the Eckert number, Prandtl number, and radiation parameter motivates the irreversibility due to heat transfer, whereas as the Weissenberg number rises, the irreversibility of heat transfer falls around the wall. According to the numerical values in the table, growth in Weissenberg number, thermal Biot number, Forchheimer number, curvature parameter, and radiation parameter initiate the magnitude of the rate of heat and mass transfers. In contrast, the rates fell as the Eckert and Prandtl values rose. Analysis of energy conversions and system efficiency can be done using this study, particularly in heat engines, refrigeration systems, and other thermodynamic processes.