Python Programming Research Articles

Comprehensive Review and Comparative Analysis of Keras for Deep Learning Applications: A Survey on Face Detection Using Convolutional Neural Networks

This paper aims to provide a literature review and comparative analysis focused on the importance of using Python as the primary language for machine learning (ML), deep learning (DL): The incorporation of libraries adapted to different areas is highlighted. One of the most depressing things about using Python in the programming sector is its easy and extensive libraries. Of these few, Keras shines with the majority of its design decisions being made based on the core concepts. Keras allows a number of possibilities for model deployment in a production mode, it supports multiple GPUs properly, and it also supports distributed model training. The design of Keras is very simple to use and can be easily learned due to its usage of Python programming language, making it a good open-source tool to be used in building and testing deep learning (DL) models. This paper focuses on Keras, an open-source deep learning application program interface that runs on Python and is based on TensorFlow but compatible with others such as PyTorch, TensorFlow, CODEEPNEATM, and Pygame. The review goes deeper into the details regarding Keras, including its goals, issues it sought to address, achievements it has made and some lessons drawn from its use.

Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

Learning Python Programming in Middle School - an Innovative path to Digital Literacy

In today's world, digital literacy plays a key role in preparing young people for future challenges. Learning to program, especially in Python, is becoming an increasingly important part of high school education. This article draws attention to innovative methods and approaches to implementing a Python programming curriculum in secondary schools, considering them as an effective path to developing digital literacy in students. The article discusses the benefits of learning Python, its role in developing programming and logical thinking skills, and offers recommendations for using innovative teaching methods such as online courses, project work, and competitions to achieve digital literacy goals in high school.

An empirical study of fault localization in Python programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java—the most common choices for fault localization research—carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first multi-family large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.’s recent large-scale empirical study of fault localization in Java (Zou et al. 2021) as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection (Widyasari et al. 2020). The results replicate for Python several results known about Java, and shed light on whether Python’s peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.

Pengamatan Testing Sistem Informasi Pada Website DemoBlaze

This project aims to test a dummy e-commerce website to ensure it meets the requirements and functions properly as expected. The testing is conducted using the Python programming language with IntelliJ IDEA as the Integrated Development Environment, and utilizing a tool called Selenium to test the website with Chrome WebDriver as the connector to execute commands on the Google Chrome browser. The testing process includes simulating various user activities such as registration, login, adding items to the cart on the website, and online payment transactions. Each testing step is designed to identify and fix potential issues, ensuring that all main website functions run smoothly and meet user expectations. Furthermore, this testing aims to evaluate students' ability to write effective testing scripts. By conducting this testing, students are expected to gain valuable practical experience in software testing and enhance their understanding of the importance of testing in the software development cycle. The results of this testing are expected to enhance students' understanding of the importance of testing in the software development cycle and ensure that the e-commerce site can provide an optimal user experience. Overall, this project emphasizes the importance of comprehensive and structured testing to seek high-quality software.

COMPARATIVE ANALYSIS OF DIFFERENT VARIANTS OF AZIMUTHAL CONFORMAL PROJECTION FOR THE TERRITORY OF THE REPUBLIC OF SERBIA

<p>The paper analyzes four variants of conformal azimuthal projections for the territory of the Republic of Serbia. In the procedure, a double conformal mapping was applied. As it is common practice to perform mapping in azimuthal projections from a sphere to a plane, it was initially necessary to perform a conformal mapping from the ellipsoid to the sphere, after which a conformal mapping from the sphere to the plane was executed. The central point of the projection is a point with a value of 44° north latitude and 21° east longitude. The mapping territory is trapezoidal in shape, defined by the values of the geographic latitudes and longitudes of the southernmost (41°53'), northernmost (46°11'), westernmost (18°49'), and easternmost (23°) points in the territory of the Republic of Serbia. The mapping variants are defined based on different values of linear scale at the central point of the projection. Data processing was carried out within the Jupyter Notebook component, part of the Anaconda software package, using the Python programming language. The results are presented numerically, graphically, and visualized using AutoCAD software.</p>

Exploring the role of personal quantification in alleviating generalized anxiety disorder among Chinese PhD students: A cross-sectional study.

Personal quantification plays a crucial role in preserving individual mental health. However, in previous research, its effectiveness in alleviating generalized anxiety disorder (GAD) has not been conclusively established. This study explores the impact of personal quantification on GAD among PhD students. The research data was obtained through questionnaires distributed to 308 PhD students across universities in China. Among these students, 118 anxiety-free participants were excluded, yielding valuable data from 190 students with GADs. We employed Python programming language and SPSS software for the empirical analysis. The results illustrated that personal quantification significantly and negatively impacted GAD (β = -0.148, P = .002), concurrently producing a significantly positive effect on self-efficacy (β = 0.359, P < .001). Further analysis showed that through 5000 sampling iterations and a 95% confidence level, self-efficacy significantly reduced certain symptoms of GAD (β = -0.1183; P = .026; 95% Cl: -0.2222 to -0.0144). Moreover, when the coefficient of self-efficacy was significantly negative, the impact of personal quantification on GAD remained statistically significant (β = -0.1056; P = .033; 95% Cl: -0.2025 to -0.0087). The findings indicated that personal quantification has a significant role in alleviating GAD among PhD students, which is partly mediated through self-efficacy. This study contributes valuable insights to the nonpharmacological alleviation of GAD in Chinese PhD students.

Предиктивное моделирование эксплуатационных свойств пеностекла с использованием моделей машинного обучения на основе линейной регрессии

Building construction requires the use of efficient thermal insulation materials such as foam glass in view of energy conservation. The paper considers predictive modelling of the performance properties of foam glass using machine learning models. The paper presents a mathematical description of the additives impact in the charge on the properties of foam glass. Nine charge compositions for foam glass synthesis were developed and their main microstructure parameters were determined. The authors tested the regression models using the JupyterNotebook software environment and the SciKit-Learn library in the Python programming language. The paper analyses the regression equation coefficients and estimates the modelling error. The obtained results confirm the effectiveness of predictive modelling of foam glass performance properties on the basis of linear regression.

Penerapan Algoritma K-Means Untuk Clustering Penilaian Layanan Berdasarkan Indeks Kepuasan Mahasiswa Universitas Nurul Jadid

Student Satisfaction Index has a very important role in the university environment as it is closely related to the accreditation assessment process. In this context, Nurul Jadid University faces shortcomings in calculating student satisfaction, which has the potential to disrupt the efficiency of time use and have an overall negative impact. As a solution, this research aims to cluster student answers using the K-Means algorithm implemented through the Streamlit web platform with the Python programming language. The results showed that this approach was able to produce excellent clustering, with an accuracy rate of 97%. The main objective of this research is to improve efficiency in the process of measuring student satisfaction levels, with the hope of making a significant contribution to improving the quality of university services and the process of evaluating university accreditation more efficiently. As such, this research has important implications in improving the overall performance of the university in the face of challenges.

Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments.

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Direct-to-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and user-friendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.

DETECTION OF UNHEALTHY WEBSITES USING MACHINE LEARNING

In recent years, advancements in Internet and cloud technologies have led to a signicant increase in electronic trading in which consumers make online purchases and transactions. Accompanying this achievement are vices like unauthorized access to users' sensitive information and damages to enterprise resources. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. This study aims to develop an efcient machine-learning program to detect phishing websites with high accuracy. Most phishing webpages look identical to the actual web pages and various strategies for detecting phishing websites, such as blacklisting, and heuristics, among others have been suggested. Existing research works showed that the performance of the phishing detection system is limited and there is a demand for intelligent techniques to protect users from cyber-attacks. A Uniform resource locator (URL) detection technique based on a supervised machine learning approach – Naïve Bayes is employed and implemented in Python programming language. The efcacy of this approach was determined on a phishing dataset made up of 7900 malicious and 5800 legitimate sites, respectively. The results show that using the proposed methodology an accuracy of 96% can be achieved by using stacking, ltering along the Naïve Bayes and logistic regression. This study thoroughly investigates the use of machine laearning with features extracted from the URLs and was able to showcase common words for the identication of either phishing (unhealthy) or good websites and proffered a guide to end users against the recent approaches in malicious URLs detection.

ОБРАЗОВАТЕЛЬНЫЕ ИТ-ПРОЕКТЫ КАК СРЕДСТВА ВЫЯВЛЕНИЯ И РАЗВИТИЯ ЦИФРОВОЙ ГРАМОТНОСТИ УЧАЩИХСЯ ШКОЛ

The article analyzes the problems of conducting the educational course “Fundamentals of Programming in Python” within the framework of the “Code of the Future” project with the participation of the provider - Volgograd State Technical University. The tasks of each participant in the learning process are determined, and negative conditions affecting it are identified. The authors determined the input and output indicators for the formation of digital competencies among students in the field of programming, confirming the leadership and necessity of the project.

Study the Emitter-Base Forward Current in Heterojunction Bipolar Transistors Using Python

Abstract In GaAs-type materials, the primary mechanism for intra-valley scattering of low-energy electrons is their interaction with acoustic photons. These collisions are of the elastic and isotropic type, with their main effect being the nearly uniform scattering of the velocity vector of the electrons. Since the drift current in heterojunction bipolar transistors is proportional to the electric field, the contribution of the majority drift current cannot be neglected, as even a very small internal field produces a significant current when the number of carriers is very high. At the metallurgical heterojunction (x = 0), the total current is equal to the sum of the two thermionic emission components. The paper analyses the variation of the direct current of the Al0.3Ga0.7As/GaAs heterojunction, used as an emitter-base NP junction in heterojunction bipolar transistors, using the Python program.

Sentimental reflection of global crises: Czech and Ukrainian views on popular events through the prism of internet commentary

Abstract Social media have become a part of our lives, and their use helps us learn about events and comment on them with certain emotions. The purpose of our study was to determine the most frequent tone (positive, negative, neutral) of comments on impactful emergency and crisis news in the Czech Republic and Ukraine on a specific topic (pandemics, war, natural disaster etc.) using the sentiment analysis method. The methods of the study included a theoretical analysis of literature, social media (Twitter, Telegram), a Python program using: large language models GPT-3.5-Turbo and Twitter-XLM-RoBERTa, processing and interpretation of results (psycholinguistic).

Python Programming for Degree Heating Weeks Estimation using Sea Surface Temperature Data from The Google Earth Engine Dataset as Coral Bleaching Analysis Tools

Abstract This study aims to develop a program to calculate Degree Heating Weeks (DHW) using Python programming and Sea Surface Temperature (SST) data available in Google Earth Engine (GEE) datasets, e.g., HyCom, NOAA OISST v2.1, MODIS-Aqua, and NOAA Pathfinder v5.3. This study contributes to providing a DHW calculator currently unavailable on the Internet to analyze coral bleaching. This program is called PyDHW. The DHW was calculated by inputting the desired location in a polygon, a specific time range, and the SST data. The results show that Python code can extract SST from the GEE dataset according to the user’s input. This SST data is the average of the SST inside the polygon area. This program calculates and shows a graph of the DHW showing coral bleaching alert levels. All these processes were performed quickly in one run. HyCom and NOAA OISST v2.1 have a long and continuous data range. NOAA OISST v2.1 is still updated in the GEE dataset rather than the others. The MODIS-Aqua contains blank time-series data for several measurements. The NOAA Pathfinder v5.3 data shows extreme change in time series data and low temperatures with different patterns from the other SST data. However, this program is still under development and needs improvements. This program is expected to help users concerned with coral research and monitoring.

Data analysis of secondary metabolites in pond apple (Annona Glabra L.) for potential FtsZ inhibitors in silico

Background: The pond apple (Annona glabra L.) is a specie widely distributed in our country with many activities such as anticancer, anti-HIV, antibacterial, antimalarial, antidiarrheal, antiparasitic, etc. The in vitro screening process showed that the methanol extract of Annona glabra L. inhibited the activity of FtsZ GTPase prominently. This study aims to investigate the ability to inhibit FtsZ GTPase in silico of secondary metabolites in pond apple. Materials and Methods: Annona glabra metabolites were collected from articles in Pubmed, Researchgate, etc. The 6YD5 crystal structure in the PDB protein database was selected to build the docking model. The docking process was performed on LeadIT software. Data was processed and visualized with the Python programming language. Results: 192 structures in pond apple were collected from 1991 to 2015. The docking model was built to meet the requirements. 37 compounds successfully docked from 140 non-overlapping structures. (-)-Roemeroline is an aporphine alkaloid with the lowest docking score of -19.7 kJ/mol. The potential structures are mainly distributed in leaves, stems, and fruits. Conclusion: The chloroform fraction of pond apple contains abundant alkaloids, amides, polyphenols which is the potential fraction to inhibit FtsZ. Multicyclic alkaloids such as aporphine, azaanthraquinone, protoberberine provide initial active compounds for the search for new targeted antibacterial drugs. Key words: Annona glabra, FtsZ, molecular docking, antibacterial, secondary metabolites

Data-driven View of Customer Psychological Anxiety Attributes for Cooperate Social Responsibility in Supply Chain Management: a LDA Model

Background-Cooperate Social Responsibility (CSR) in supply chain management requires understanding customer psychological anxiety attributes. A data-driven approach, such as using a Latent Dirichlet Allocation (LDA) model, can provide insights. By recognizing and addressing customer psychological anxiety, CSR can provide better support, which leads to higher customer satisfaction. When customers feel understood and supported, they are more likely to have a positive perception of the company and its supply chain services. Subjects and methods- The study aims to use the LDA method to explore consumer psychology anxiety and its attributes for CSR in supply chain management. The corpus is collected from the Web of Science core collection with keywords “CSR” and “supply chain management”, and 965 articles related to the field from1990-2022. LDA is a natural language processing technique that uncovers thematic structures in textual data. By applying LDA to customer feedback, businesses can identify anxiety attributes. Steps include data collection, preprocessing, LDA model training, topic interpretation, and deriving business insights. Results- The study used the Python program to run LDA, after putting in the text data, the study identify number of 11 topics according to the value of topic coherence, then identified topics based on the most representative words or phrases within each topic combined CSR and supply chain related knowledge and fit the service quality model to find the customer psychological anxiety attributes, the results reveal that customer tend to be anxiety about the aspects of reliability and assurance for CSR in supply chain management, more concern about the environmental and social aspects responsibility. Conclusions- The study revealed customer psychological anxiety about CSR initiatives and strategies within their supply chain management. The results show that consumers feel most anxiety about the aspects of reliability and assurance of supply chain management. They are concerned about the environment and social responsibility that the supply chain enterprise is taking, especially in the food supply chain field. By addressing these concerns, organizations can enhance customer satisfaction, build stronger relationships, and improve their overall CSR performance. The findings of this study also contribute to the field by providing valuable guidance, further research can add other text mining methods like structural topic modelling, or add deeper quantitative research in the field and develop of a new customer-CSR service quality model in the service industry.

Application of Machine Learning in Clustering Maize Producing Regions in Indonesia

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.