Python Language Research Articles

The Image Classification Algorithm Was Implemented on The MNIST Data Set

In the current era of rapid development of science and technology, the recognition and classification of digital images is the key to solving many problems, such as the application of license plate recognition, document digitization, and remote sensing image surface classification. Based on the MNIST handwritten numerical data set collected by the National Institute of Standards and Technology (NIST), this report uses Python language and PyTorch programming framework to construct a convolutional neural network (CNN) structure and practice and experience the image classification of handwritten digits in the MNIST data set.

KIAM Astrodynamics Toolbox for spacecraft orbital motion design

The KIAM Astrodynamics Toolbox, a new software library for designing spacecraft orbital motion, is introduced. The toolbox is developed at the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences using Fortran and Python languages, thus combining the computational speed and the flexibility of program design. The library can be useful for space flight mechanics specialists, as well as for students in relevant educational programs.

Development of a tool for teaching reactor calculation based on the PYTHON language

The flexibilization of teaching with the use of computational tools, which enable students' cognitive gains, is relatively new in engineering education, however, it is gaining more and more space. The educational resources developed in this work are extremely relevant for learning, since most academics have extreme difficulties in mastering calculations and their interpretation. The reactor calculation discipline has a growing demand for the use of these resources and software is an effective resource for this proposal. In this sense, a programming language was used in PYTHON Software, which has general use, from entertainment to scientific such as Artificial Intelligence - AI. The language developed aimed to size chemical reactors and optimize response time to a problem situation. The source code was structured so that the user can interact in order to facilitate the insertion of the parameters that characterize each reactor. With this, not only the executable program, but also the step-by-step programming was made available to academics, to serve as teaching material to support teaching. As a result, students were satisfied with using the software, with 75% of them stating that using this tool was very intuitive, dynamic and that it was a user-friendly platform.

Comparison of Sentiment Analysis Model for Shopee Comments on Google Play Store

The current COVID-19 pandemic has greatly changed the order of consumption and the Indonesian economy. During the health crisis that hit Indonesia, the e-commerce sector experienced very rapid development because of changes in consumer behavior that are looking for safe and comfortable shopping alternatives. During the COVID-19 pandemic, Shopee became the number 1 online shopping site in Indonesia. However, this cannot be used as a standard for user satisfaction. User satisfaction can only be measured from comments by Shopee application users through the comments and rating features provided by the Google Play Store. Therefore, to be able to find out public opinion about Shopee, a sentiment analysis of the Shopee application will be carried out which can later be used by management to develop even better applications. In this study, the dataset taken is the rating and reviews of Shopee application users on the Google Play Store using the Multinomial Naïve Bayes method, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. This study uses 1000 comment and rating data which are processed using the Python language. The results of this study indicate that the method that has the highest level of accuracy is the Support Vector Machine algorithm with an accuracy of 88%, Extra Trees Classifier with an accuracy of 86%, Logistic Regression with an accuracy of 85%, Random Forest Classifier with an accuracy of 85%, K- Nearest Neighbors with an accuracy of 83%, and the last is Multinomial Naïve Bayes with an accuracy of 78%.

Survival Analysis of Patients with COVID-19 using Deep Neural Network and Random Forrest Techniques

Introduction: The prediction of the survival chance of coronavirus disease 2019 (COVID-19) patients is as important as the early detection of the coronavirus. Since patient mortality, factors may differ by location, this study concentrated on identifying the influential factors and predicting survival for COVID-19 patients using machine learning methods in Fars province, Iran.Material and Methods: The research dataset was extracted in the period January 21, 2020, to September 25, 2020, and contains 25858 hospitalized patients’ records with 51 features. These records were classified into two categories: death (label 1) and survival (label 0). The methodology of this research is CRISP standard. A comparison was made between the efficiency of two deep neural network and random forest algorithms in predicting survival. Modeling steps were done with Python language in the Google Colab environment.Results: Experimental results demonstrated that the deep neural network algorithm had better performance than random forest with accuracy, precision, recall, F-score, and receiver operating characteristic of 97.2%, 100%, 93.54%, 96.66%, and 97.9%, respectively. Based on the results of the random forest model, history of hypertension, chronic neurological disorders, chronic lung diseases, asthma, chronic kidney disease and, heart disease were the most important risk factors related to death.Conclusion: Deployment of our proposed model allows medical professionals to exercise greater caution during the treatment of patients who are most likely to die due to their medical conditions.

Fruits Leaf Disease Detection Using Convolutional Neural Network

Due to the traditional agricultural system, losses of millions of money have been loses every year. Farmers were always ready in agricultural work without risking their lives. If smart methods can be adopted in the agricultural system, the farmers will not have to suffer much damage. Using machine learning and testing with Convolutional Neural Network algorithm (mobileNet method), in this research to find out the actual accuracy, 3642 photos of apple leaves of Kaggle dataset and CSV files are used. In this paper, using Python language with the help of Jupyter notebook, Eposes has been tested 15 times to create confusion metrics. In this paper, precision, recall, f1_ score and average accuracy have been found and studied. An average accuracy of 95 percent has been obtained from the study. 95% accuracy is considered as a good result of the test using machine learning. By adopting this method, we can also give more motivation to the agricultural sector.

A design TCADAS tool for semiconductor devices and case study of 65 nm conventional floating-gate MOS transistor

An automatic programming tool has become an essential component in virtual fabrication in recent years. This paper aims to propose a methodology of virtual fabrication for semiconductor devices and design a tool called Technology Computer-Aided Design Automatic Simulation (TCADAS) which can perform a completely virtual fabrication, device simulation, process variation, and output extraction. Especially, the TCADAS tool eliminates drudgery when studying semiconductor devices such as complexity in setting inputs, substantial manual work, and long run time of simulations. This work investigates the completed flow for a 65 nm conventional Floating-gate MOS transistor as a case study, which is widely considered a vital determinant of the non-volatile memory field. A detailed automatic process from the entry to a completed three-dimension structure, device characterization, extract important output parameters such as memory window, ION/IOFF, Gate Coupling Ratio (GCR), and speed operations are presented. The TCADAS tool was designed by Python language and the utilities of TCAD Silvaco tools including Athena, Atlas, and DevEdit3D.

Predictive model for acute myocardial infarction in working-age population: a machine learning approach

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population.

CLASSIFYING THE SUPERVISED MACHINE LEARNING AND COMPARING THE PERFORMANCES OF THE ALGORITHMS

Supervised Learning (SL), also recognized as SML, means Supervised Machine Learning. Its a subclass of AI (Artificial Intelligence) and Machine Learning (ML). Its defined by the conduct of entitled datasets for training algorithms that predict outcomes precisely or classify data. The input dataset is faded into the supervised Machine Learning model, which synthesizes its weights until the model has been fitted properly, which happens as a segment of the cross-validation process. Supervised learning machine assists organizations in solving different kinds of real-world problems. SML is searching for algorithms that externally outfitted the instances to produce common hypotheses, preparing predictions for future cases.The supervised Machine Learning (SML) classifications are frequently completed tasks by effective intelligent systems. This paper discusses different categories of Supervised Machine Learning classification technology, compares different categories of supervised learning algorithms and identifies the best effective classification algorithm based on some instances, data set and variables or features. This paper discusses eight different types of SML algorithms. Those were envisaging: Artificial Neural Network (ANN), Bayesian Networks, K-nearest Neighbor (KNN), Random Forest, Decision Tree (DT), Linear Regression, Support Vector Machine (SVM), and Logistic Regression.These eight algorithms develop in the python language. Using a sample dataset for every algorithm and justify the algorithm performance. Here, justify the algorithms based on three different outcomes: throughput, response time, and accuracy. The supervised learning method depends on pre-defined parameters. The performance metric has an important role in identifying the ability and capacity of any kind of machine learning algorithm. The outcomes show that Decision Tree is the best prediction performance in this paper and gives the best accuracy, response time and throughput. The next accurate algorithms in SML algorithms are Logistic Regression and SVM after the DT algorithm.

New criteria for metal oxide surge arrester condition monitoring based on leakage current analysis: Considering non‐uniform pollution effect

AbstractThe role of surge arresters is vital in power systems' protection against transient overvoltages, which can arise due to occurrences like lightning strikes or switching operations. Based on the performance of the surge arrester, assessing its condition is an essential requirement for early detection of any damage or deterioration. This article proposes innovative criteria based on the surge arresters’ leakage current analysis for the classification of uniform and non‐uniform pollution levels. The leakage current signal of clean, uniform, and non‐uniform polluted surge arresters has been examined under different contamination levels, humidity, and non‐uniformity degrees. Based on the experimental results analysis, a new criterion has been proposed for assessing the surge arrester condition, which has resulted from the fifth and first‐order harmonics ratio of resistive leakage current. Analysis of the proposed criterion demonstrates that this index has a significant correlation to the pollution level. The proposed criterion ability has been established based on random forest, support vector machine, decision tree algorithm, and multi‐layer perceptron in Python language. The obtained criterion based on experimental data in different conditions has been employed for classifier training, which can be applied in the power network for better condition monitoring of surge arresters ensuring fast and accurate outcomes.

An open source platform addressing structural stability risk assessment in historical centres

The management of stability risk assessment in historical centres in a project that aims to design and develop an open platform where blocks of buildings and large structures may be represented in damage maps before the event occurs, addressing damage forecasts for seismic movements impacting on the structural stability of the CH. The structural stability is calculated using a Matlab code developed at the National Technical University of Athens, translated to an open source programming language (Python)using the software tool GeoPandas to enable operations that would normally require spatial database software (PostGIS). Furthermore, the use of open source technologies like QGIS desktop software will provide open and free access to view and contribute in the future.

The Impact of Approved Information and Communication Technology Indicators for Development Purposes on The Gross Domestic Product of Selected Arab Countries for The Period 2007-2021

Information and communication technology (ICT) has a significant impact on all economic sectors through innovation, higher productivity, and economic growth. This research paper seeks to illustrate how specific ICT indicators have influenced the gross domestic product (GDP) of selected Arab nations (Iraq, Jordan, the United Arab Emirates, Saudi Arabia and Morocco) during the period from 2007 to 2021.. The multiple regression model has been used and implemented by Python language. The obtained results demonstrate that the effect of fixed telephone line subscriptions adversely affects the Gross Domestic Product (GDP) in the prementioned countries , while the international bandwidth of the Internet and the Internet Users Index positively affect the GDP in the countries of Saudi Arabia, Jorden and the United Arab Emirates. On the other hand, Morocco was the opposite.

EVALUATION OF PROVINCES IN TÜRKİYE WITH HEALTH INDICATORS BY DENSITY-BASED SPATIAL CLUSTERING ANALYSIS

This study aims to evaluate the health resource distribution of provinces in Turkey using DBSCAN cluster analysis method. The optimum values of DBSCAN parameters (epsilon and minPts) were tested by simulation and the clustering silhouette value was taken as the basis for selecting the appropriate parameter set. The results of the descriptive statistical analysis of the dataset show a high coefficient of variation, indicating inequalities in the distribution of health resources. By dividing provinces into two clusters, the study reveals the similarity of local dynamics in the inequality of resource distribution. The findings provide important insights for relevant stakeholders to address the disparities between provinces in Turkey. The fact that the study adopts a method other than the hierarchical and k-means clustering methods dominant in the literature and that the codes of the algorithm are shared in Python language broadens the horizons of the relevant researchers and increases the transparency and reproducibility of the study.

SELECTION AND JUSTIFICATION OF THE LOCATION OF CAMERAS OF THE ALLROUND VISION SYSTEM ON AN UNMANNED DUMP TRUCK

The choice of equipment installation location on the sides of an unmanned vehicle, and especially such a dump truck, is a difficult task, which is solved, as a rule, intuitively and by the method of fullscale modeling. The authors of the article have solved the formulated problem, on substantiation of the choice of places of sensorics arrangement on the dump truck, using the developed specialized software application. The Carla simulator was chosen as a tool for carrying out the research as it has wide possibilities of visualization of the surrounding world. At the initial stage of selecting the location of the camera arrangement, the requirements for their viewing angles and the quality of the image obtained from them were formulated. This required the development of a software application in the Python language using the Open3D library, the choice of which is justified by the fact that the Carla simulator provides access to its API through a library for this language, and also because it allows rapid prototyping of applications. The developed software application helps the user to select the location of the video cameras on the dump truck in order to experience the unmanned control system. Further research is aimed at automating the selection of camera locations on board an unmanned dump truck.

Particle Size Analysis of Nanopowders Using Neural Networks and Electron Microscopy

The purpose of the research is to develop an application capable of automatically determining the particle size distribution of nanopowder using neural network technology in order to simplify the process of preparing documentation during its manufacture.Methods. To determine the physical properties of nanopowders during their fabrication, it is necessary to analyze the particle size distribution. A methodology for determining the size distribution of nanopowder particles based on light neural networks is proposed. Images obtained by electron microscopy are used for processing, which allows to speed up the preparation of manufactured powders for sale. The dataset collected for training contains real images of samples of different powders, augmented data and generated images. The Python language, LabVIEW graphical programming environment, YOLO convolutional neural network and various Python language libraries were used in the development.Results. The study resulted in a model trained on the collected dataset that is capable of recognizing particles in images. A software interface was created to work with the model to analyze nanopowder samples.Conclusion. The developed application allows to automatically determine the size of each powder particle on the basis of the obtained images, as well as to build graphs of their size distribution. This greatly simplifies the work of nanopowder producers and facilitates the preparation of the necessary documentation for the produced product.

Study on Fracture Mechanism in Micro Ultrasonic Vibration Cutting Process

Using Python language to import Voronoi diagrams into ABAQUS, a two-dimensional microscopic model of vibration cutting of 45 steel is established, and based on this model, the stress changes of grains and grain boundary units on the cutting path are analyzed. The results show that in ultrasonic vibration cutting, the direction of normal stress and shear stress in the grain fracture unit will reverse, and the energy threshold will be reached during multiple impact processes, resulting in failure and deletion; During the impact process, grain boundaries are more prone to advanced failure under normal stress.

FPGA-based CCD signal acquisition and transmission system design

In order to facilitate the analysis and processing of optical signals, an FPGA-based CCD signal acquisition and data transmission system is designed in this work. The system uses an FPGA as the main control device, the TCD1304DG/AP chip as the optical signal detector, and the CYUSB3KIT-003 development board product by Cypress for data transmission. Verilog and Python languages are employed for modular design and on-board verification. Through the coordination of each module, the system successfully achieves CCD signal data acquisition and transmission.

Simulation of Pitting Corrosion Under Stress Based on Cellular Automata and Finite Element Method

Abstract A new cellular automaton (CA) program was written in python language to simulate the random pitting evolution process, which can not only obtain a variety of different corrosion products but also obtain a variety of common corrosion morphologies on the surface of metal pipes, bridge steel members, etc. In addition, commercial finite element (FE) software abaqus was redeveloped using python scripting language, and the FE mesh with the same size as the cellular mesh was established based on the consistent mesh algorithm, which ensured the efficiency and accuracy of the cyclic iterative algorithm. The stress and strain fields were calculated in real-time by applying the force load, the dissolution probability parameter P was updated in python according to the force-chemical coupling model, and a new corrosion morphology was obtained in python. At the same time, the birth and death element method was applied in abaqus to kill the corrosion elements in this iterative step simultaneously, and the new stress-strain field was recalculated in abaqus. The established consistent grid modeling strategy and cyclic iterative algorithm can significantly improve the solving efficiency of pitting evolution under the coupled action of corrosive medium and load. The results show that the stress concentration caused by pit expansion and the corrosion acceleration effect dominated by plastic deformation will promote each other, leading to the continuous growth of pitted pits. The established modeling strategy and cyclic iterative algorithm can significantly improve the solving efficiency of pitting evolution under the coupled action of corrosive medium and load.

Application of radiomics model based on lumbar computed tomographyin diagnosis of elderly osteoporosis.

A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT)images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.

Modeling of Particle Size Distribution in the Presence of Flocculant

This study presents a mathematical description of the solid fraction aggregation process in the presence of a flocculant and its result. The basis is a population balance equation. The model is realized in Python language. Verification was carried out using red mud from the investigated enterprise; Flomin AL P 99 VHM was used as a flocculant. The mean square deviation for the parameter “mean aggregate diameter” is equal to 19.88 μm. The time required for the model calculation is about 3 min. The time spent on modeling depends on the number of calculation channels. In this study, 40 channels (20 with PSD source data, and 20 with empty values required for the calculation) were used for the calculation. The time spent on the model calculation is much shorter than the inertia via each of the communication channels for the studied symmetric radial type thickener. A user interface is developed, where the input parameters are the initial pulp particle size distribution, viscosity and density of pulp in the thickener, particle surface area, concentration and flow rate of flocculant, concentration of solid particles, inner diameter and height of the feed well, and simulation time. The result of the simulation is particle size distribution in the feed well of the washer and the mean flocculus diameter.