Python Language Research Articles

Machine Learning Modelling for Imbalanced Dataset: Case Study of Adolescent Obesity in Malaysia

Obesity among adolescent is a public health issue with increasing burden of disease. Predicting imbalanced health data with Machine Learning may introduce bias and lead to diminished model performance. Misclassification in healthcare data could lead to misdiagnosing a patient or failing to detect a health issue when it is present. The purpose of this study is to predict adolescent obesity using machine learning along with implementation of multiple approaches on the imbalanced dataset. This study used secondary dataset from National Health and Morbidity Survey 2017. Samples 13 – 17 years were selected for the classification. SPSS V26 was used for data pre-processing, data cleaning, and data analysis. Meanwhile, Python language used for prediction and evaluation of the models. Approaches on the imbalanced dataset including resampling method (Random Oversampling, Random Under-sampling) and hybrid method (SMOTE and ADASYN) were implemented. This dataset was used for the formation of predictive models on ML algorithm including Artificial Neural Network, Decision Tree, K-Nearest Neighbour, Logistic Regression, Naïve Bayes, Random Forest and Support Vector Machine. The performance of each model was evaluated and compared using accuracy, precision, recall, F- score and Area under the Curve (AUC). Random Oversampling approached with Decision Tree Algorithm performs the best with accuracy (91.35%), precision (0.93), recall (0.91), F- score (0.91) and AUC (0.91) for the prediction of obesity among adolescent in Malaysia. The presented ML model development workflow along with the imbalanced techniques can be adapted to other health survey-based studies and may be valuable for developing other clinical prediction models.

Sterowanie autonomicznym bezzałogowym statkiem powietrznym z wykorzystaniem uczenia przez wzmacnianie

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatable avoid trees and fly through the forest.

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Background Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life. Objective The objective of this study was to explore an mHealth (mobile health) solution by investigating the feasibility of smartphones for processing large EEG recordings for the remote monitoring of patients with epilepsy. Methods We developed a mobile app to automatically analyze and classify epileptic seizures using EEG. We used the cross-database model developed in our previous study, incorporating successive decomposition index and matrix determinant as features, adaptive median feature baseline correction for overcoming interdatabase feature variation, and postprocessing-based support vector machine for classification using 5 different EEG databases. The Sezect (Seizure Detect) Android app was built using the Chaquopy software development kit, which uses the Python language in Android Studio. Various durations of EEG signals were tested on different smartphones to check the feasibility of the Sezect app. Results We observed a sensitivity of 93.5%, a specificity of 97.5%, and a false detection rate of 1.5 per hour for EEG recordings using the Sezect app. The various mobile phones did not differ substantially in processing time, which indicates a range of phone models can be used for implementation. The computational time required to process real-time EEG data via smartphones and the classification results suggests that our mHealth app could be a valuable asset for monitoring patients with epilepsy. Conclusions Smartphones have multipurpose use in health care, offering tools that can improve the quality of patients’ lives.

A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System

Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.

Methods for Solving Differential Equations in Python Language

This article provides an overview of methods for solving differential equations using the Python programming language. The article discusses in detail numerical methods such as the Euler method, the Runge-Kutta method and the finite difference method and provides examples of their application using the SciPy library. In addition to this, the article discusses the basic steps in solving differential equations, including determining the equation, boundary conditions, and initial conditions. Code examples are discussed in detail to facilitate understanding and implementation of methods in your own projects. This material is intended for developers and researchers who want to effectively use the Python language to solve differential equations in various fields of science and technology.

Identification of Vivo Material Parameters of Arterial Wall Based on Improved Niching Genetic Algorithm and Neural Networks

Cardiovascular diseases are seriously threatening human health and the incidence rate is high. Many scholars are devoted to studying arterial mechanical properties and material parameters. In this study, the bovine artery was selected as the experimental object and the uniaxial tensile test was carried out by cutting the specimens along its axial, circumferential and [Formula: see text] directions. The finite element software ABAQUS and hyperelastic Holzapfel Gasser Ogden (HGO) constitutive model were used to simulate the experimental process. Niche technology is introduced on the basis of genetic algorithm, and the program of Improved Niche Genetic Algorithm for material parameter identification is compiled based on Python language. In addition, BP Neural Network was constructed based on Tensorflow mathematical system. The material parameters of the constitutive model of bovine artery in different directions were identified by finite element method and experimental data. The results show that Improved Niche Genetic Algorithm and Neural Network, respectively, combined with finite element are both effective and accurate methods for predicting the parameters of arterial vascular hyperelastic materials, which can provide reference and help for the study of arterial vascular mechanical properties.

Suggestions for the Use of Big Data for a Manufacturing Company

This paper discusses the stages and methods of working with big data. It describes the tasks and problems in the field of logging and wood products production, solved by analyzing processed data. The article presents an algorithm for working with big data and its realization in the form of a program complex developed in the Python language. The main benefits derived from the visualization of processed information are listed.

A real-world data analysis-based study of Chinese medicine treatment patterns after breast cancer surgery.

Based on the real clinical data of Hospital Information System to explore the common clinical syndromes of traditional Chinese medicine after breast cancer surgery, analysis of traditional Chinese medicine in the treatment of breast cancer after the compatibility law. The real medical records of breast cancer patients after surgery in a tertiary hospital in Sichuan Province were collected and screened to build a medical record database. Python language was used for data preprocessing to remove outliers and fill in missing values. Using International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Modeler software, Apriori association rules algorithm for data analysis, mining Chinese medicine treatment of breast cancer after common syndromes and the corresponding medication rules. A total of 472 cases of clinical real medical record data were included. Data analysis showed that there were 42 TCM syndromes after breast cancer surgery, of which the highest frequency was liver depression and spleen deficiency, qi deficiency and blood stasis, qi stagnation and blood stasis, qi and blood deficiency, qi and yin deficiency, phlegm and blood stasis. A total of 416 kinds of traditional Chinese medicine were involved. High-frequency drugs included angelica sinensis, coix seed, bupleurum, ginger magnolia bark, keel, oyster, astragalus, platycodon grandiflorum, antler frost, vinegar tortoise shell, poria cocos, lily, Jianqu, Ophiopogon japonicus (Maidong), Shancigu, etc. A total of 18 pairs of commonly used drug combinations were excavated, such as Fushen-Gancao-Chaihu-Angelica, Huangqi-Baishao-Jianghoupu, Chaihu-Huanhua-Maidong-Lily, Baizhu-Huangqi-Maidong, Fuling-Baishao, etc. The clinical syndrome type of traditional Chinese medicine after breast cancer surgery is mainly liver depression and spleen deficiency syndrome. The clinical treatment is mainly soothing liver and relieving depression, and harmonizing liver and spleen. Analyze the syndrome type and the corresponding drug compatibility law, and provide decision support for the clinical dialectical prescription of traditional Chinese medicine after breast cancer surgery.

PyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

BackgroundVariability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data.ResultsIn this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose.ConclusionsWe present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost.

Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.

Analysis and Visualization of Road Accidents Using Heatmaps Based on Web Data

Abstract Road accidents have increased rapidly in recent years for a variety of reasons. Analyzing and visualizing road accidents through heatmaps can help improve policies for their prevention by informing about areas with a high-risk of road accidents. The purpose of this research is to build a model for the analysis and visualization of road accidents through heatmaps. Information about road accidents is extracted from the news of the main online media portals through scripts in the Python language and Web Scraping techniques. From the extraction of about 30,000 articles from news portals for one year, only 829 were selected in the end that provided information about road accidents. As a result, and contribution of this research, a corpus was built with the geographic coordinates of road accidents and on this data our model was applied for the analysis and visualization of high-risk areas of road accidents using heatmaps. The visualization of heatmaps was done through a Python script, where it was applied to the geographic coordinates of road accidents.

Comparative analysis of deterministic and reliability-based structural optimization methods

Optimization is the act of obtaining the best possible result under established conditions. Usually, the optimization of a structural design is done considering the structure's dimensions, the materials' properties, and the loads as deterministic values. This way, the optimization process can lead to a more economical design without guaranteeing that this structure is safe. In practice, there are always uncertainties about the final dimensions of the built structure, material properties, and loads. Then, the need arises to use design optimization techniques based on reliability to guarantee a project that is both economical and safe. This objective is achieved by including uncertainties in the optimization process. This article evaluates the parameters that determine the global minimum of the optimization methods DDO (Deterministic Design Optimization) and RBDO (Reliability-Based Design Optimization). This work aims to compare the structural optimization methods of DDO and RBDO through an example. The results are obtained through the codes of the methods implemented in the Python language and show that when comparing the two optimization methods, the presence of uncertainties alters the optimal solution.

Smart portable system for monitoring vibration based on the Raspberry Pi microcomputer and the MEMS accelerometer

In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.

Design and Implementation of Smart Home System Based on STM32 Microcomputer

Abstract With the rapid development of the Internet of Things science and technology, people′s living standards are gradually improving, and the requirements for the living environment are also getting higher and higher, which makes smart homes gradually enter thousands of households. The purpose of this project is to design a system that integrates hardware and software and can measure and transmit various data. Among them, the hardware part includes data measurement and data display. The data measurement module consists of DHT11 temperature and humidity sensor, DSM501 particle number sensor and MQ3 alcohol concentration sensor. The experimental data will be displayed on the TFTLCD screen. The system software is partly run on the Windows operating system, using the Python language development. This system takes ESP8266 module as the transfer station, realizes the communication between STM32 development board and computer. The experiment shows that the system has the advantages of high measurement data accuracy, fast data refresh speed, complete data transmission, simple design, high reliability, easy installation, economical and practical, and has certain practical value in life, production, industry and other fields.

"Algorithm Analysis and Design" Python Teaching Example of Greedy and Dynamic Programming

The greedy algorithm and dynamic programming algorithm have always been difficult for students to understand in the course of algorithm analysis and design. This article uses Python as a descriptive language and selects classic examples of greedy and dynamic programming algorithms to analyze these two algorithms in detail, providing effective references for learning the Python language.

Software Support for Arbitrary Precision Arithmetic in Programming Languages

Arbitrary precision arithmetic, also known as bignum arithmetic, is a computational technique that allows programmers to perform arithmetic operations on numbers with significantly higher precision and magnitude than what is typically supported by the built-in numerical data types in programming languages. This technique is especially useful when working with extremely large or extremely precise numbers, such as in cryptography or scientific computations. Arbitrary precision arithmetic has many applications in the areas of Cryptography, Numerical Computation, Statistical Analysis, and High Precision measurements. For example, the calculation of the modulus in the RSA algorithm involves numbers with 1024-bit numbers and higher. An arithmetic calculation involving Multiplication and exponentiation of such numbers using Modulo arithmetic cannot be easily carried out in the existing programming Languages unless special software is provided. We can calculate the mathematical constant Pi to many thousand decimal places using the support provided in Programming Languages. Many programming languages provide built-in support for libraries for arbitrary precision arithmetic. We discuss the support provided in C/C++, Java and Python Languages with examples. Besides Programming Languages, Toolkits like Matlab and Sagemath also are used for scientific computation and special software support provided in these toolkits can enable arbitrary precision arithmetic. Most Programming Languages have support for floating-point arithmetic. We also discuss how arbitrary precision floating point arithmetic. can be supported in C/C++, Java, and Python. In addition, we discuss support for arbitrary precision integer arithmetic in Ruby, Javascript and Matlab and support for arbitrary precision floating point arithmetic in the Perl Language. Finally, we provide an example of computing the constant Pi to many decimal places using the sagemath tool.

Home Automation Using Smart AI Assistant

Artificial intelligence is taking over all the platforms and applications and making them more and more user friendly and intelligent and gives the application a human like behaviour and thinking. AI is being used everywhere now-a-days because applications that use AI are able to learn and improve and require less maintenance. This paper “Home automation using smart AI assistant” is a voice command-based AI assistant coded using python and Natural Language Processing NLP that can respond to user queries and responds to voice-based commands of the user. Using this AI Assistant users can send emails to anyone just by speaking the receiver’s name, subject of email and the email body. The users can send WhatsApp messages to any of their contacts just speak the message and to whom you want to send it, users can do google searches and open google tabs by voice search, users can do Wikipedia searches and can also open any YouTube video they want to watch. With this AI Assistant we can also get weather updates and news updates, just speak what news updates you want and the Assistant will read it for you. We can also check the computer performance, add remainders and to-dos. We can open our documents, play our favourite music or watch our favourite movie and do many more thigs just by a simple voice command. This assistant is also used for voice-controlled home automation. Using this Assistant, we can control our home electrical appliances with simple voice commands like turn on the room lights, fans, AC etc.

신입생 ICT 리터러시 역량진단을 통한 소프트웨어 기초교육 방안 모색

This study was conducted to analyze the results of ICT literacy diagnosis for college freshmen and derive implications for college liberal arts software curriculum based on this. Data collection targeted 3,488 new students at M University in Daejeon in 2021, 2022, and 2023, and compared grades based on ICT-related prior learning, analyzed diff erences in grades by major, and analyzed differences in ICT literacy capabilities. As a result of the study, 940 (27.05%) out of 3,488 respondents over the past three years responded that they had experience in ICT-related programs. The average score of the ICT literacy competency diagnosis of students in the college of engineering, college of education, and college of art and design, who had a high proportion of ICT-related learning experience, was high. While the average score of students at the college of music and college of technoscience, who had a low proportion of ICT-related learning experience, was low. In other words, ICT-related prior learning experience showed a high correlation with ICT literacy competency scores. As a result of comparing the percentage of correct answers by area, the percentage of correct answers in the access and ethics areas was higher than that of other areas, but the percentage of correct answers in the management and sharing areas was somewhat lower. These results are believed to be due to the fact that as the computing environment is moving from PC to mobile, data storage, software use, and development environments are changing to focus on cloud computing. In addition, implications derived from the research results include the possibility of using block coding language in basic software education, the need for basic and advanced curriculum reflecting the increase of social interest and demand for the Python language, and resource management methods according to changes in the computer use environment.

Fully Automated Paper Document Sorting Robot Design

A fully automated paper document sorting robot was developed in this project. This robot classifies documents efficiently and accurately. The objective of this project was to improve the efficiency of classifying or sorting paper documents, reduce costs, and save time. The robot can classify documents according to user-defined rules, such as keywords, dates, serial numbers, bar codes, and the meaning of paragraphs. Since it can classify or sort documents intelligently, it can complete large-scale document classification quickly. The robot is constructed using an aluminum profile to create a box-type truss gantry structure frame. It was built on the LubanCat 4 motherboard and controlled through Python language programming. Driven by a stepper motor to move the manipulator. The camera module is combined with an artificial intelligence algorithm to recognize paper in real time, and the text is recognized after taking pictures of the paper. The sorting function is performed by several sensors. In addition, a web-based human-computer interaction platform was developed using the Flask web framework in Python. Users could access this platform in a variety of ways, allowing them to easily and swiftly configure parameters and send operational instructions to perform various functions.

CFD Analysis in the Mesh Modified Gas Diffusion Layer of a Proton Exchange Membrane Fuel Cell (PEMFC)

Computational Fluid Dynamics (CFD) software is well known for its application feasibility as well as reliable results in modeling electrochemical, thermal, and fluid transport processes. CFD has been used to investigate the phenomena involved in the operation of fuel cells, providing a large amount of data that must be analyzed to improve cell efficiency. This paper aims to demonstrate that programming can be used in the post-processing phase, using scripts in Python language to automate data analysis, based on the results of the simulation of oxygen transport in Polymer Electrolyte Membrane Fuel Cell (PEMFC). The OpenFOAM open-source CFD tool solved the fluid governing equations through the SIMPLE algorithm of three proposed Gas Diffusion Layer (GDL) case studies. In this work, an algorithm is presented to extract, compute and visualize the post-process results, supporting the GDL selection.