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Teaching Monte Carlo Simulation with python

JOURNAL OF STATISTICS AND DATA SCIENCE EDUCATION 2023年第1期31卷 33-44页

作者： Holman, Justin O. Hacherl, Allie Colorado State Univ Hasan Sch Business 2200 Bonforte Blvd Pueblo CO 81001 USA Judson Univ Elgin IL USA

It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly applicable statistical computing methods. This article describes efforts to teach Monte Carlo simulation using python. A series of simulation assignments are completed first in Google Sheets, as described in a previous article. Then, the same simulation assignments are completed in python, as detailed in this article. This pedagogical strategy appears to support student learning for those who are unfamiliar with statistical computing but familiar with the use of spreadsheets. for this article are available online.

关键词： Statistical Computing python programming Spreadsheets

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A New Image Processing System to Diagnose the Orange Fruit Disease 10

A New Image Processing System to Diagnose the Orange Fruit D...

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10th International Conference on Internet of Things - Systems, Management and Security (IOTSMS)

作者： Foroughi, Arman Jimenez, Jose Miguel Lloret, Jaime Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras Gandia Spain Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras Valencia Gandia Spain

ISBN: (纸本)9798350393897

Citrus fruits are among the most important food components. Orange is one of the most important citrus fruits because of its vitamin C content and delicious taste, and it is planted and harvested in most countries. Aphids, pests, and fungi cause the destruction of orange fruits. Therefore, it is very important to quickly identify diseases. Several factors are important to improve the ripening of the orange fruit, so that it does not suffer from viruses or fungi. First, several important viruses and fungi and their causes in oranges should be investigated, and ways to improve orange ripening should be made easy;here, we used an image processing system and Red, Green, Blue (RGB) to Hue, Saturation, Lightness (HSL) conversion to identify several important viruses and fungi from the color of orange peel. In addition, the proposed algorithm detects both healthy and diseased oranges. The accuracy of the present study was 93.6%. In this study, an algorithm for diagnosing orange-fruit diseases was proposed. This algorithm is performed using python programming and executed by the Raspberry Pi. Therefore, oranges with Melanosis, Canker, and Black Spot disease were detected. The defective part of the orange fruit is shown in the form of mask and HSL images as well as the integration of the mask and HSL images in the output. After running the program, the severity of the disease is shown through the number of pixels of the defective part of the orange, and guidance for the prevention and treatment of the disease is described.

关键词： Image Processing python programming HSL Raspberry Pi Citrus Diseases

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Attendance System based on Facial Recognition using OpenCV 2

Attendance System based on Facial Recognition using OpenCV

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Rajesh, Thota Radha Kuchipudi, Lakshmi Narayana Dammalapati, Akhila Surendran, R. Vignan Fdn Sci Technol & Res Guntur Deparmentt CSE Guntur Andhra Pradesh India Vignan Fdn Sci Technol & Res CSE Dept Guntur Andhra Pradesh India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Face recognition is one of the most prevalent applications of image processing, playing a critical role in various industries, particularly in authentication tasks such as school attendance. The "face recognition" attendance system enables high-definition monitoring and advanced computer technologies to identify students based on their facial biostatistics. This system aims to digitize the traditional and labor-intensive method of recording attendance by calling names and maintaining paper records, which are prone to manipulation and proxies. Current biometric and conventional methods also suffer from similar vulnerabilities. This research work proposes a robust solution utilizing the python OpenCV library to automate the attendance process. The system generates and saves attendance reports in Excel format post face recognition, and it has been tested in diverse conditions, including varying lighting, head movements, and changes in student-camera distance. The proposed approach demonstrates high accuracy and efficiency, significantly reducing manual effort and time commitment. Furthermore, the system is cost-effective and requires minimal installation effort, presenting a reliable and efficient tool for classroom attendance management.

关键词： Face Recognition Attendance Management OpenCV python programming Computer Vision Student Tracking

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University of Cincinnati and Saint Ursula Academy Partnership: Introducing Female High School Students to the Field of Information Technology, Year 2 16

University of Cincinnati and Saint Ursula Academy Partnershi...

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17th Annual Conference on Information Technology Education (SIGITE) and 5th Annual Conference on Research in Information Technology (RIIT)

作者： Fritz, Jen Matthews, Margie Wulf, Tom Scott, Jim Fritz, Jamie Univ Cincinnati CECH Sch IT Cincinnati OH 45221 USA St Ursula Acad 1339 East McMillan St Cincinnati OH 45206 USA Univ Cincinnati Cincinnati OH 45221 USA

ISBN: (纸本)9781450344524

For the past two years, the School of Information Technology (SoIT), College of Education, Criminal Justice, and Human Services (CECH) at the University of Cincinnati (UC) has worked with the women's high school St. Ursula Academy (SUA) to provide Information Technology class sessions to SUA's female students. Our aim is to introduce young women into the IT field and give them positive experiences to help bolster this underrepresented group within the future IT workforce. Analyzing the young women's registration for our courses and future IT college courses, our impact influenced at least 29 students to register for a UC IT course next year. Further, surveys revealed that perceptions and confidence levels were significantly improved after attending the SUA IT courses. The next project will expand to other underrepresented populations within the IT workforce.

关键词： Information Technology JavaScript programming python programming Linux Operating System PC Hardware Raspberry Pi Projects Personal Web Site Development

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Generating Pell Numbers

Generating Pell Numbers

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Gao, Weizheng Allagan, Julian Gao, Shanzhen Su, Jianning Malomo, Olumide Eyob, Ephrem Adekoya, Adcyemi Elizabeth City State Univ Dept Math Comp Sci & Engn Technol Elizabeth City NC 27909 USA Virginia State Univ Dept Comp Informat Syst Reginald F Lewis Coll Business Blacksburg VA USA Georgia State Univ Perimeter Coll Atlanta GA USA

ISBN: (纸本)9798350361513;9798350372304

There are around 857 items on Pell numbers in the On-Line Encyclopedia of Integer Sequences. Mathematicians and Computer Scientists love Pell numbers because they seem to be very easy, but they are related to many challenging topics. We will discuss some properties, algorithms, and python programs used to generate Pell numbers, conjectures, and applications of Pell numbers.

关键词： Pell number Pell sequence python programming generating function recursive relation iterative algorithm matrix

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Real-Time Surface Damage Analysis of Concrete Structures using python-based Image Analysis 17

Real-Time Surface Damage Analysis of Concrete Structures usi...

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Conference on Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

作者： Jha, Shivam Kaur, Amardeep Southern Illinois Univ Edwardsville Edwardsville IL 62026 USA

ISBN: (纸本)9781510660816;9781510660823

Aging infrastructure worldwide has led to an interest in using innovative solutions for non-destructive damage assessment. The traditional in-person assessment of large-scale infrastructure like dams and bridges etc. can be very time and resource-consuming. With advances in technology, image processing and machine learning have shown promise in providing alternate ways of damage assessment in large-scale and difficult-to-access infrastructure. However, this approach has mostly been applied passively and not in real-time. The work presented here describes a supervised machine learning-based approach to damage analysis of concrete structures. python programming language is used to write and train algorithms to provide a real-time damage analysis. In addition to crack detection, the dimensional analysis provides additional information regarding an existing or developing crack. With this type of real-time information, timely action can be taken regarding decisions like performing repairs or decommissioning a structure for public safety.

关键词： Image Analysis Concrete Crack Detection Structural Health Monitoring Machine Learning python programming

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Parsl+CWL: Towards Combining the python and CWL Ecosystems

Parsl+CWL: Towards Combining the Python and CWL Ecosystems

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Karle, Nishchay Clifford, Ben Babuji, Yadu Chard, Ryan Katz, Daniel S. Chard, Kyle University of Chicago Department of Computer Science Chicago United States Argonne National Laboratory Lemont United States University of Illinois Urbana-Champaign NCSA & CS & iSchool UrbanaIL United States

ISBN: (纸本)9798350355543

The Common Workflow Language (CWL) is a widely adopted language for defining and sharing computational workflows. It is designed to be independent of the execution engine on which workflows are executed. In this paper, we describe our experiences integrating CWL with Parsl, a python-based parallel programming library designed to manage execution of workflows across diverse computing environments. We propose a new method that converts CWL CommandLineTool definitions into Parsl apps, enabling Parsl scripts to easily import and use tools represented in CWL. We describe a Parsl runner that is capable of executing a CWL CommandLineTool directly. We also describe a proof-of-concept extension to support inline python in a CWL workflow definition, enabling seamless use in Parsl's python ecosystem. We demonstrate the benefits of this integration by presenting example CWL CommandLineTool definitions that show how they can be used in Parsl, and comparing performance of executing an image processing workflow using the Parsl integration and other CWL runners. © 2024 IEEE.

关键词： commandlinetool common workflow language common workflow language (cwl) computational reproducibility cross-platform execution cwl dataflow programming dynamic workflows high performance computing (hpc) inline expressions parallel computing parsl python programming resource management scalability scientific workflows workflow workflow automation workflow ecosystem workflow execution workflow integration workflow management systems workflow portability

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Algorithm for Calculation of the Measured Single Fly-over Aircraft Noise 24

Algorithm for Calculation of the Measured Single Fly-over Ai...

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24th International Congress on Acoustics, ICA 2022

作者： Boonchoo, Thapana Lertsawat, Krittika Faculty of Science and Technology Thammasat University Thailand ENLAWTHAI Foundation Thailand

The noise measurement data of the single fly-over aircraft noise levels are fundamental noise events information to understand the existing situation of noise from aircraft operations under the major flight path near the airport. The standardized noise measurement method is described under the ISO 20906 as a background study of Thai version guidelines of the aircraft noise measurement in community. Under the major flight path, the noise situation at the receiver sites in communities may need to be collected to contribute those noise data to the social survey and noise-dose relationship study, and/or the noise impact assessment study, and other purposes. In this paper, we will provide the post-processing algorithm which employs two-step clustering approach to calculate the single events of fly-over aircraft noise in sound exposure levels from the measured time series of the 100 milliseconds A-weighted sound pressure levels by sound level meter complied with ISO/IEC 61672 class 1. It will be useful for post-assessing the cumulative aircraft noise events in other sound descriptors as stated in ISO 1996 for environmental noise purposes. © ICA *** rights reserved

关键词： aircraft noise events post-processing algorithm python programming

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Exploring the Effectiveness of Various Machine Learning Models in Predicting Heart Failure: A Comparative Study 2

Exploring the Effectiveness of Various Machine Learning Mode...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rathi, Snehal Das, Aakash Gupta, Aashi Bagrecha, Janhvi Mahajan, Umakant Patankar, Kunal Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (纸本)9798350327533

The major cause of human mortality worldwide is cardiovascular disease, and lowering the mortality rate depends on early detection and intervention. By examining huge datasets of patient data to find similarities, machine learning algorithms can be a key player in the prediction of cardiovascular disease. This study compares the effectiveness of popular machine learning algorithms. The algorithms were examined, and accuracy, precision, recall, and overall performance were measured. The study's findings demonstrated that all the algorithms performed well in terms of foretelling heart disease, with accuracies above 85%. The KNN (K Nearest Neighbour) machine algorithm, with accuracy, precision, and recall values of 97.6%, 98%, and 98%, respectively, achieved the best overall performance. The effect of Min Max normalization on the effectiveness of the algorithms was also examined in the study. The data is scaled using the min-max normalization method to a range from 0 to 1. The study's findings demonstrated that the normalization caused a slight decline in overall performance. The results of this study could be used to create machine learning models for predicting heart disease that are more precise and effective. As a result, mortality may be decreased through improved heart disease early detection and treatment. © 2024 IEEE.

关键词： Decision Tree Heart Disease K Nearest Neighbors Machine Learning Prediction python programming Random Forest Supervised Machine Learning Support Vector Machine Unsupervised Machine Learning

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Optimizing the number of vehicles for a public bus line on the grounds of computer simulations 5

Optimizing the number of vehicles for a public bus line on t...

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5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Naumov, Vitalii Cracow Univ Technol Dept Transportat Syst Krakow Poland

ISBN: (纸本)9781509064847

The paper presents an approach to estimate the optimal number of vehicles on public transport line;this approach is based on a simulation model of the process of servicing the bus line passengers. A problem of determining such a number of buses, that provides the minimum cost of transport at the maximum level of passenger service, is quite a complex issue due to the stochastic nature of the transport process and the random nature of the demand for trips of urban residents. An efficiency of a public bus line is proposed to be considered on the basis of the total operation costs of a bus line in terms of the interests of particular elements of the transport system passengers as customers of public transport and public transport companies as providers of services. The author presents a class library implemented with the use of the python programming language;on the basis of this library, the model simulating the process of the public transport line operation was developed. The results of the simulation experiment, based on the developed software, allowed the author to define the functional dependence between the total waiting time for bus passengers at bus stops and the characteristics of the public transport line. Using the obtained dependence, a formula for estimating the optimal number of vehicles on a public transport line was established as an argument, in which the function of the transport subsystem total cost reaches its extreme minimum.

关键词： number of vehicles public transport line python programming computer simulations

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